Classifications

• • 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
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/561—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
  • G01R33/5611—Parallel magnetic resonance imaging, e.g. sensitivity encoding [SENSE], simultaneous acquisition of spatial harmonics [SMASH], unaliasing by Fourier encoding of the overlaps using the temporal dimension [UNFOLD], k-t-broad-use linear acquisition speed-up technique [k-t-BLAST], k-t-SENSE
• • 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
  • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
• • 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
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
  • G01R33/56308—Characterization of motion or flow; Dynamic imaging
  • G01R33/56325—Cine imaging
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F17/10—Complex mathematical operations
  • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2576/00—Medical imaging apparatus involving image processing or analysis
  • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
  • A61B2576/023—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part for the heart
• • 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
  • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
  • G01R33/561—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences
  • G01R33/5619—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by reduction of the scanning time, i.e. fast acquiring systems, e.g. using echo-planar pulse sequences by temporal sharing of data, e.g. keyhole, block regional interpolation scheme for k-Space [BRISK]
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H30/00—ICT specially adapted for the handling or processing of medical images
  • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing

Definitions

• the invention relates to the field of generating MR images (MR: Magnetic Resonance) of a moving object, said object executing motion comprising a plurality of moving phases (movement phases) within a period of time.
• MR Magnetic Resonance
• the invention further relates to a corresponding MRI system for generating MR images of an object, said object executing motion comprising a plurality of moving phases within a period of time.
• CMRI cardiac MRI
• the first one is that CMRI examination requires too much scanner time to be performed and many dummy planning scans before the relevant information is acquired.
• the second one is the high training degree required from technicians to be able to perform cardiac examinations with the right cardiac orientations, making difficult to be spread in any environment.
• Volumetric 3D isotropic acquisition covering the whole chest could help to avoid these two limitations and improving the cardiac examination workflow.
• the method for generating the MR images of the object comprises the steps of:
• the identification is performed by manually selecting the static and dynamic region on the first image.
• a box around the heart could be selected as the dynamic region and thereby the region outside the box will be selected as the static region.
• said moving object executes a motion comprising the plurality of moving phases within the total period of time.
• the data of the provided first and second dataset are previously acquired by data acquisition using an MRI scanner.
• this Fourier transformed image (generated in step (e) and based on the first dataset) is undersampled following the same sampling scheme as the second data set.
• the inventional idea is based on providing (previously acquired) data of the moving object in its more or less static environment to exploit data redundancy and high SENSE acceleration factor to reach isotropic resolution.
• This method uses a new approach to manage the reconstruction problem that 3D acquisition will suppose for the above mentioned PINOT reconstruction.
• PINOT acquisition it can be distinguished two different data sets. In the first dataset it is acquired those k-space lines that will gather the static and dynamic information. In the second dataset a subgroup of k-space lines are acquired for every moving phase (movement phase) of the object based on the idea that it can be recovered some information from the full data set to be able to do the final reconstruction.
• this approach -as a difference with PINOT- it is not necessary to build the whole matrix formulation as described in eq. (1).
• all images or at least the second image is/are generated by use of a SENSE reconstruction.
• SENSE SENSE Encoding
• the array elements are usually surface coils, which exhibit strongly inhomogeneous, mutually distinct spatial sensitivity.
• the underlying principle of the SENSE approach is to regard the influence of coil sensitivity as an encoding effect similar to gradient encoding.
• the sensitivity effect is mathematically largely analogous to gradient encoding.
• a key advantage over the gradient concept is that the sensitivity mechanism permits simultaneous encoding with the multiple distinct sensitivities of the receiver array. Thus, considerable savings in scan time can be achieved by partially replacing sequential gradient switching with parallel sensitivity encoding.
• Sensitivity encoding is based on the fact that receiver sensitivity generally has an encoding effect complementary to Fourier preparation by linear field gradients. Thus, by using multiple receiver coils in parallel scan time in Fourier imaging can be considerably reduced.
• the problem of image reconstruction from sensitivity encoded data is formulated in a general fashion and solved for arbitrary coil configurations and k-space sampling patterns.
• the moving object is a heart; the MR image is a cardiac MR image; and the moving phases of the object are cardiac phases from a cardiac region.
• the moving object in its more or less static environment is the whole chest with a non-angulated coronal volume to exploit data redundancy and high SENSE acceleration factor to reach isotropic resolution in cardiac cine images in a single breath- ho Id.
• This acquisition and reconstruction methodology allows to obtain 3D isotropic cardiac images in a breath-hold duration with a reconstruction time of minutes.
• At least one further image is generated by repeating steps (f) to (i) of the nine steps (a - i) respectively often.
• These other images relate to further moving phases (movement phases) PH2 to PHN.
• the first image is generated by use of a full sampling image and the second image (and all other following images) is/are preferably generated by use of a SENSE reconstruction.
• the data provided in the first dataset and the second dataset are generated by data acquisition using a MRI scanning unit (MRI scanner).
• MRI scanner MRI scanning unit
• ROI region of interest
• rFoV reduced Field of View
• a plurality of receiver coils are used for said data acquisition.
• the edited first image is divided into a plurality of images, each image inverse Fourier transformed in the k-space domain.
• the Fourier transformed images for each coil are under-sampled following the same sampling scheme as the sampling of the second data set.
• each image of the plurality of images is weighted by a coil sensitivity of the corresponding receiver coil out of the receiver coils before performing the inverse Fourier transformation.
• the MRI system for generating MR images of an object is established for performing the following steps:
• ROI of the first image, which reduced region of interest includes the dynamic region.
• the moving object executes a motion comprising the plurality of moving phases within the period of time, wherein this period of time is a total acquisition time.
• the system is established for performing the above mentioned method for generating a MR image of a moving object in its environment, especially a heart in the chest.
• the MRI system comprises (i) a MRI scanning unit for data recording comprising an array of multiple simultaneously operated receiver coils and (ii) a computer based data processing unit for image acquisition of magnetic resonance imaging.
• the steps (a) to (i) are performed by the data processing unit of the MRI system.
• the data acquisition steps leading to the steps (a) and (f) are performed by use of the MRI Scanning unit.
• Various other embodiments of the invention concern to a computer program product to execute the aforementioned method, especially by use of the aforementioned MRI system.
• Fig. 1 shows a graphic illustration of a procedure for displaying of generating cardiac MR images according to a preferred embodiment of the invention.
• heart as the object to be imaged.
• the invention is however applicable to other objects like other organs as well.
• the heart is selected merely as an example.
• Fig. 1 shows a schematic representation of all the reconstruction steps using the static information and SENSE acquisition at the same time.
• This schematic representation represents a procedure for generating cardiac MR (CMR) images 10, 20 of the chest with a non-angulated coronal volume within a ROI and within a total period of time.
• CMR cardiac MR
• the procedure comprises the steps of:
• Step 2 (S2): Generating a first image 10 of a ROI from the first dataset;
• Step 3 (S3): Identifying a dynamic region 12 and a static region 14 inside the first image 10, wherein these regions 12, 14 are predominantly dynamic or static respectively within the total period of time (S3);
• Step 9 Generating a second image 20 of a reduced region of interest (rROI) at least including the dynamic region 12.
• the identification is performed by manually selecting the static and dynamic region on the first image.
• a box around the heart could be selected as the dynamic region and thereby the region outside the box will be selected as the static region.
• the data of the provided first and second dataset are previously acquired by data acquisition.
• the above mentioned total period of time is a total acquisition time.
• Step 1 e.g. a full FoV single acquisition is performed to remove folds over artifacts.
• Step 6 an undersampling acquisition of the ky-kz space is performed for updating the reduced FoV (rFoV) for every cardiac phase.
• full FoV corresponds to the (complete) ROI and the term rFoV corresponds to the reduced ROI.
• Step 5a the inverse Fourier transformation (FFT 1 ) of the edited first image 16 will be undersampled in the same way as the undersampling used when providing (acquiring) the second dataset.
• the procedure comprises the following steps: Step 1 (SI): Acquiring a first dataset of a (first) cardiac phase from a cardiac region;
• Step 3 Identifying a dynamic region 12 and a static region 14 inside the first image 10, wherein these regions 12, 14 are predominantly dynamic or static respectively within the total period of time (S3);
• Step 5a Undersampling the inverse Fourier transformation (FFT-1) of the edited first image 16 using a undersampling strategy and thereby providing an undersampled first dataset 30
• the depicted procedure can distinguish two different data sets.
• the first data set it is acquired those k-space lines that will gather the static and dynamic information.
• the second data set a subgroup of k-space lines are acquired for every cardiac phase (heart phase) base on the idea that it can be recovered some information from the full data set to be able to do the final reconstruction.
• the approach -as a difference with PINOT- it is not necessary to build the whole matrix formulation as is described in Eq. (1).
• the reconstruction is spitted in three different stages.
• This reconstruction stage In this reconstruction stage a full image 10 is generated for a single cardiac phase using the first data set described above (S2). This image 10 can be generated using conventional SENSE reconstruction or full sampling image to improve signal accuracy.
• 2nd Stage In this reconstruction stage in the full reconstructed image the previously defined dynamic region is set to 0 (S4) in order to get the information just from those static regions 14 that remain equal along all cardiac phases.
• the images 16 are weighted by coil sensitivities of each coils and inverse Fourier transformed in the k-space domain (S5).
• the Fourier transformed images for each coil are under-sampled following the same sampling scheme as the second data set described above (indicated by the unlabeled arrow between S5 and S7).
• the generated k-space lines from the static region are subtracted from the updated k-space lines in every cardiac phase (S7).
• 3rd Stage In this reconstruction stage the images generated in the 2nd stage are Fourier transformed into the image space (S8) and reconstructed using conventional SENSE reconstruction but just taken the information from the reduced FoV (S9).

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