EP2642918A2 - Systèmes et procédés de voxelation automatisée de régions d'intérêt pour spectroscopie par résonance magnétique - Google Patents

Systèmes et procédés de voxelation automatisée de régions d'intérêt pour spectroscopie par résonance magnétique

Info

Publication number
EP2642918A2
EP2642918A2 EP11842542.0A EP11842542A EP2642918A2 EP 2642918 A2 EP2642918 A2 EP 2642918A2 EP 11842542 A EP11842542 A EP 11842542A EP 2642918 A2 EP2642918 A2 EP 2642918A2
Authority
EP
European Patent Office
Prior art keywords
roi
electronic image
image
pixels
region
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP11842542.0A
Other languages
German (de)
English (en)
Other versions
EP2642918A4 (fr
Inventor
John P. Claude
James C. Peacock
Paul H. Kane
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Aclarion Inc
Original Assignee
Nocimed LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nocimed LLC filed Critical Nocimed LLC
Publication of EP2642918A2 publication Critical patent/EP2642918A2/fr
Publication of EP2642918A4 publication Critical patent/EP2642918A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/055—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves  involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/483—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy
    • G01R33/485—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy based on chemical shift information [CSI] or spectroscopic imaging, e.g. to acquire the spatial distributions of metabolites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/543—Control of the operation of the MR system, e.g. setting of acquisition parameters prior to or during MR data acquisition, dynamic shimming, use of one or more scout images for scan plane prescription
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone
    • G06T2207/30012—Spine; Backbone

Definitions

  • the present disclosure relates to clinical magnetic resonance spectroscopy (MRS), for example, to single voxel MRS, and to the automated prescription of voxels within regions of interest where MRS is to be performed, such as for example intervertebral discs.
  • MRS magnetic resonance spectroscopy
  • Various embodiments disclosed in the present disclosure relate to systems and methods for locating, analyzing, or otherwise obtaining information relating to a region of interest.
  • Various embodiments relate to automated voxelation of regions of interest for single voxel magnetic resonance spectroscopy.
  • Various features are described below and can be used in various combinations with each other. Many combinations of the features described below will be apparent which are not specifically discussed and are intended to form a part of this disclosure.
  • One aspect of the present disclosure includes one or more computer readable media comprising computer instructions configured to cause one or more computer processors to perform actions comprising:
  • the electronic image comprises a magnetic resonance imaging (MRI) image.
  • MRI magnetic resonance imaging
  • selecting image coordinates comprises selecting a two dimensional shape that covers a selected area of the region of interest when converted to world coordinates.
  • the two dimensional shape is rectilinear.
  • selecting image coordinates comprises calculating a two dimensional model that approximates the region of interest based on the population of pixels.
  • calculating the two dimensional model comprises applying an expectation maximization algorithm for estimating parameters of one or more Gaussian distributions for of the population of pixels.
  • the computer instructions are configured to cause the one or more computer processors to position a two dimensional shape based on the two dimensional model to cover a selected area of the region of interest.
  • the computer instructions are further configured to cause the one or more computer processors to output information relating to the region of interest based on the world coordinates, the information comprising one or more of a location, an orientation, a shape, an area, and a volume of the region of interest.
  • the computer instructions are configured to cause the one or more computer processors to perform additional actions comprising:
  • processing the one or more additional electronic images to emphasize pixels that are associated with the at least one structure useful for identifying the region of interest in the one or more additional electronic images;
  • identifying one or more additional populations of pixels in the corresponding one or more additional electronic images the one or more additional populations of pixels being associated with the at least one structure; selecting one or more additional image coordinates from the one or more additional electronic images, the one or more additional image coordinates being based on the one or more additional populations of pixels;
  • the electronic images are of slices substantially parallel to, and spaced apart from, each other.
  • the world coordinates define a three dimensional selected volume of the region of interest.
  • the computer readable media is configured to be used with a magnetic resonance spectroscopy (MRS) system in communication with the one or more computer processors, wherein the MRS system is configured to provide an MRS spectrum of chemical constituents within the three dimensional selected volume.
  • MRS magnetic resonance spectroscopy
  • the computer instructions are further configured to cause the one or more computer processors to analyze a post-acquisition scan to determine whether the region of interest moved during an MRS acquisition.
  • the region of interest is a nucleus of an intervertebral disc of a spine.
  • the computer instructions are configured to analyze the MRS spectrum and to determine whether the three dimensional selected volume was likely mis-prescribed based on one or more signals.
  • the one or more signals comprise a lipid signal.
  • the computer instructions are configured to provide a single three dimensional scan volume to the MRS system configured for single voxel MRS.
  • the computer instructions are configured to cause the one or more computer processors to select multiple three dimensional volumes corresponding to multiple regions of interest and to provide the multiple three dimensional volumes to the MRS system configured for multi voxel MRS.
  • the electronic image is of a first acquisition mode
  • the MRS spectrum is of a second acquisition mode different than the first acquisition mode
  • the three dimensional selected volume is a rectilinear volume.
  • the computer instructions are configured to cause the one or more computer processors to define a two dimensional shape using the initial electronic image, the two dimensional shape corresponding to a cross sectional shape of the three dimensional selected volume, and to modify the cross sectional shape of the three dimensional selected volume to fit the region of interest corresponding to at least one of the one or more additional electronic images.
  • the computer instructions are configured to cause the one or more computer processors to define a plurality of two dimensional shapes associated with the region of interest for the corresponding electronic images, and wherein the three dimensional selected volume has a cross sectional shape corresponding to the overlapping area of the plurality of two dimensional shapes.
  • the computer readable media can be configured to be used with a patient therapy system configured to provide a therapy procedure to a patient based at least in part on the three dimensional selected volume.
  • the patient therapy system is a radiation therapy system or an ultrasound therapy system configured to direct energy to the three dimensional selected volume.
  • the computer instructions are configured to cause the one or more computer processors to calculate a three dimensional model that approximates the region of interest based on the populations of pixels from the electronic images.
  • the electronic images are magnetic resonance imaging (MRI) images and the computer instructions are configured to cause the one or more computer processors to receive the MRI images from an MRI system in communication with the one or more computer processors.
  • MRI magnetic resonance imaging
  • processing the electronic image comprises smoothing the electronic image.
  • smoothing the electronic image comprises modifying a brightness value for a pixel based on the brightness of neighboring pixels.
  • the neighboring pixels comprise one or more pixels from one or more additional neighboring electronic images.
  • processing the electronic image comprises performing at least one top-hat filtering operation.
  • processing the electronic image comprises performing at least one morphological image processing operation.
  • processing the electronic image comprises performing a first top-hat filtering operation on an upper portion of the spine and performing a second top-hat filtering operation on a lower curved portion of the spine.
  • processing the electronic image comprises performing an order statistic filtering operation.
  • the at least one structure comprises the region of interest, the computer instructions being configured to cause the one or more computer processors to process the electronic image to emphasize the region of interest.
  • the at least one structure comprises at least one structure adjacent to the region of interest.
  • indentifying the population of pixels in the electronic image comprises analyzing pixels in a predefined search area of the electronic image and assigning analyzed pixels having a particular property to the population of pixels associated with the at least one structure.
  • the particular property is a pixel brightness intensity.
  • the predefined search area is based on statistical analysis of historical data relating to a likely location for the at least one structure.
  • indentifying the population of pixels in the electronic image is based on input from an operator, the input comprising one or more locations associated with the at least one structure.
  • the computer instructions are further configured to cause the one or more computer processors to analyze the population of pixels based on at least one reliability criteria.
  • the at least one reliability criteria comprises a comparison of a number of pixels in the population of pixels to a threshold pixel number.
  • the computer instructions when the population of pixels does not satisfy the reliability criteria, the computer instructions cause the one or more processors to flag the population of pixels for review by a user.
  • the computer instructions when the population of pixels does not satisfy the reliability criteria, the computer instructions cause the one or more processors to employ additional algorithms to modify the population of pixels to improve reliability.
  • One aspect of the present disclosure is a system for obtaining information relating to a region of interest, the system comprising:
  • One mode of the present disclosure further comprises a magnetic resonance spectroscopy (MRS) system in communication with the one or more computer processors, wherein the MRS system is configured to provide an MRS spectrum of chemical constituents within the region of interest.
  • MRS magnetic resonance spectroscopy
  • One mode of the present disclosure further comprises a magnetic resonance imaging (MRI) system in communication with the one or more computer processors, wherein the MRI system is configured to provide a plurality of MRI images.
  • MRI magnetic resonance imaging
  • the electronic image is of a first imaging mode
  • the computer instructions are further configured to cause the one or more processors to access an additional electronic image of a second imaging mode different than the first imaging mode, the additional electronic image being of the substantially the same area as the initial electronic image, wherein the world coordinates are based in part on the additional electronic image of the second imaging mode.
  • the computer instructions are further configured to cause the one or more processors to:
  • the computer instructions are further configured to cause the one or more processors to:
  • process the additional electronic image to emphasize pixels in the additional electronic image that are associated with the at least one structure useful for identifying the region of interest; identify a population of pixels in the additional electronic image associated with the at least one structure;
  • One aspect of the present disclosure is a method for obtaining information relating to a region of interest, the method comprising:
  • the electronic image comprises a magnetic resonance imaging (MRI) image.
  • MRI magnetic resonance imaging
  • selecting image coordinates comprises selecting a two dimensional shape that covers a selected area of the region of interest when converted to world coordinates.
  • selecting image coordinates comprises calculating a two dimensional model that approximates the region of interest based on the population of pixels.
  • calculating the two dimensional model comprises applying an expectation maximization algorithm for estimating parameters of one or more Gaussian distributions for the population of pixels.
  • the computer instructions are configured to cause the one or more computer processors to position a two dimensional shape based on the two dimensional model to cover a selected area of the region of interest.
  • One mode of the present disclosure further comprises outputting information relating to the region of interest based on the world coordinates, the information comprising one or more of a location, an orientation, a shape, an area, and a volume of the region of interest.
  • One mode of the present disclosure further comprises:
  • the world coordinates define to a three dimensional selected volume of the region of interest.
  • One variation of the present disclosure further comprises scanning the three dimensional selected volume with a magnetic resonance spectroscopy (MRS) system in communication with the one or more computer processors to provide an MRS spectrum of chemical constituents within the three dimensional selected volume.
  • MRS magnetic resonance spectroscopy
  • One variation of the present disclosure further comprises analyzing a post- acquisition scan, using the one or more computer processors, to determine whether the region of interest moved during an MRS acquisition.
  • the region of interest is a nucleus of an intervertebral disc of a spine.
  • One variation of the present disclosure further comprises analyzing the MRS spectrum, using the one or more computer processors, to determine whether the three dimensional selected volume was likely mis-prescribed based on one or more signals.
  • One variation of the present disclosure further comprises defining one or more additional three dimensional selected volumes covering at least portions of one or more additional regions of interest, and scanning the additional three dimensional selected volumes one at a time with the MRS system using single voxel MRS.
  • One variation of the present disclosure further comprises defining one or more additional three dimensional selected volumes covering at least portions of one or more additional regions of interest, and scanning the additional three dimensional selected volumes simultaneously with the MRS system using multi voxel MRS.
  • the electronic image is of a first acquisition mode, and wherein the MRS spectrum is of a second acquisition mode different than the first acquisition mode.
  • One variation of the present disclosure further comprises:
  • One variation of the present disclosure further comprises defining a plurality of two dimensional shapes associated with the region of interest for the corresponding electronic images, wherein the three dimensional selected volume has a cross sectional shape corresponding to the overlapping area of the plurality of two dimensional shapes.
  • One variation of the present disclosure further comprises using with a patient therapy system to provide a therapy procedure to a patient based at least in part on the three dimensional selected volume.
  • the patient therapy system is a radiation therapy system or an ultrasound therapy system configured to direct energy to the three dimensional selected volume.
  • One embodiment of the present disclosure further comprises calculating a three dimensional model that approximates the region of interest based on the populations of pixels from the electronic images.
  • the electronic images comprise magnetic resonance imaging (MRI) images
  • the method further comprising acquiring the MRI images using an MRI system in communication with the one or more computer processors.
  • MRI magnetic resonance imaging
  • processing the electronic image comprises smoothing the electronic image.
  • smoothing the electronic image comprises modifying a brightness value for a pixel based on the brightness of neighboring pixels.
  • the neighboring pixels comprise one or more pixels from one or more additional neighboring electronic images.
  • processing the electronic image comprises performing at least one top-hat filtering operation.
  • processing the electronic image comprises performing at least one morphological image processing operation.
  • processing the electronic image comprises performing a first top-hat filtering operation on an upper portion of the spine and performing a second top-hat filtering operation on a lower curved portion of the spine.
  • processing the electronic image comprises performing an order statistic filtering operation.
  • the at least one structure comprises the region of interest, wherein processing the electronic image comprises emphasizing the region of interest.
  • the at least one structure comprises at least one structure adjacent to the region of interest.
  • indentifying the population of pixels in the electronic image comprises analyzing pixels in a predefined search area of the electronic image and assigning analyzed pixels having a particular property to the population of pixels associated with the at least one structure.
  • the particular property is a pixel brightness intensity.
  • the predefined search area is based on statistical analysis of historical data relating to a likely location for the at least one structure.
  • One mode of the present disclosure further comprises receiving input from an operator, the input comprising one or more locations associated with the at least one structure, and wherein identifying the population of pixels in the electronic image is based on the input.
  • One mode of the present disclosure further comprises analyzing the population of pixels, using the one or more computer processors, based on at least one reliability criteria.
  • the at least one reliability criteria comprises a comparison of a number of pixels in the population of pixels to a threshold pixel number.
  • One embodiment of the present disclosure further comprises, when the population of pixels does not satisfy the reliability criteria, flagging the population of pixels for review by a user.
  • One embodiment of the present disclosure further comprises, when the population of pixels does not satisfy the reliability criteria, employing additional algorithms to modify the population of pixels to improve reliability.
  • each of the actions recited is performed by the one or more processors.
  • the electronic image is of a first imaging mode
  • the method further comprises accessing an additional electronic image of a second imaging mode different than the first imaging mode, the additional electronic image being of the substantially the same area as the initial electronic image, wherein the world coordinates are based in part on the additional electronic image of the second imaging mode.
  • One mode of the present disclosure further comprises:
  • processing the additional electronic image to emphasize pixels in the additional electronic image that are associated with the at least one structure useful for identifying the region of interest;
  • One embodiment of the present disclosure further comprises:
  • processing the additional electronic image to emphasize pixels in the additional electronic image that are associated with the at least one structure useful for identifying the region of interest;
  • One aspect of the present disclosure is a method for prescribing a shape within a region of interest (ROI) in an electronic image of a body portion of a patient, comprising:
  • One aspect of the present disclosure is a method for configuring a medical system to be used in performing an operation on a region of interest (ROI) in a body portion of a patient, the method comprising:
  • One aspect of the present disclosure is a method for defining a region of interest (ROI) between bones in an electronic image of a body portion comprising a skeletal joint in a patient, the method comprising:
  • One mode of the present disclosure further comprises configuring a medical system in a configuration that is operable to perform an operation on the ROI.
  • the body portion comprises a skeletal joint and the ROI is located at least in part between bones of the skeletal joint
  • the method further comprising using the one or more processors to process the electronic image to identify a region between the bones and bordered at least in part by the bones, and to define the ROI to coincide with at least a part of the region.
  • One embodiment of the present disclosure further comprises configuring a medical system in a configuration that is operable to perform an operation on the ROI.
  • the body portion comprises a skeletal joint and the ROI is located at least in part between bones of the skeletal joint, and further comprising using the one or more processors to process the electronic image to identify a region between the bones and bordered at least in part by the bones, and to define the ROI to coincide with at least a part of the region.
  • One mode of the present disclosure further comprises using the one or more processors to process the electronic image to define the ROI.
  • One mode of the present disclosure further comprises using the one or more processors to process the electronic image to prescribe the shape.
  • prescribing the shape is entirely performed using the one or more processors.
  • prescribing the shape is partially performed using the one or more processors.
  • One mode of the present disclosure further comprises prescribing a shape to fit within the ROI.
  • One embodiment of the present disclosure further comprises using the one or more processors to process the electronic image to prescribe the shape.
  • One mode of the present disclosure further comprises prescribing a shape to fit within the ROI.
  • One embodiment of the present disclosure further comprises using the one or more processors to process the electronic image to prescribe the shape.
  • said electronic image comprises a 2D planar image.
  • One embodiment of the present disclosure further comprises using the one or more processors to process the 2D planar image to define the ROI as a 2D ROI in the 2D planar image.
  • One variation of the present disclosure further comprises using the one or more processors to process the 2D planar image to prescribe a 2D shape to fit within the 2D ROI.
  • One embodiment of the present disclosure further comprises using the one or more processors to process the 2D planar image to prescribe a 2D shape to fit within a 2D ROI.
  • said electronic image comprises a 3D electronic image constructed from a series of spatially unique but related 2D planar images of the body portion, and further comprising using the one or more processors to: process multiple said 2D planar images within the series to define multiple respective 2D ROIs therein, and to construct a 3D ROI in the 3D image from the multiple 2D ROIs.
  • the 3D ROI corresponds with a definable structure within the body portion, and further comprising using the one or more processors to process the 3D electronic image to define the 3D ROI by applying a template map to each of said plurality of 2D planar images and providing a default region predictive of locating said structure in each said respective 2D planar images based on prior knowledge derived from other similar 2D planar images from other patients, and processing the default region in the 2D planar image to define the 2D ROI from which the 3D ROI is constructed.
  • One variation of the present disclosure further comprises using the one or more processors to process multiple of the 2D planar images to prescribe a 3D shape to fit within the 3D ROI in the 3D image.
  • the ROI corresponds with a definable structure within the body portion, and further comprising using the one or more processors to process the electronic image to define the ROI using a template map providing a default region predictive of locating said structure based on location information derived from other electronic images from other patients, and processing the default region in the electronic image to define the ROI.
  • the default region processing comprises at least one of edge detection and a contrast filter.
  • One mode of the present disclosure further comprises using the one or more processors to process the electronic image to define the ROI using edge detection.
  • One mode of the present disclosure further comprises using the one or more processors to process the electronic image to define the ROI using a contrast filter.
  • One embodiment of the present disclosure further comprises using the one or more processors to process the electronic image to define the ROI using edge detection.
  • One variation of the present disclosure further comprises prescribing the 3D shape to achieve a criteria related to volume or dimension of the 3D shape.
  • the criteria comprises a maximum contained volume or dimension within the 3D ROI.
  • One variation of the present disclosure further comprises:
  • One variation of the present disclosure further comprises using the one or more processors to process the electronic image to prescribe multiple said 3D shapes within the 3D ROI.
  • One variation of the present disclosure further comprises prescribing the multiple 3D shapes to collectively achieve a criteria relating to volume of the 3D shapes.
  • the criteria comprises a maximum contained volume within the 3D ROI.
  • One variation of the present disclosure further comprises using the one or more processors to apply an inward dimensional off-set from an outer boundary of the 3D ROI for form an off-set 3D ROI, and prescribing the multiple 3D shapes to collectively achieve a criteria relating to a volume of the 3D shapes.
  • the criteria comprises a maximum contained volume within the off-set 3D ROI.
  • One variation of the present disclosure further comprises configuring a medical system to perform an operation on at least a part of the 3D ROI corresponding with the multiple 3D shapes.
  • the medical system comprises a nuclear magnetic resonance (MR) system, and further comprising:
  • the MR system configuring the MR system in a configuration that is operable in an operating mode to acquire MR-based data from at least the part of the 3D ROI, such that multiple portions of the MR-based data acquired in the operating mode correspond with each of the multiple 3D shapes;
  • the configuration comprises a T2-weighted imaging sequence.
  • the configuration comprises a Tl -weighted imaging sequence.
  • the configuration comprises an MR spectroscopy (MRS) pulse sequence.
  • MRS MR spectroscopy
  • the configuration comprises a Tl-rho pulse sequence.
  • One variation of the present disclosure further comprises correlating a value of the MR-based data for each of the 3D shapes with a diagnostic criteria.
  • One variation of the present disclosure further comprises displaying an indicia related to the correlation for each of the 3D shapes as an overlay to the electronic image. [0141] One variation of the present disclosure further comprises defining the 3D ROI and prescribing the 3D shapes before configuring the operating the medical system in the configuration.
  • One variation of the present disclosure further comprises using the defined 3D ROI and 3D shape prescriptions to configure the medical system in the configuration.
  • One variation of the present disclosure further comprises defining the 3D ROI and prescribing the 3D shapes after configuring the medical system in the configuration and after operating the medical system in the operating mode.
  • One variation of the present disclosure further comprises using the 3D shapes to correlate the operation corresponding with portions of the ROI represented by the respective 3D shapes.
  • One variation of the present disclosure further comprises:
  • MRS magnetic resonance spectroscopy
  • One mode of the present disclosure further comprises using the one or more processors to process the electronic image to prescribe a single shape that is a different shape than the ROI to fit within the ROI to achieve a criteria relating to volume or dimension of the single shape.
  • the criteria comprises a maximized dimension or contained volume within the single shape.
  • One mode of the present disclosure further comprises:
  • the ROI and the shape comprise different respective geometries.
  • the ROI comprises a geometry comprising at least one non-straight linear edge boundary.
  • the ROI comprises at least in part a curvilinear edge boundary.
  • the shape comprises a rectilinear geometry.
  • One mode of the present disclosure further comprises prescribing the shape for achieving a criteria related to volume or dimension of the shape.
  • the criteria comprises a maximized dimension or contained volume within the ROI.
  • the ROI comprises at least a portion of an intervertebral disc between two superior and inferior respective vertebral bodies bordering the disc.
  • One embodiment of the present disclosure further comprises using the one or more processors to define the ROI at least in part by locating two superior and inferior borders between the disc and the vertebral bodies in the electronic image.
  • the borders comprise vertebral body end-plates.
  • One variation of the present disclosure further comprises defining the ROI by defining at least one annular wall of the disc and connecting the vertebral bodies in the electronic image, such that the ROI is defined as a region contained between the borders and the at least one annular wall of the disc.
  • the electronic image comprises a 2D planar image through the disc and vertebral bodies, and further comprising defining the ROI by defining first and second opposite portions of the annular wall in the electronic image, such that the ROI is defined as a region contained between the borders and the two opposite portions of the annular wall.
  • the electronic image comprises a 2D planar image and each of the borders comprises a line first end and a second end, and further comprising defining the ROI by defining two connecting lines between first ends and second ends of the respective borders, thereby confining the ROI as an area contained within the borders and connecting lines.
  • the ROI comprises a nucleus portion of the intervertebral disc.
  • the ROI comprises an annulus portion of the intervertebral disc.
  • One variation of the present disclosure further comprises defining first and second said ROIs comprising a nucleus portion and an annulus portion of the disc, respectively, and prescribing a first shape to fit within the first ROI and a second shape to fit within the second ROI.
  • One mode of the present disclosure further comprises using the one or more processors to operate a computer program in a computer readable medium for performing the processing of the electronic image.
  • One mode of the present disclosure further comprises configuring an MR system in a configuration that is operable to perform an MR operation on at least a portion of the ROI corresponding with the shape.
  • One embodiment of the present disclosure further comprises configuring the MR system in a configuration that is operable to acquire a Tl -weighted image of at least the portion of the ROI.
  • One embodiment of the present disclosure further comprises configuring the MR system in a configuration that is operable to acquire a T2-weighted image of at least the portion of the ROI.
  • One embodiment of the present disclosure further comprises configuring the MR system in a configuration that is operable to acquire Tl-rho data of at least the portion of the ROI. [0169] One embodiment of the present disclosure further comprises configuring the MR system in a configuration that is operable to acquire an MR spectroscopy data from at least the portion of the ROI.
  • One mode of the present disclosure further comprises configuring a CT system in a configuration that is operable to perform a CT imaging operation on at least a portion of the ROI corresponding with the shape.
  • One mode of the present disclosure further comprises configuring an X-ray system in a configuration that is operable to perform an X-ray imaging operation on at least a portion of the ROI corresponding with the shape.
  • One mode of the present disclosure further comprises configuring a nuclear imaging system in a configuration that is operable to perform a nuclear imaging operation on at least a portion of the ROI corresponding with the shape.
  • One mode of the present disclosure further comprises configuring a PET imaging system in a configuration that is operable to perform a PET imaging operation on at least a portion of the ROI corresponding with the shape.
  • One mode of the present disclosure further comprises configuring a medical diagnostic system in a configuration that is operable to perform a medical diagnostic imaging operation on at least a portion of the ROI corresponding with the shape.
  • the medical diagnostic system comprises a combination of multiple unique imaging modalities comprising first and second modalities, and further comprising configuring at least the first modality in a respective first configuration to perform a first operation on at least the portion of the ROI.
  • One embodiment of the present disclosure further comprises configuring the second modality in a respective second configuration to perform a second operation on at least the portion of the ROI.
  • One variation of the present disclosure further comprises:
  • the medical diagnostic system comprises a combination MR/CT, PET/CT, or PET/MR system.
  • the body portion comprises a foramen.
  • the body portion comprises a body space defined by at least one tissue wall.
  • the body portion comprises at least a portion of an organ.
  • the body portion comprises at least a portion of a prostate gland.
  • the body portion comprises at least a portion of a breast.
  • the body portion comprises at least a portion of a brain.
  • the body portion comprises at least a portion of a tumor.
  • the body portion comprises at least a portion of a bone.
  • the electronic image comprises an image coordinate system, and further comprising defining the ROI in image coordinates.
  • One embodiment of the present disclosure further comprises prescribing the shape in image coordinates.
  • One variation of the present disclosure further comprises prescribing the shape in world coordinates. [0190] One mode of the present disclosure further comprises processing the electronic image to define multiple ROIs in the electronic image.
  • the multiple ROIs correspond to multiple intervertebral discs.
  • One embodiment of the present disclosure further comprises prescribing a plurality of shapes to fit within the plurality of ROIs in the electronic image.
  • One mode of the present disclosure further comprises:
  • the allowing comprises allowing the user to modify the recommended shape to prescribe the second shape as the shape.
  • the electronic image is of a first imaging mode
  • the method further comprising modifying the defined ROI or the prescribed shape based on an additional electronic image of a second imaging mode different than the first imaging mode.
  • the electronic image is of a first imaging mode
  • the method further comprising comparing the defined ROI or the prescribed shape to information derived from an additional electronic image of a second imaging mode different than the first imaging mode.
  • One aspect of the invention is a method for determining patient motion during a medical procedure on a patient, comprising:
  • One mode of the present disclosure further comprises mapping a voxel prescribed in a region of interest (ROI) based upon the first image onto same coordinates in the second image.
  • ROI region of interest
  • One embodiment of the present disclosure further comprises determining change of voxel location relative to the ROI in the first and second images.
  • One aspect of the present disclosure is a medical device system, comprising one or more processors configured to run computer instructs stored on one or more computer readable media to perform one or more of the actions described herein.
  • This aspect of the disclosure can be combined with the other aspects, modes, embodiments, variations, and features described herein to form various combinations and sub-combinations.
  • One aspect of the present disclosure is one or more computer readable media comprising computer instructions configured to cause one or more computer processors to perform one or more of the actions described herein.
  • This aspect of the disclosure can be combined with the other aspects, modes, embodiments, variations, and features described herein to form various combinations and sub-combinations.
  • the defining the ROI in the electronic image comprises:
  • processing the electronic image using one or more computer processors, to emphasize pixels associated with at least one structure useful for identifying the region of interest;
  • One embodiment of the present disclosure further comprises:
  • FIG. 1 is a flowchart that shows a method for performing manual voxelation.
  • FIG. 2A shows a mid-sagittal 2-dimensional MRI image of an intervertebral disc with a voxel manually applied thereto.
  • FIG. 2B shows a mid-coronal 2-dimensional MRI image of an intervertebral disc with a voxel manually applied thereto.
  • FIG. 2C shows an axial or oblique axial 2-dimensional MRI image of an intervertebral disc with a voxel manually applied thereto.
  • FIG. 3A shows a mid-sagittal 2-dimensional MRI image of an intervertebral disc with a mis-prescribed voxel applied thereto.
  • FIG. 3B shows a mid-coronal 2-dimensional MRI image of an intervertebral disc with a mis-prescribed voxel applied thereto.
  • FIG. 3C shows an axial or oblique axial 2-dimensional MRI image of an intervertebral disc with a mis-prescribed voxel applied thereto.
  • FIG. 3D shows an MRS spectrum resulting from the mis-prescribed voxel of FIGS. 3A-C.
  • FIG. 4A shows a mid-sagittal 2-dimensional MRI image of an intervertebral disc with a correctly prescribed voxel applied thereto.
  • FIG. 4B shows a mid-coronal 2-dimensional MRI image of an intervertebral disc with a correctly prescribed voxel applied thereto.
  • FIG. 4C shows an axial or oblique axial 2-dimensional MRI image of an intervertebral disc with a correctly prescribed voxel applied thereto.
  • FIG. 4D shows an MRS spectrum resulting from the correctly prescribed voxel of FIGS. 4A-C.
  • FIG. 5 is a flow diagram for an automated voxelation method.
  • FIG. 6 shows a mid-sagittal 2D planar MRI image of a spine.
  • FIG. 7A is a flow diagram showing the image processing portions of the flowchart of FIG. 5.
  • FIG. 7B is the mid-sagittal MRI image of FIG. 6 after the performance of certain image processing portions.
  • FIG. 8A is a flow diagram showing the ROI location and isolation portions of the flowchart of FIG. 5.
  • FIG. 8B is the mid-sagittal MRI image of FIG. 6 with increased contrast to highlight the intervertebral discs and showing default ROI areas.
  • FIG. 8C is the mid-sagittal MRI image with ellipsoids surrounding the automatically identified disc locations.
  • FIG. 9A is a flow diagram showing the 2D voxel formation portions of the flowchart of FIG. 5.
  • FIG. 9B is the mid-sagittal MRI image showing 2D voxel prescriptions within the respective discs of the 5 lumbar disc levels.
  • FIG. 10A is a flow diagram showing the 3D voxel formation portions of the flowchart of FIG. 5.
  • FIG. 10B shows a 3D voxel relative to two transverse mid-sagittal and mid-coronal 2D MRI images.
  • FIG. 11 is an example embodiment of a system for performing voxelation or for otherwise analyzing a ROI in one or more MRI images.
  • FIG. 12 is a flow chart illustrating example embodiments of methods for obtaining information relating to a ROI in one or more MRI images.
  • Various embodiments disclosed in the present disclosure relate to clinical magnetic resonance spectroscopy (MRS), in particular single voxel MRS, and still more particularly to the automated prescription of voxels within regions of interest where MRS is to be performed, such as, for example, intervertebral discs.
  • MRS magnetic resonance spectroscopy
  • One example application of the present disclosure is to single voxel MRS of the intervertebral discs of the lumbar spine, such as the three to five lower lumbar discs, such as for the purpose of diagnosing disc degeneration or discogenic pain.
  • the systems and methods described herein can be used in the application of MRS to diagnose chronic, severe discogenic low back pain by identifying discs that are more likely to be painful versus non-painful though chemical signature analysis of disc tissue.
  • RF radiofrequency
  • the target volume or three dimensional (3D) volume is referred to as a voxel (e.g., a single point or pixel with volume).
  • voxel can refer to the three dimensional target volume.
  • a two dimensional voxel shape can be a slice or cross section of the three dimensional voxel volume.
  • voxels are typically defined or prescribed using the sagittal, coronal, and axial or oblique axial two dimensional (2D) MRI images that include the region of interest ("ROI").
  • the MRS technician manually draws or enters coordinates for a pattern (typically a rectangle) in each of the three plane images, for example according to various present embodiments to outline the nucleus of a lumbar disc.
  • the graphical interface converts the 2D patterns in multiple transverse (in some cases orthogonal) planes into a 3D volume or voxel.
  • VOI volume of interest
  • voxel volume will typically directly correlate with signal strength.
  • a general goal of most MRS voxel prescriptions is thus to maximize voxel volume within a desired ROI for a tissue, so as to increase the signal strength, while still excluding bordering tissues or structures of potentially different chemical composition relevant to MRS spectral acquisitions from the ROI.
  • a rectilinear voxel may fit easily into a large ROI. This may be the case for example in many cases involved in applications for the brain or breast where the voxel volume is a small percentage of the ROI volume.
  • the ROI may be rather small and non- rectilinear and may consist of compound surfaces (e.g., having different curvature and/or shape at different portions of the perimeter of the ROI, such as nodules, which in some cases do not correspond to a smooth geometric equation or curve), such as resulting in a more ellipsoidal type shape (e.g. intervertebral discs).
  • a rectangle that is fitted for a particular planar slice through an ellipsoid ROI may not fit in the ROI of an adjacent slice, resulting in the voxel dimensions exceeding the shape of the ROI.
  • the MRS technician typically verifies that the voxel prescribed in one set of 2D images fits within the adjacent 2D images or slices that encompass the voxel. Even for skilled technicians, this can be challenging to get correct in many cases.
  • this structure can be bordered on some or all sides by different structures that are chemically distinct from the intended disc tissue. This is in particular the case superiorly (vertically above) and inferiorly (below) the disc, where vertebral body end-plates reside. These structures involve, among other constituents, bone marrow which are rich in lipid that has an MRS signature that overlaps with and may mask other target chemicals (such as for example lactic acid and alanine).
  • a mis-prescribed voxel for a disc nucleus MRS exam thus may contain tissue from the vertebral endplate adjacent to the nucleus resulting in resonant frequencies from both tissues.
  • the MRS signatures e.g., lipid
  • tissue surrounding the ROI can compromise the ability to assess overlapping chemicals of the ROI (e.g., lactic acid and alanine), in addition to simply representing the wrong tissue.
  • a degenerative painful disc itself may contain lipid.
  • lipid signal is representative only of bordering bone or end-plates and not the intervertebral disc
  • a lipid signature in a resulting acquired spectrum could indicate a mis-prescription and motivate a re-prescription in a repeat scan, such as by moving or shrinking the voxel.
  • lipids and associated MRS signatures can be present both in some actual disc tissues, and in a mis-prescription involving bordering vertebral bodies or the end-plates, the ability to recognize whether a mis-prescription occurred based on a lipid signal may be compromised in some cases.
  • the identification of a lipid signature in an acquired spectrum is not useful as an indicator of voxel mis-prescription, while in other cases a lipid signature may be indicative of a voxel mis-prescription.
  • detection of a lipid signal over a threshold level can cause an MRS acquisition to be repeated (with a voxel of the same or different size), disregarded, flagged for user review, and/or further analyzed for accuracy.
  • Time means money in imaging, and healthcare costs have become among the most prominent issues in all of modern society world-wide. At least near the top of such issues reside the rising costs of imaging. Moreover, time that a patient resides in an MRI environment for an exam, or "in the tube,” is limited due to patient comfort and other concerns. Over extended exam times, patients will often become more restless, thus more likely to move, and movement can confound an MR exam (e.g. patient movement after a voxel prescription can move the patient's tissues while the voxel location remains fixed in MR machine coordinates, effectively creating a voxel mis-prescription). Furthermore, patients such as low back pain patients may be in tremendous discomfort to begin with. If the manual process were replaced by one that was at least partially automated, significant time could be saved. For illustration, by reference to the example immediately above, replacing manual voxelation with a fully automated process could potentially cut the time of the exam by as much as 25-50%.
  • Manual voxelation thus represents a human operator-dependent process, and the results of the MRS exam can directly (and in some cases critically) depend on correct performance of the manual voxelation.
  • manual voxelation introduces the risk of human error.
  • the risk of human error is high, and the impacts of the potential error can be critical, the need to find a solution to remove this potential opportunity and source for human error becomes that much more important. This does not necessarily reflect badly on the human operators themselves.
  • Manual voxelation for single voxel spectroscopy may be very difficult to get right, especially in challenging MRS applications in small defined tissue regions. Inter-operator variability could be high even between the most highly skilled and diligent of operators, especially in particularly challenging cases.
  • an automated voxelation process can reduce or eliminate the occurrence of human error and can increase consistency and predictability in the voxelation process and in the MRS results.
  • one aspect of this disclosure provides a useful solution to replace, or at least augment, manual voxel prescription (e.g., for single voxel MRS exams) by an at least partially automated voxelation system and method.
  • the voxelation is either fully or nearly fully automated. According to one particular benefit, this can mitigate certain associated potential risks and issues that may impact single voxel MRS exams. According to another benefit, it shortens the time necessary for the voxelation portion of the exam, which shortens the time for the overall exam, which may result in more efficient delivery of healthcare, more patient comfort, and more robust results.
  • one mode of this aspect provides a system and method to automate the voxel prescription process by identifying the boundaries of the ROI in a set of 2D images that encompass the ROI, creating a 3D model of the ROI, and fitting a rectilinear shape/voxel within the ROI. The resulting dimensions and coordinates of the voxel are then presented to the MRS technician for entry into the graphical user interface.
  • the system may also process/define multiple voxels for multiple ROI within a common field of view (FOV) simultaneously.
  • FOV field of view
  • FIG. 1 shows the flow process of an MRS session 10 that uses manual voxel prescription. The process is illustrated by blocks 11-15 shown in FIG. 1 as described in further detail below. Detailed numbers such as for dimensions etc., as indicated immediately below and elsewhere herein this disclosure, are provided in order to describe specific illustrative embodiments only, and are provided as "about” approximations, and may vary from such specified values as apparent to one of ordinary skill.
  • sagittal, coronal, and axial or oblique axial MRI image sets can be captured.
  • a series (e.g., 13 slices) of high resolution T2-w MRI images of the ROI in the sagittal plane can be captured using a field of view (FOV) of 40cm x 40cm and a 4mm slice thickness.
  • a series (e.g., 13 slices) of high resolution T2-w MRI images of the ROI in the coronal plane can be captured using a field of view (FOV) of 40cm x 40cm and a 4mm slice thickness.
  • a single high resolution T2-w MRI image of the ROI in the axial or axial oblique plane can be captured using a field of view (FOV) of 40cm x 40cm and a 4mm slice thickness.
  • FOV field of view
  • MRI images may be produced using various other suitable parameters.
  • a series of multiple MRI images can be used in the axial or oblique axial plane instead of a single MRI image, and the series of sagittal and coronal MRI images may contain more or fewer than the 13 images described above.
  • the MRI image of the slice that intersects the center of the ROI in the sagittal plane can be identified.
  • the MRI image having the largest cross-sectional area of the ROI is the slice that intersects the center of the ROI.
  • slice number 7 or 8 will typically intersect the center of the ROI.
  • this process may be repeated for the MRI images in the coronal plane, to identify the MRI image of the slice that intersects the center of the ROI in the coronal plane. If a series of MRI images are used in the axial or oblique axial plane, an MRI image that intersects the center of the ROI in the axial or oblique axial plane can also be identified. If the axial or oblique axial MRI image set includes only a single image, that single MRI image can be used as the center image.
  • a rectangle can be drawn using the MRI graphical user interface in each of the three planes. For each axis, if an MRI image was identified in block 12 as intersecting the center of the ROI (e.g., having the largest cross-sectional area), that MRI image can be used to draw the rectangle for that axis.
  • the system can present the adjacent MRI images from the MRI image sets with the drawn rectangle projected onto the image.
  • the user can observe the projected rectangle in some or all of the MRI images in the sagittal MRI image set to verify that the rectangle fits inside the ROI for each slice in the sagittal orientation.
  • the user can observe the projected rectangle in some or all of the MRI images in the coronal MRI image set to verify that the rectangle fits inside the ROI for each slice in the coronal orientation.
  • the user can observe the projected rectangle in some or all of the MRI images in the axial or oblique axial MRI image set to verify that the rectangle fits inside the ROI for each slice in the axial or oblique axial orientation.
  • block 15 if needed, the user can adjust the dimensions, coordinates, and angle of the rectangle so as to keep the rectangle within the ROI. It will be understood that in some embodiments, block 15 can be omitted, for example, if no adjustments to the rectangle are needed after the initial rectangle designation. In some cases, the user may reduce the size of the rectangle so as to exclude area outside the region of interest. The user may also increase the size of the rectangle so as to capture more volume of the region of interest. The user may also change the angle or orientation of the rectangle so as to better fit the volume of the ROI.
  • the process can repeat block 14 to confirm that the adjusted rectangle fits into the ROI in some or all of the slices of the MRI images sets.
  • the user can repeat blocks 14 and 15 (multiple times if needed) until a rectangle is defined that covers a large portion of the ROI but does not extend outside the ROI.
  • the user may first adjust the rectangle in the sagittal and coronal directions and once satisfied with their positioning can confirm in the single axial or oblique axial plane MRI image that the rectangular dimensions are contained within the ROI in the axial or oblique axial direction.
  • the process can proceed to block 20 and perform the MRS scan. Blocks 11 through 20 can then be repeated for additional ROIs (e.g., additional intervertebral discs) to be scanned.
  • FIGS. 2A-C show mid-sagittal (FIG. 2A), mid-coronal (FIG. 2B), and axial or oblique axial (FIG. 2C) 2-dimensional (2D) planar views of MRI images taken during imaging phase of one illustrative MRS exam of a human subject's spine 30, including a disc 40 bordered by superior and inferior end-plates 45, 46 between disc 40 and superior and inferior vertebral bodies 50, 60 located above and below disc 40.
  • the disc 40 can include a disc nucleus 42 and a disc annulus 44. Shown superimposed on these planar MRI images is a rectangular voxel 70 drawn per a manual prescription as described above.
  • FIGS. 3A-3D and 4A-4D illustrate another MRS exam of another subject where one MRS exam conducted according to a mis-prescribed voxel (FIGS 3A-3D) is compared against the MRS results of another MRS exam conducted with a more appropriate prescription (FIGS. 4A-4D).
  • FIGS. 3A-3C show mid-sagittal (FIG. 3A), mid-coronal (FIG. 3B), and axial or oblique axial (FIG. 3C) 2-dimensional (2D) planar views of MRI images taken during imaging phase of an illustrative MRS exam of a human subject's spine 100, including a disc 110 bordered by superior and inferior end-plates 112, 113 between disc 110 and superior and inferior vertebral bodies 120, 130 located above and below disc 110. Shown superimposed on these planar MRI images is a rectangular voxel 140 drawn per a manual prescription as described above. The voxel 140 as shown in FIG.
  • MRS spectrum 150 includes a relatively narrow and distinctive n-acetyl aspartate (NAA) peak 152 typically representative of proteoglycan (PG) in disc nucleus tissues, and a still stronger peak 154 with much wider line width (e.g. broad between opposite sides of the peak, such as at 50% power of peak) that spans across spectral regions 156, 158 associated with lactic acid and alanine, respectively.
  • NAA n-acetyl aspartate
  • PG proteoglycan
  • This strong, broad peak region 154 is characteristic of lipid signal. If and to the extent any lactic acid or alanine signal may or may not contribute to the signal intensity in this region is difficult to ascertain.
  • FIGS. 4A-4C show similar images for the same spine 100 in the same subject, and in fact during the same MR study session, but for a different MRS exam scan according to a different manual voxel prescription shown at voxel 145.
  • voxel 145 has slightly reduced height vertically across disc 110 and potentially excludes end- plates 112, 113, either of which may have been partially captured by the larger voxel 140 with the larger vertical height dimension in the prior exam.
  • the Proteoglycan-related n-acetyl aspartate (NAA) peak 162 for spectrum 160 is slightly reduced signal intensity than the similar peak 152 shown in FIG. 3D
  • lipid peak 154 appears to have been the result of captured end-plate contaminant in the MRS spectrum 150 of the "over-prescribed" voxel dimensions in that case.
  • FIG. 5 shows a software flow diagram 200 of an automated voxelation method that can be used by an automated voxelation system, such as the system of FIG. 11 discussed below.
  • the system can include an MRI/MRS system configured to acquire MRI images of a region of interest in a portion of a patient's body and/or to perform an MRS exam or procedure using the voxelation.
  • the system can also include a computer system that can have a processor and a computer readable medium, which can be configured to execute a program that performs some or all of the method shown in FIG. 5. The method of FIG.
  • FIGS. 6-10B can include: image processing 210, including further detailed blocks 211-214 shown; ROI location and isolation 220, including further detailed blocks 221-224 shown; 2D voxel formation 230, including further detailed blocks 231-233 shown; and 3D voxel formation 240, including further detailed blocks 241-246 shown.
  • image processing 210 including further detailed blocks 211-214 shown
  • ROI location and isolation 220 including further detailed blocks 221-224 shown
  • 2D voxel formation 230 including further detailed blocks 231-233 shown
  • 3D voxel formation 240 including further detailed blocks 241-246 shown.
  • FIG. 6 shows a mid-sagittal 2D planar MRI image 250 of the same spine illustrated in the examples of manual voxel prescription shown and described above by reference to FIGS. 3A-4D, but prior to an automated voxel prescription process according to some embodiments.
  • FIG. 7A shows a flow diagram which reintroduces various detailed blocks 211-214 of image processing phase 210 of the automated voxelation approach illustrated above in FIG. 5.
  • the modified mid-sagittal 2D planar image 250 shown in FIG. 7B illustrates the results from the image processing described.
  • FIG. 8A reintroduces the flow diagram from FIG. 5 for a ROI location and isolation phase 220 of the automated voxelation program 200, including detailed blocks 221- 224.
  • FIG. 8B shows a mid-sagittal 2D planar MRI image 250 as in prior figures for the exemplary spine, but as modified to reflect the results arrived at following completion of the location of a unique population of pixels for the ROI at block 222 shown in FIG. 8A. More specifically, FIG. 8B shows default ellipsoid search areas 260 applied to the image 250, with the unique population of pixels estimated to represent the disc ROI shown at contrast enhanced bright pseudo-ellipsoidal regions 270 within default ellipsoid search areas 260.
  • FIG. 8C shows the same MRI image 250 as prior FIGS. 6, 7B, and 8B above, but as further modified to reflect and plot the estimated ellipsoid shapes 280 generated by the program for the respective disc nucleus regions at block 224 shown in FIG. 8A.
  • FIG. 9A reintroduces flow diagram 200 with respect to the 2D voxel formation phase 230, including more detailed blocks 231-233 in order to prepare for 3D voxel formation phase 240.
  • An illustrative result of this phase is shown in FIG. 9B, which shows the mid-saggital 2D planar image 250 with the 2D voxel prescriptions 290 shown within the respective discs of the 5 lumbar disc levels.
  • FIG. 9B also shows the 2D planar image 252 for the orthogonal transverse oblique axial plane for the L4-L5 disc in this example.
  • FIGS. 10A-B illustrate the result of this process as follows.
  • FIG. 10A reintroduces for flow diagram 200 the flow for 3D voxel formation phase 240, including more detailed blocks thereof 241-246.
  • An illustrative 3D voxel prescription result is shown for one disc of the spine in angular perspective view relative to two transverse mid-sagittal (e.g., yz plane) and mid-coronal (e.g., xz plane) 2D MRI images.
  • mid-sagittal e.g., yz plane
  • mid-coronal e.g., xz plane
  • the process locates an MRI image, which can be a sagittal MRI image through the center of the ROI (e.g., having the largest ROI cross-sectional area).
  • the automated voxelation can use the same working set of MRI images used in manual voxel prescription process.
  • the automated voxelation system and method can access the image files in each series and form a volume image structure for each consisting of a stack of image arrays.
  • the method for collecting the sagittal, coronal, and axial or oblique axial image sets can be performed automatically or can be partially automatic.
  • a user can identify a mid-sagittal location for the mid- sagittal image (e.g., by selecting one of a series of low resolution images that corresponds to a sagittal center position), and the system can automatically compile a series of high resolution MRI images including a mid-sagittal MRI image at the location indicated by the user as well as other sagittal images taken at planes substantially parallel to, and space from, the mid- sagittal image.
  • some or all of the image processing phase 210 and ROI location and isolation phase 220 can be performed on a plurality of MRI images before a center slice is identified, so that a center MRI image can be identified by automatically comparing the sizes of the ROI in the plurality of MRI images.
  • the system can access a plurality of MRI images taken from substantially parallel planes that intersect the ROI, can calculate an area of the ROI in the plurality of MRI images, and can use the image with the largest ROI area as the center slice for voxel positioning.
  • the system can calculate the ROI area for the multiple ROI in the plurality of MRI images and can select a center MRI image having the largest aggregate ROI area combined from the multiple ROI.
  • This approach can be beneficial, for example, when identifying multiple voxels, e.g., for multivoxel MRS procedures with multiple ROI.
  • a center MRI image can be selected based on the largest ROI area for a single ROI, and in some cases, the system can allow for different MRI images to be selected as the center MRI image for different ROI. This approach can be beneficial, for example, for single voxel MRS procedures.
  • the system can access multiple MRI images (e.g., taken from substantially parallel planes), and can test all or a subset of the images (e.g., the middle 7 images of an array of 13 image slices) to identify a center image.
  • the system can start with an image at the middle of a series of images and can measure the ROI area for a number of images (e.g., 3 images) on either side of the middle image in the series of images.
  • the selected center image can be an image other than the middle image of the series of MRI images, and the selected center image can, in some cases, correspond to a slice that is not through a center of the disc, or spine, or patient's body.
  • a center image can be the middle image of a series of MRI image slices, and can, in some cases, pass through substantially the center of the ROI, the disc, the spine, and/or the patient's body.
  • the system can identify the MRI images that have an ROI interest that meets a threshold amount, and can define the center MRI image to be the image at the middle of the series of images that meet the threshold FOI area amount.
  • the system can omit the identification of a center image.
  • the system can use some or all of the MRI images to generate a 3D model of the ROI without identifying a center image for the ROI.
  • a voxel can be formed based on the 3D model of the ROI without identification of a center image for the ROI.
  • the center image identification process can be repeated for the coronal series and axial or oblique axial series of MRI images in the coronal and axial or oblique axial planes.
  • the voxelation method 200 can use MRI images taken from only a single axis (e.g., the sagittal axis) for prescribing a voxel for the ROI.
  • the images of one or both of the other two axes e.g., the coronal and/or the axial images
  • the system can locate the ROI and/or positioned the voxel based on images from a single axis (e.g., sagittal) and the system can use one or more images from one or both of the other axes (e.g., coronal and/or axial) for displaying information to the user, such as for showing a 3D voxel in a 3D presentation between multiple images of different planes, as shown in Figure 10B and discussed below.
  • a single axis e.g., sagittal
  • the system can use one or more images from one or both of the other axes (e.g., coronal and/or axial) for displaying information to the user, such as for showing a 3D voxel in a 3D presentation between multiple images of different planes, as shown in Figure 10B and discussed below.
  • the system can convert the MRI image to a different format, such as from DICOM (Digital Imaging and Communications in Medicine) image data to MATLAB "gray" format with double precision intensity values in the range from 0 to 1.
  • a different format such as from DICOM (Digital Imaging and Communications in Medicine) image data to MATLAB "gray" format with double precision intensity values in the range from 0 to 1.
  • Portions of the MRI image can correspond to the ROI and surrounding area in a physical target object (e.g., a patient's spine).
  • a physical target object e.g., a patient's spine
  • An electronic image can be any electronic representation of an image, and may be related to MRI, CT, PET, X-ray, or other modality.
  • the electronic image can include an image coordinate system and a plurality of pixels with unique respective image coordinates.
  • Such electronic images may be considered "acquired" from a subject by virtue of the respective imaging modality that gathers information from the body which is then converted to the electronic image.
  • An electronic image may comprise for example a single 2D planar image with x-y, x-z, or y-z coordinates, or a series of related 2D images in different planes that provide a coordinated "picture" of a region, such as may be combined in an array of images to provide a 3D image.
  • various types of electronic images can be used in place of the MRI images that are specifically discussed.
  • the system can access metadata from the image files to support 3D interpretation of the image-coordinate data in 3D machine coordinates, also referred to as world coordinates.
  • the system can form 4x4 homogeneous coordinate transformation matrices from the metadata to support image to machine coordinate transformation.
  • images coordinate data corresponding to portions of the MRI image can be converted or transformed into world coordinates corresponding to portions of the target object.
  • the system can then organize all the above data into an Auto Vox data structure that contains all the information to create a 3D volume display in machine (or world) coordinates.
  • a 3D volume display is shown in Figure 10B, which is discussed below.
  • image processing can be performed to smooth the image, level the intensity variation from anterior to posterior, remove the bright spinal column and posterior fat signal, and finally emphasize the ROI (e.g., disc nucleus).
  • the result of image processing 210 can be an image consisting almost exclusively of emphasized sub-images of multiple ROIs, as shown in FIG. 7B.
  • an image smoothing operation 212 can modify the brightness of a pixel based on the brightness values of neighboring pixels, for example setting the pixel brightness to an average value, or a weighted average value, etc.
  • the neighboring pixels can be part of the same image (e.g., a 3x3 or 5x5 area around the pixel) and/or can be part of other images in the series of substantially parallel images (e.g., sagittal MRI images).
  • the 3x3x3 kernel mentioned above can use the adjacent image on both sides of the image containing the pixel being processed.
  • the kernel can be referred to as cubic because the number of pixels that are used to modify the brightness of a pixel are equal (e.g., 3) in each direction (e.g., x, y, and z), even though the physical volume may not form a cube.
  • the distance between adjacent MRI slices can be larger than the distance between pixels within the same image resulting in the cubic kernel coving a somewhat elongated rectilinear volume rather than a cube.
  • top-hat filtering can be performed on the MRI image.
  • the top-hat filtering can be configured to deemphasize (e.g., darken or remove) portions of the image that correspond to features other than the ROI while preserving the ROI portions of the image.
  • the spinal column and posterior fat can form bright portions of the image (e.g., due to high water content), and the top-hat filtering operation can be configured to deemphasize these features while preserving the disc portions of the image.
  • the system can perform, for example, morphological top-hat filtering on the image using vertical linear structuring element 50 pixels tall and 5 pixels wide.
  • the structuring element can be effectively 50 running averages with 5 data points per average that is shifted across the image.
  • the structuring element can be generally shaped like the shape of the structures to be deemphasized. As the structuring element is swept across the image, if the pixels covered by the structuring element satisfy a criteria (e.g., a threshold brightness level) then the pixels covered by the structuring element can be deemphasized (e.g., darkened or removed).
  • the structuring element can be generally tall and thin (e.g., 50 pixels tall and 5 pixels wide) so that the structuring element can fit into the vertically oriented spinal column and fat portions of the image and not fit into the intervertebral disc portions of the image which are generally ellipsoidal in shape and relatively short and wide.
  • the top-hat filtering can primarily target removing the bright water signal of the spinal canal and minimizing the signal from the posterior fat.
  • the top-hat filtering can primarily deconstruct the bright water-rich image of the spinal canal in the MRI image to facilitate the algorithm that searches for the bright (e.g., water-rich) oval shape associated with the disc nucleus. It also can have the effect of leveling the intensity of the image.
  • the top-hat filtering operation can basically remove (or darken) any parts of the image which it can encompass by the structuring element. Its dimensions are selected so it does not affect the ROI (e.g., disc nucleus) images.
  • a first top-hat operation does not effectively remove the signal from the lower curved portion of the spinal canal, typically below L4L5.
  • the system can then perform a second top-hat filtering operation with the structuring element as a line 50 pixels long and at a 45 degree slope to target the sloping lower portion of the spinal canal to target the lower curved portion of the spinal canal.
  • Many variations are possible. Many different configurations of top-hat filtering operations can be performed depending on the shape and structure of the ROI and the surrounding area, and multiple top-hat filtering operations of various different numbers can be performed.
  • other forms of morphological image processing operations can be performed to either emphasize the ROI or to deemphasize the regions of the image not associated with the ROI.
  • the system can perform order statistic filtering to further smooth and level the image while preserving edges.
  • two dimensional (2D) order statistic filtering can be used.
  • the kernel, or domain, for the order statistic filtering can be a 5 by 5 pixel square, although other sizes can be used.
  • the order statistic filtering can set the brightness of a pixel based on the brightness of neighboring pixels.
  • the sixth order can be used, although other orders can be used.
  • the filter operation can order the neighboring pixels and the analyzed area (e.g., 25 pixels in the 5x5 example) from darkest to brightest, and the filter operation can set the brightness value of the analyzed pixel to the sixth brightness value from the darkest.
  • the order statistic filtering operation can generally darken the image except for pixels of the image that are generally surrounded by other bright pixels.
  • the 5x5 kernel and sixth order parameters were optimized experimentally for some applications relating to intervertebral disc ROI, but many other configurations (e.g., of size, shape, and/or orders) can be used depending on the application. This operation can serve to remove small speckles and fill small holes while generally preserving edges of the ROI.
  • the method can use only one of 3D image smoothing 212 and order statistic filtering 214 for removing noise, etc. and the other of 3D image smoothing 212 and order statistic filtering 214 can be omitted.
  • the method can locate a center image 211 at a later stage of the method or the locating of a center image 211 can be omitted entirely.
  • additional image processing operations can be added in combination with or in place of the operations illustrated in the image processing phase 210 of FIG. 7 A.
  • noise reduction filters can be applied.
  • a contrast enhancing filter can be applied to emphasize contrast in the image to facilitate identification of the edges of the ROI.
  • edge detection can be performed on the image to identify or emphasize the edges of the ROI. The methods and systems disclosed herein can perform one or more of the operations of the image processing phase alone or with various combinations of the other components of the method shown in FIG. 5 or described herein.
  • the system can next isolate and locate the ROI (e.g., disc nuclei) at phase 220.
  • default search areas 260 can be applied to the image. For example, in the illustrated embodiment, statistics on the location of each disc level in the image have been developed. For the default search areas 260 shown in FIG. 8B, a training set of ten MRI studies where digitized to locate disc center and disc tilt. The means of the disc centroid and disc tilt were used to define an ellipsoid search area for each level, such as shown at default ellipsoid search areas 260 in FIG. 8B.
  • the search area ellipse 260 has a semi- major axis of 60 pixels and a semi-minor axis of 30 pixels and is centered on the mean location in image coordinates for a disc at that level, and the tilts for each level are 0°, 0°, -5°, -18°, and -30° for levels L1L2 to L5S1 respectively, although other configurations can also be used.
  • This approach is justified as the lumbar image prescription protocol is very well standardized and followed in the studies acquired to date.
  • the system may use active shape modeling to locate ROIs or operator intervention to identify and/or confirm ROIs.
  • this present illustrated embodiment thus provides a template for refining statistical methods to determine actual disc locations based upon an atlas applied to the image developed from pre-existing knowledge of typical locations derived from prior acquisitions and segmentation defining the disc locations (which segmentation may even be done manually for purpose of creating the atlas).
  • the template provides default regions in which the various disc levels in spines previously used to construct the template were known to be located, thus providing default regions applied to a given spine image so that processing algorithms may more effectively narrow a focused statistical search for the actual discs within that one new spine image.
  • a single default search area 260 can be applied if a single ROI is to be identified, and in some embodiments, a plurality of default search areas 260 can be applied if multiple ROI to be identified.
  • the system can analyze the population of pixels 270 based on a reliability criteria.
  • One criteria that may be applied is to require a certain threshold number, such as for example at least 300, of pixels in the pixel population (or grouped together) to qualify as a reliable estimate.
  • a tiered approach can be used depending on the number of pixels detects as part of the pixel population.
  • the pixel population may still be processed but considered, and flagged, as potentially unreliable; or, the pixel population may be kicked out of the automated system for manual prescription; or, further algorithms may be employed specifically tailored to overcome such circumstance.
  • the system can be set such that no attempt is made to locate the corresponding disc.
  • Many variations and alternatives are possible.
  • the comparison of the reliability criteria can reduce or avoid incorrectly keying on residual image artifacts as though they were a ROI.
  • the aforementioned values have been empirically determined for a given population of examples, which is relatively small. Thus, values other than those specifically disclosed herein may be used in view of further data of clinically relevant numbers representative of a given target population distribution. Also, values other than those specifically disclosed herein may be used for other specific applications other than that described herein (e.g., voxelation in regions of the body other than intervertebral discs).
  • the default and estimated aspects of these detailed embodiments applied may be influenced by certain subject-dependent variables, such as height, weight, BMI, gender, age, race or ethnicity.
  • the present disclosure contemplates that aspects of the present embodiments may take such variability into account, such as for example generating default ellipsoid search areas 260 from spine MRIs from target numbers of samples on such uniquely identifiable sub-population bases (e.g., based on age, gender, race, height, weight, or BMI).
  • FIG. 8B shows a bright spot 272 that appears structurally to be near but clearly outside of the L3-L4 disc nucleus, but is still nonetheless captured within that respectively assigned default ellipsoid search area 260.
  • the bright spot 272 is separated from the more heavily populated pixilated region that does appear to be the respective disc nucleus.
  • Such non-target tissue region artifact 272 may result in statistics used in further processing to capture signal intensity from such peripheral structures outside of the target disc nucleus, and thus potentially compromise the statistics used to estimate the disc nucleus.
  • FIG. 8C shows one possible result for this example at the estimated ellipsoid shape 285 for that same L3-L4 disc. Relative to the other estimated disc nucleus ROI ellipsoids 280, the estimated shape 285 is slightly off-angle and extended partially in the direction of the artifact signal 272, and appears to potentially capture some inferior vertebral body structure.
  • some embodiments of this disclosure contemplate use of further algorithms and methods to distinguish possible sources for such potential artifact, such as by statistical methods to exclude artifacts in one step from being used in calculations in the next step (e.g., confidence interval calculations, basing on separation between strongest contiguous signal region, location relative to the default ROI, combinations thereof, etc.).
  • artifacts can be excluded by review of the quality of the results 285 shown in FIG. 8C relative to signal intensity, contrast, or image homogeneity captured within the drawn shape.
  • the results can be modified to correct or allow for circular correction algorithms back to prior operations (but performed differently based on data obtained from subsequent operation(s)).
  • a neighboring function can be performed that identifies that the identified population of pixels includes a neighboring group of pixels near the main group of pixels, and the neighboring function can cause the neighboring group of pixels to be excluded from analysis of the population of pixels.
  • the neighboring function can identify neighboring groups of pixels that do not contact and are separated from the main group of pixels.
  • the neighboring function can be configured to identify a neighboring group of pixels that do contact the main group of pixels by recognizing that the neighboring group of pixels do not conform to an expected shape of the ROI, for example, if the bright spot 272 of Figure 8B were connected to the bright ellipsoid disc shape as an arm or extension that does not fit with the ellipsoid shape.
  • further manual opportunities may be provided for assistance.
  • the image 250 shown as modified in FIG. 8B may be presented to a user to allow for regions to be manually captured (e.g. mouse drag) for exclusion or inclusion.
  • Manual indications of areas of exclusion or inclusion may be done as a matter of course or may be done only when a particular interim result is "flagged" as potentially erroneous and thus appropriate for manual intervention.
  • review of the process and/or results can be performed in order to optimize results or to correct or remove sources of error.
  • the system can calculate a two dimensional model from the shape of the population of pixels.
  • an expectation maximization algorithm for decomposing Gaussian mixtures (EM_GM algorithm) can be applied at block 223.
  • the EM_GM algorithm can provide parameters (e.g., means and covariance matrix) of a two dimensional Gaussian distribution representing the spatial distribution of the pixels in the cluster.
  • a cross section or footprint of the 2D Gaussian distribution can have a generally ellipsoidal shape.
  • Various other shape approximation operations can be used to estimate the shape of the ROI based on the population of pixels identified at block 222.
  • active shape modeling, statistical shape modeling, or various other techniques such as those generally referred to as blob detection can be used to estimate the shape of the ROI.
  • the image processing can emphasize the ROI so that the process can locate the populations of pixels without using the applying the default search areas at block 221.
  • a default area e.g., formed by atlas-based segmentation and/or historical date of common ROI locations
  • the identified ROI, or prescribed voxel can be flagged as potential unreliable, disregarded, presented to the user for review, recalculated, and/or adjusted.
  • operations can be added in combination with, or in place of, the operations illustrated in the method shown in FIG. 8A.
  • various types of blob detection techniques can be used to identify the shape of a ROI.
  • the method can use user input along with automated procedures to identify the ROI.
  • the user can be permitted to identify a location (e.g., by clicking on the image) that is inside of the ROI (a single location for a single ROI or multiple locations for multiple ROI) to provide guidance in locating the ROI.
  • the process can search within an area centered on the location identified by the user to search for the ROI boundaries.
  • the process can use an edge detection operation to identify edges of the ROI around the location identified by the user.
  • the program can also use the image brightness of the location clicked by the user in determining what brightness threshold to apply for identifying other pixels associated with the ROI.
  • the contrast ratio between ROI and non-ROI portions of the image can vary significantly between patients, but if the user specifies a location that is known to be part of the ROI, the brightness of that portion of the image can be used to set the brightness threshold used to identify other portions of the same ROI or of other ROI.
  • the program can allow the user to identify (e.g., click) points outside the ROI, such as along one or both of the end plates that surround the intervertebral disc, and the program can use those points to guide the locating of the end plates or of the ROI (e.g., disc between the end plates).
  • the program can select the voxel size and/or orientation based at least in part on the clicked end plate points, e.g., by fitting a linear line or curve between the clicked end plate points and placing the voxel between the lines associated with the end plates.
  • the end plate clicks can be used to define a search area for locating the ROI as well.
  • the methods and systems disclosed herein can perform one or more of the operations of the ROI location and isolation phase 220 alone or with various combinations of the other components of the method shown in FIG. 5 or described herein.
  • ROI discret nucleus characteristic data provided per the operations above, it becomes possible to automatically form two dimensional (2D) shapes or voxels, at phase 230.
  • image coordinate data can be extracted, and at block 232, the image coordinates can be converted to world coordinates using a transformation matrix.
  • the system can, for example, apply an experimentally determined scale factor (e.g., of 2.9 times the image to world scale factor (e.g., 0.39 from the DICOM metadata)) to the semi- minor axis to determine the voxel thickness in the z-axis (axial axis), and to the semi-minor axis to determine the voxel dimensions in the machine y-axis (coronal axis), as shown in block 233.
  • the 2D shape can be a rectilinear shape such as a rectangle, although many other 2D shapes can be used. Many variations are possible.
  • Various components of the 2D voxel formation phase 230 can be combined or omitted.
  • the 2D shape can be defined in image coordinates and then the coordinates of the 2D image can be transformed to machine (or world) coordinates, or the transformation to world coordinates can be omitted from the 2D voxel formation phase 230, for example by transforming image coordinates to world coordinates after the 3D voxel is formulated.
  • the methods and systems disclosed herein can perform one or more of the operations of the 2D voxel formation phase 230 alone or with various combinations of the other components of the method shown in FIG. 5 or described herein.
  • 3D Voxel Formation of phase 240 can include determining voxel width across the coronal plane or x-axis (sagittal axis), which can include the following.
  • the system can locate a sagittal slice adjacent to the previous processed sagittal image slice at block 214, and can repeat image processing 242, ROI location and isolation 243, and 2D voxel formation 244 for the sagittal slice adjacent to the previously processed sagittal image.
  • the system can compare the newly calculated 2D voxel to the 2D voxel from the previously processed sagittal slice to determine if the previously established 2D voxel dimensions fit within the current 2D voxel of the current sagittal slide being analyzed.
  • the system can modify (e.g., decrease) the 2D voxel dimensions if required to fit the current 2D voxel.
  • a three dimensional (3D) volume or voxel can be formed using the final 2D voxel dimensions as a cross sectional shape for the 3D voxel projected across the width of the analyzed sagittal slices to form a rectilinear volume.
  • the volume of the 3D voxel can be increased or maximized while also being contained within the ROI.
  • various 3D voxel shapes and sizes can be used having various different dimensions (e.g., a voxel having a smaller height may have a larger length and/or width while remaining contained in the ROI than a voxel having a larger height, which can have a smaller length and/or width in order to fit into the ROI).
  • the system can select a 3D voxel having a maximized or increased volume.
  • one or more of the MRI images may not contribute to the formation of the voxel, for example, if inclusion of an MRI image near the end of the ROI would require that the height of the voxel be reduced to a degree that would lower the total volume of the voxel, the MRI image near the end of the ROI can be disregarded for the forming of the voxel.
  • the volume can be maximized by defining a voxel for multiple combinations of MRI images and the voxel with the largest volume can be selected. For example, for an array of MRI images having 13 slices, voxels can be defined using 13 slices, 12 slices, 11 slices, 10 slices, etc.
  • the volumes for the voxels can be calculated and compared and the voxel having the largest volume can be selected (e.g., a voxel using 9 MRI images and omitting the 2 end slices from each side).
  • the voxel can be formed to provide an increased or maximized dimension (e.g., height, width, or length) of the voxel.
  • the process can define the voxel contained within an inward offset from the boundaries of the ROI.
  • the offset can provide a buffer that can prevent the voxel from covering non-ROI tissue in the event of minor inaccuracies in voxelation and/or minor patient movement during a procedure.
  • the voxel were defined to reach to the edge (or very close to the edge) of the ROI, patent movement during the examination or minor inaccuracies in voxelation can cause the voxel to cover a portion of non-ROI tissue during a procedure, which can reduce the quality of the procedure as discussed above.
  • the offset can be applied at various stages of the process.
  • the final voxel can be reduced in size after voxelation.
  • the population of pixels can be reduced in size after being populated, for example, by removing a layer of pixels (e.g., 1, 2, 5, 10 pixels, etc.) at the edge of the population of pixels.
  • the size of the shape that approximates the population of pixels can be reduced in size, or the 2D voxel shapes can be reduced in size before they are used to form the 3D voxel.
  • the process can be configured to increase or maximize the area of the voxel (similar to the description above) while containing the voxel within the inward offset boundary of the ROI.
  • multiple voxels can be formed for a single ROI.
  • a plurality of rectilinear voxels can be positioned inside of an ellipsoidal shape to increase the amount of the ellipsoidal shape that is included for a procedure.
  • the plurality of voxels can have different shapes and/or sizes.
  • a relatively large voxel can be positioned at a central region of the ROI and one or more smaller voxels can be positioned around the larger voxel in the ROI.
  • the plurality of voxels can have the same size.
  • a standard voxel size and shape e.g., cube or square
  • the process can be configured to fit the standard voxels into the shape of the ROI.
  • the y-axis (coronal axis) value can be set equal to the x-axis (sagittal axis) value, thereby simplifying the voxel formation.
  • the system instead of modifying the 2D shape as needed at each sagittal layer, the system can form the 2D shapes for each sagittal layer independent of the other layers, and the system can then define the cross sectional shape of the 3D voxel to be the area shared by all the 2D shapes when the 2D shapes are overlay on each other.
  • the 3D voxel can be a non-rectilinear volume shape.
  • the 3D voxel can be formed by connecting the multiple 2D shapes (e.g., by interpolating) to form a 3D voxel, which can have an irregular shape not defined by an equation or mathematical shape.
  • the system can skip the 2D voxel formulation phase 230.
  • the system can calculate a 3D model for the ROI based on the populations of pixels that are identified in the series of parallel sagittal slices.
  • a 3D voxel can then be selected that fits into the 3D model.
  • a 3D voxel can be selected that provides an increased or substantially maximized volume for the given 3D model.
  • the 3D voxel can be displayed, for example, so that a user can inspect the 3D voxel.
  • the 3D voxel can be displayed relative to a mid-sagittal MRI image, a mid-coronal MRI image, and/or an axial (or oblique axial) MRI image (not shown in FIG. 10B), so that the 3D voxel can be compared to the ROI displayed on the MRI images.
  • the system can allow the user to manipulate the view of the 3D voxel for inspection at various angles.
  • the system can allow the user to manually adjust the size or shape of the 3D voxel.
  • block 245 can be omitted. In some embodiments, block 245 can be performed only if one or more of the prior operations caused the procedure to be flagged as being potentially unreliable.
  • the system can display the voxel dimensions, coordinates, and/or angles for the user.
  • the voxel dimensions, coordinates, angles, and/or other voxel data can be transferred to an MRS system so that the 3D voxel can be used as a scan area during a MRS exam, as described above.
  • the system can provide the 3D voxel information to the MRS system in machine (or world) coordinates.
  • the conversion from image coordinates to world coordinates can be performed at various stages of the process 200.
  • the system can convert image coordinates for the pixels in the population of pixels to world coordinates, and the formation of the 2D model, 2D voxel, 3D model, and/or 3D voxel can be done in world coordinates.
  • the system can use image coordinates for formation of the 2D model, 2D voxel, 3D model, and/or 3D voxel, and the final data transferred to the MRS system can be converted to world coordinates. It will be understood that the conversion from image coordinates to world coordinates can be performed at other stages of the process 200 than those specifically identified.
  • the methods and systems disclosed herein can perform one or more of the operations of the 3D voxel formation phase 240 alone or with various combinations of the other components of the method shown in FIG. 5 or described herein.
  • Various components of the illustrated 3D voxel formation phase 240 can be combined, omitted, or supplemented with additional components.
  • the program can use images of different modes to improve the accuracy of the voxelation process or to check the accuracy.
  • Different modes of MRI images can be, for example, MRI images made using a Tl process (first mode) and MRI images made using a T2 process (second mode).
  • the images can be made of the same tissue to allow for comparison between the images of the different modes.
  • the voxelation process can be performed on the images of different modes, and the resulting voxel results can be compared. Because images of different modes can have differences such as different contrast ratios applied to different tissues, the resulting voxels can be different for each mode of imaging.
  • the voxels produced using the different modes of images can be combined (e.g., averaged) to form a final voxel to be used in the MRS exam.
  • one mode of images does not sufficiently represent one aspect of the tissue being imaged
  • one or more of the other modes of images can better represent that aspect of the tissue and improve the accuracy of the final voxel.
  • the voxels produced by the different modes of images can also be compared, and if the differences between the voxels is above a threshold level, the voxelation process can be flagged as potentially unreliable, can be restarted, can be aborted, or can be supplemented with additional operations designed to minimize errors.
  • the information derived from the images of different modes can be compared or combined before voxel formation, for example after defining populations of pixels for the multiple images of different modes, the defined pixel populations can be combined (e.g., averaged) or compared to confirm accuracy.
  • FIG. 11 schematically illustrates an example embodiment of a system 1100 that can be configured to perform the process 200, or some portion thereof, or some variation thereof.
  • the system 1100 can include a computer processor 1102 and computer readable medium 1104.
  • the processor 1102 can be a general purpose processor or a special purpose processor, and the computer readable medium 1104 can be, for example, a tangle, non-transitory computer readable medium such as a hard disk, a non-volatile memory module, a volatile memory module, an optical disc, etc.
  • the computer readable medium 1104 can include computer instructions 1106 (e.g., a software program) which can be configured to cause the system 1100 to perform the method 200, or some portion thereof, or some variation thereof, as disclosed herein.
  • different code modules can be stored on separate computer storage devices or media, and can be executed by different processors or machines.
  • the computer readable medium can include one or more MRI images 1108 to be used for voxelation or for otherwise analyzing a ROI represented in the MRI images.
  • the system can include an MRI system 1110, which can be used to acquire the MRI images 1108.
  • the system 1112 can also include an MRS system configured to perform an MRS exam, which can use a voxel provided by the system 1100 as a scan area.
  • the MRI system 1110 and the MRS system 1112 can be integrated into a single system configured to generate MRI images and to perform MRS examinations.
  • the MRI system 1110 and/or the MRS system 1112 can be omitted, and the system 1100 can perform voxelation without being connected to the MRI system 1110 or the MRS system 1112.
  • the components of the system 1100 can be in communication with each other and can be located in close proximity to each other.
  • the entire system 1100 can be integrated into a single device (e.g., with a computer system integrated into a joint MRI/MRS system).
  • the components of the system 1100 can be located in a single room or within the same building (e.g., a hospital).
  • some components of the system 1100 can be located remotely from other components.
  • the MRI system 1110 and/or the MRS system 1112 can be located remotely from the processor 1102 and computer memory 1104, and a communication connection can be established using the internet or a network.
  • the prescription of one or more voxels, or other analysis of the ROI can be performed by a different system or by a different party than the system or party that acquires the images 1108 and/or performs the MRS exam. In some embodiments, the prescription of one or more voxels, or other analysis of the ROI, can be performed at a later time and/or at a different place than the acquisition of the images 1108 and/or than the MRS exam.
  • the MRI images can be acquired (e.g., by MRI system 1110) using a first acquisition mode (e.g., T2 MRI imaging) and an MRS procedure (e.g., performed by MRS system 1112) can be performed using a second acquisition mode (e.g., Tl rho MRI imaging).
  • a first acquisition mode e.g., T2 MRI imaging
  • MRS procedure e.g., performed by MRS system 1112
  • Tl rho MRI imaging e.g., Tl rho MRI imaging
  • the ROI locating and voxelation can be performed based on data obtained from the first acquisition mode (e.g., T2) and the voxel can be used for a procedure or exam that uses the second acquisition mode (e.g., Tl rho).
  • information provided by the ROI locating or voxelation process can be used for treatment of a patient.
  • a voxel can identify a target area in the patient's body for treatment such as radiation therapy, high-intensity focused ultrasound therapy, or various other procedures.
  • MR nuclear magnetic resonance
  • CT computed tomography
  • X-ray X-ray
  • PET imaging system PET imaging systems
  • other imaging systems can be used for identifying or analyzing the ROI.
  • MRI images it will be understood that CT images, PET images, X-ray images, or images of a different modality can be used in combination with or instead of the MRI images discussed herein.
  • voxel or ROI information it will be understood that CT, PET, X-ray, and other procedures can be performed based on the voxel or ROI information.
  • FIG. 12 is a flow diagram showing various example methods.
  • a method can start at block 1202 by processing an MRI image as described herein, and at block 1204, the system can identify a population of pixels relating to the ROI in the MRI image. The process can proceed to block 1206 where the system can form a 2D model of the ROI shown in the MRI image using the population of pixels. The 2D model can be used to form a 2D voxel shape at block 1208. In some embodiments, the system can form a 2D voxel shape directly from the population of pixels, without forming a 2D model of the ROI (as shown by the arrow between blocks 1204 and 1208).
  • the process can proceed to block 1210 after forming a 2D model or a 2D voxel shape, or the process can proceed to block 1210 after identifying a population of pixels for the MRI image (e.g., without forming a 2D model or voxel shape).
  • the process can move to block 1212 to access the next MRI image, and then proceed back to block 1202 to repeat. If no additional MRI images are to be analyzed, the process can proceed to block 1214 to form a 3D model of the ROI (e.g., using data from the populations of pixels from the MRI images or by calculating a composite of the 2D models made at block 1206 for the MRI images).
  • the process can advance to block 1216 and use the 3D model to form a 3D voxel that fits into the 3D model of the ROI.
  • the process can also advance from block 1210 to block 1216 to create a 3D voxel without a 3D model.
  • the process can form a 3D voxel by expanding one of the 2D voxel shapes (formed at block 1208) across the width of the MRI image slices, and the process can adjust the shape of the 2D voxel shape as needed to remain inside the ROI for each MRI image slice, as described above.
  • the lines leading from blocks 1204, 1206, 1208, 1214, and 1216 to the block 1218 illustrate that at various stages of the process, data can be converted from image coordinates to world coordinates (e.g., for use in an MRS exam or for locating a ROI).
  • the image coordinates corresponding to the population of pixels can be converted to world coordinates that represent the location of a ROI.
  • the method can be performed for a single image and can proceed from block 1202, to 1204, and then to 1218.
  • one or more 2D models (for a single MRI image or multiple MRI images), or a 3D model can be converted to world coordinates for identifying the location, size, and/or orientation of the ROI.
  • the method does not create a voxel to define a scan area for an MRS exam, and the process can merely provide information about the ROI (e.g., size, location, orientation) to a user or to a system.
  • the world coordinates can be used for an MRS exam or other procedure or can be reported to a user or system for additional analysis.
  • the world coordinates can be used for additional portions of the processes disclosed in Figure 12. For example, a 3D model formed at block 1214 can be converted to world coordinates at block 1218 and the world coordinates can be used to form a 3D voxel at block 1216.
  • the 2D voxel shapes formed at block 1208 can be converted to world coordinates and the world coordinates can then be used for forming the 3D voxel at block 1216.
  • Many other variations are possible.
  • Alternative flow paths through the flow cart of FIG. 12 are possible other than those specifically discussed, and are contemplated as part of this disclosure.
  • the methods illustrated by FIG. 12 can be implemented by a system such as system 1100 of FIG. 11.
  • the present description provides for an automated voxelation system and method useful for providing voxels (e.g., for single voxel MRS exams), with particular application described for lumbar intervertebral disc nuclei.
  • voxels e.g., for single voxel MRS exams
  • the broad aspects may be applied to other specific applications without departing from the broad intended scope hereof, such as larger disc volumes to include annulus (often becoming less distinguished from nuclei in degenerative discs), other disc levels along the spine, or other structures of the body where single voxel MRS might be performed.
  • the methods and systems described herein can be used in connection with multivoxel MRS exams.
  • coronal or axial (or oblique axial) images can also be used.
  • a series of coronal plane slices can be used to create a 3D voxel, for example, by creating 2D voxel shapes for the coronal images, modifying the 2D voxel shapes as needed to remain in the ROI, and defining a voxel length (in the y-axis) from the distance covered by the series of coronal images.
  • Other methods disclosed herein can also be modified to use coronal or axial (or oblique axial) images where sagittal images are described.
  • the various specific approaches taken among the various methods herein described for the specific application of automated voxelation also have other beneficial uses than only in such overall system and method.
  • the current disclosure describes a system and method for automatically estimating the location, shape, and volume of intervertebral disc nuclei based on MRI images from an otherwise standard MRI exam. While this can be beneficial for further use in the additional automated voxelation approaches further developed in these detailed embodiments, this approach and result alone is considered an independent, beneficial aspect of the present disclosure, with many beneficial uses contemplated.
  • such result may be applied as useful for other MR-based exams and pulse sequences, such as for example estimating Tl or T2 signal intensities, Tl-rho data, etc. for the defined ROI.
  • the voxelated results of the disclosure may be used for other applications than merely MRS, such as immediately described above.
  • MRS and Tl-rho values can both be taken for the same ROI or voxel, such as for example in a complex multi- pulse sequence exam of disc chemistry, which may be algorithmically diagnostically useful, such as for example in calibrating an MRS curve based on NAA/proteoglycan peak region calibration against Tl-rho-based calculations for the same.
  • the location of the disc e.g., the center of the disc nucleus
  • the orientation of the disc e.g., the angle of tilt
  • the location and orientation of the disc can be used to prescribe the location and orientation for MRI image slices to be used in clinical analysis. This can be particularly advantageous in the axial or oblique axial orientation where the angle of disc tilt can vary significantly between discs and between patients.
  • one or more edge detection algorithms may be used to estimate the location and dimensions of the vertebral body end-plates bordering above and below discs, to facilitate exclusion of the end- plates from a disc voxelation and MRS exam.
  • edge detection algorithms e.g. contrast based, filter assisted, etc.
  • These perimeter structures may themselves be estimated to prescribe a perimeter around a disc, from the "outside-in” approach, and in which a voxel prescription is then optimized.
  • the methods and systems described herein can identify a population of pixels associated with a structure adjacent the ROI or otherwise useful in determining the location of the ROI.
  • a model of the ROI and/or a voxel defining a scan area can then be created in a manner similar to the descriptions above but wherein the populations of pixels are used to define the ROI from the "outside-in" approach instead of defining the ROI directly.
  • the anterior and posterior borders are less defined than the superior and inferior end-plates (which are typically well defined via Tl and/or T2 MRI contrast).
  • the system can automatically connect the anterior and posterior ends of the curvilinear estimated lines for the superior and inferior end-plates to define the anterior and posterior borders to appropriately encapsulate the disc in at least many cases, if not nearly all cases with only fringe exceptions.
  • Such manual operations can either replace some of the automated estimation operations provided herein (e.g., replacing the use of empirically derived default ellipsoid search areas), or may rather enhance the likelihood of accurate results (e.g., to better refine the locations at or by which to apply the other estimation algorithms).
  • manual overrides may be provided, at operator option. For example, after automated voxelation, the users may be permitted to re-prescribe or modify the automated result based on their own observations, and/or knowledge of patient movement. In some cases the result of the automated voxelation can be automatically flagged for manual adjustment or inspection, as described herein.
  • the system may prompt the user to inspect or modify the automatically generated voxel.
  • the interim operations taken by the automation system may be made invisible to the operator, or all or various operations may be made available to user for review, which may aid in interpreting results.
  • the operations can be made available to a reviewer of the results after completion of the voxelation or MRS exam report.
  • the system can perform a post-acquisition scan for validation that the subject did not move during the MRS data acquisition.
  • the system can automatically locate the targeted disc after the MRS data acquisition and compare that post- acquisition position to the pre-acquisition position to determine if the voxel prescription remains valid after the data acquisition.
  • the post-acquisition scan can be faster than the initial scan. For example, in some cases only a limited area is scanned that is near the original position of the disc. In some embodiments, only a post-acquisition mid- sagittal scan is compared to the pre-acquisition mid-sagittal scan to assess patient movement.
  • the post-acquisition disc location can be identified similarly as described above in connection with the pre-acquisition scan, and the coordinates of the pre and post- acquisition scans can be compared and the acquisition can be deemed invalid if the difference is greater than a threshold value.
  • the order of the steps and/or phases can be rearranged and certain steps and/or phases may be omitted entirely.
  • the methods described herein are to be understood to be open-ended, such that additional steps and/or phases to those shown and described herein can also be performed.
  • Computer software can comprise computer executable code stored in a computer readable medium (e.g., non- transitory computer readable medium) that, when executed, performs the functions described herein.
  • computer-executable code is executed by one or more general purpose computer processors.
  • any feature or function that can be implemented using software to be executed on a general purpose computer can also be implemented using a different combination of hardware, software, or firmware.
  • such a module can be implemented completely in hardware using a combination of integrated circuits.
  • such a feature or function can be implemented completely or partially using specialized computers designed to perform the particular functions described herein rather than by general purpose computers.
  • Multiple distributed computing devices can be substituted for any one computing device described herein.
  • the functions of the one computing device are distributed (e.g., over a network) such that some functions are performed on each of the distributed computing devices.
  • any such computer program instructions may be loaded onto one or more computers, including without limitation a general purpose computer or special purpose computer, or other programmable processing apparatus to produce a machine, such that the computer program instructions which execute on the computer(s) or other programmable processing device(s) implement the functions specified in the equations, algorithms, and/or flowcharts. It will also be understood that each equation, algorithm, and/or block in flowchart illustrations, and combinations thereof, may be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer- readable program code logic means.
  • computer program instructions such as embodied in computer-readable program code logic, may also be stored in a computer readable memory (e.g., a non-transitory computer readable medium) that can direct one or more computers or other programmable processing devices to function in a particular manner, such that the instructions stored in the computer-readable memory implement the function(s) specified in the block(s) of the flowchart(s).
  • a computer readable memory e.g., a non-transitory computer readable medium
  • the computer program instructions may also be loaded onto one or more computers or other programmable computing devices to cause a series of operational steps to be performed on the one or more computers or other programmable computing devices to produce a computer-implemented process such that the instructions which execute on the computer or other programmable processing apparatus provide steps for implementing the functions specified in the equation(s), algorithm(s), and/or block(s) of the flowchart(s).
  • the computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, etc.) that communicate and interoperate over a network to perform the described functions.
  • Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium or device.
  • the various functions disclosed herein may be embodied in such program instructions, although some or all of the disclosed functions may alternatively be implemented in application- specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computer system includes multiple computing devices, these devices may, but need not, be co-located.
  • the results of the disclosed methods and tasks may be persistently stored by transforming physical storage devices, such as solid state memory chips and/or magnetic disks, into a different state.

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Abstract

L'invention concerne un système et un procédé permettant d'automatiser une prescription de voxels appropriée dans une région d'intérêt (ROI) définissable de façon unique dans les tissus d'un patient, ceci afin d'effectuer une spectroscopie par résonance magnétique (SRM) dans la ROI. Les dimensions et les coordonnées d'un volume rectiligne tridimensionnel unique (voxel) dans une région d'intérêt unique sont identifiées automatiquement. Dans certains modes de réalisation cela consiste à : (1) appliquer des zones de recherches de ROI identifiées statistiquement dans un champ de vue (FOV) ; (2) traiter une image IRM de manière à lisser l'arrière-plan et à faire ressortir une structure particulière utile pour définir la ROI ; (3) identifier une population de pixels définissant la structure particulière ; (4) effectuer une analyse statistique de la population de pixels afin d'adapter un modèle 2D, comme une ellipsoïde, à la population et d'adapter ensuite une forme rectiligne dans le modèle ; (5) répéter les phases (1) à (4) en utilisant de multiples images qui incluent la ROI 3D afin de créer une forme rectiligne 3D ; (6) répéter les phases (1) à (5) pour de multiples ROI avec un FOV commun. Une interface manuelle peut également être utilisée afin d'assurer une prise de commande afin de passer à une prescription manuelle, d'aider à l'identification de structures (comme en cliquant sur les niveaux de disques), ou de modifier le voxel automatisé (par exemple modifier l'emplacement, la forme ou une ou plusieurs dimensions).
EP11842542.0A 2010-11-24 2011-11-23 Systèmes et procédés de voxelation automatisée de régions d'intérêt pour spectroscopie par résonance magnétique Withdrawn EP2642918A4 (fr)

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