WO2010116124A1 - Imagerie par résonance magnétique nucléaire à diffusion pondérée - Google Patents

Imagerie par résonance magnétique nucléaire à diffusion pondérée Download PDF

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Publication number
WO2010116124A1
WO2010116124A1 PCT/GB2010/000647 GB2010000647W WO2010116124A1 WO 2010116124 A1 WO2010116124 A1 WO 2010116124A1 GB 2010000647 W GB2010000647 W GB 2010000647W WO 2010116124 A1 WO2010116124 A1 WO 2010116124A1
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image
images
interest
value
acquired
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PCT/GB2010/000647
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English (en)
Inventor
Matthew David Blackledge
David John Collins
Martin Osmund Leach
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The Institute Of Cancer Research, Royal Cancer Hospital
Royal Marsden Nhs Foundation Trust
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Application filed by The Institute Of Cancer Research, Royal Cancer Hospital, Royal Marsden Nhs Foundation Trust filed Critical The Institute Of Cancer Research, Royal Cancer Hospital
Publication of WO2010116124A1 publication Critical patent/WO2010116124A1/fr

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    • 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
    • G01R33/56341—Diffusion 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
    • G01R33/4833—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices
    • G01R33/4835—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices of multiple slices
    • 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
    • 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
    • G01R33/5608—Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels

Definitions

  • the present invention relates to nuclear magnetic resonance (NMR) imaging methods and systems, and particularly to NMR diffusion weighted imaging.
  • NMR nuclear magnetic resonance
  • NMR imaging allows anatomical and physiological features of living human and animal bodies to be observed.
  • Typical NMR imaging of a region of interest involves performing a sequence of NMR measurement cycles.
  • the received NMR signals are then processed to reconstruct the magnetic resonance image.
  • signal localisation is obtained by controlling the strength of magnetic fields which have the same direction as the main polarizing field, but which impose gradients along the x, y and z directions.
  • DWI diffusion weighted imaging
  • pulse sequences which contain magnetic field gradients known as diffusion gradients that sensitize the magnetic resonance signal to spin motion.
  • An example pulse sequence contains temporally separated first and second diffusion gradient lobes of equal size.
  • the detected magnetic resonance signal intensity decreases in relation to the speed of water diffusion in a given volume of tissue.
  • the first moment of the diffusion gradient known as the "b-value”
  • the b-value may be adjusted by varying the area of the two lobes of the diffusion magnetic field gradient, or by varying the time interval between them.
  • the magnetic resonance signal intensity, S (b) at the center of the echo using a spin-echo diffusion-weighted pulse sequence can be related to the b-value by the Stejskal-Tanner equation:
  • the equation assumes mono-exponential decay.
  • DWI is especially useful in cancer detection, as tumour tissue has been shown to have low diffusivity relative to other tissues and so appears highly intense relative to background in DWI images. Often it is desirable to obtain images at high b-values. However, while noise levels remain generally constant at different b-values, signal levels attenuate as b- values increase. Thus at high b-values, the low signal-to- noise ratio (SNR) can cause significant deterioration in image quality.
  • SNR signal-to- noise ratio
  • the present invention provides a method for producing a diffusion-weighted magnetic resonance image of a region of interest, the method including the steps of:
  • step (c) calculating, from the map determined at step (b) , an image of the region of interest at a different b-value .
  • the image is calculated at a first b- value which is higher than the b-values used at step (a) .
  • the calculated image typically has a b-value which is significantly higher than the b-values used to acquire the images at step (a) .
  • the calculated image does not suffer from the same drop in SNR associated with images acquired at the same b- value using magnetic resonance imaging apparatuses. This is because while, in a calculated image, the signal attenuates with increasing b-value, the noise attenuates with increasing b-value as well, leading to a higher SNR.
  • each of the acquired images is acquired by- averaging a plurality of images at the respective b-value.
  • step (a) more than two images can be acquired, each at a respective and different fa- value, and in step (b) the ADC map can be determined from the more than two images, e.g. by regression analysis or nonlinear fitting. Both techniques, and particularly the image averaging, can improve the accuracy of the ADC map, and thereby improve the quality of the calculated image.
  • the two acquired images can have any two different b-values. However, preferably, one of the images is acquired with a zero b-value (i.e. a zero diffusion gradient) .
  • a zero b-value image (which is typically a T2 weighted image) generally provides more detailed anatomical information than non-zero b-value images due to signal attenuation at higher b- values . Acquiring a zero b-value also helps to reduce noise in the ADC determination (assuming mono-exponential signal attenuation) .
  • the method may further include a preliminary step of determining the desirable difference between the b-values for the acquisition of the acquired images on the basis of a provided or estimated value of the true diffusion coefficient in a portion of the region of interest.
  • the difference may be determined in order to reduce error in the ADC map determination and thereby improve the visibility of the tumour in the calculated image.
  • the images in step (a) are acquired with the number of signal averages (NSA) on a 1:3 ratio and the difference between the b-values (i.e. ⁇ b) is about 1.25/DQ, where D 0 is the true diffusion coefficient of the tissue being imaged.
  • ⁇ b is preferably about 1.11/Do for an NSA on a 1:1 ratio and is preferably about 1.34/Do for an NSA on a 1:5 ratio
  • the preferred ⁇ b values assume that the noise is Gaussian distributed and independent of b-value, and the effects of T2 decay can be ignored - see Jones, D. K., Horsfield, M. A., Simmons, A.: Optimal Strategies for Measuring Diffusion in Anisotropic Systems by- Magnetic Resonance Imaging, Magn. Reson. Med. 42:515-525 (1999) ) .
  • a second aspect of the invention provides a method of displaying 3D diffusion-weighted magnetic resonance image data, the method including the steps of: performing the method of the first aspect (optionally including any one or any combination of the optional features of the method of the first aspect) for each of a plurality of parallel slices through a volume of interest to obtain for each slice a corresponding calculated image, and displaying the calculated images as a projected volumetric image, e.g. as a maximum intensity projection of the volume of interest .
  • a further aspect of the invention provides a computer system operatively configured to perform the method of the first or second aspect.
  • the system may have one or more optional features corresponding to any one or combination of the optional features of the method of the first or second aspect.
  • a computer system for producing a diffusion- weighted magnetic resonance image of a region of interest may include : memory for storing at least two images of the region of interest acquired at respective and different b-values; and a processor or processors for determining a map of the apparent diffusion coefficient in the region of interest from the acquired images; and calculating, from the map thus- determined, an image of the region of interest at a different b-value .
  • the computer system may include a display for displaying the calculated image and/or (if the system is operatively configured to perform the method of the second aspect) the projected volumetric image.
  • a further aspect of the invention provides a magnetic resonance imaging apparatus having a computer system of the previous aspect.
  • Yet further aspects of the invention provide a computer program for performing the method of the first or second aspect, and a computer program product carrying a computer program for performing the method of the first or second aspect.
  • Figure 1 is a diagram showing an overview of a DWI calculation
  • Figure 2 shows a plot of variance of image noise against fa- value for acquired and calculated DWI images
  • a method of producing cDWI is demonstrated schematically in Figure 1.
  • These are then used to calculate an ADC map for the region.
  • a model e.g. the Stejskal- Tanner equation
  • the noise in the ADC maps is reduced such that there is minimal propagation of noise through to the cDWI images.
  • D 0 is the true diffusion coeffcient of the tissue being imaged.
  • optimum imaging parameters may not be achievable throughout the entire slice as diffusion coeffcients are generally inhomogeneous within tumours and vary between different tissues. Nonetheless, optimisation can be performed for a tissue of interest (e.g. a cancer) .
  • field of view 300 x 300 mm 2
  • slice thickness (ST) 5.0 mm
  • repetition time (TR) 1100 ms
  • echo time (TE) 228 ms
  • bandwidth in the read direction (BW) 1812 Hz/px.
  • the image noise of the calculated images (points marked "x") varied across the range of b-values, being larger than that for the acquired images when b ⁇ 700 s/mm 2 and smaller when b > 700 s/mm 2 . This reduction in image noise for the calculated images allows high SNRs to be achieved.
  • the diffusion coefficient in the tissue of interest is homogeneous (D 0 ) .
  • Image noise follows a Gaussian distribution such that the propagation of errors formula may be applied: 4. Image noise is independent of b-value.
  • the patient was scanned axially using b-values of 50, 100, 250 and 750 s/mm 2 applied in 3 orthogonal directions for ADG calculations and then again with a single b-value of 1400 s/mm 2 .
  • the average SNR of the calculated and acquired images was determined for 3 foci over 3 contiguous slices by dividing the mean signal within regions of interest (ROIs) around the lesions by the standard deviation of background noise within an ROI drawn just adjacent to the lesions in a region where no obvious signal was observed.
  • ROIs regions of interest
  • One standard deviation in the pixel values within the lesion ROIs was taken to be the uncertainty in measurement.
  • the three lower right arrows in the top left hand image indicate positions of the lymphoma deposits and the upper left arrow in the top left hand image indicates the position of the region where no obvious signal was observed.
  • contrast used in this context was the Michelson contrast defined as (S tU mour - S pros tate) /Stumour-
  • the acquired image it is very difficult to distinguish the diseased tissue (arrowed) from healthy tissue.
  • the calculated image the diseased node stands out much more clearly from the background tissue due to the higher contrast between these tissues.
  • the Michelson contrast between the mean signal within the lesion and the mean signal within the peripheral zone was -0.10 and 0.54 for the acquired and calculated images respectively.
  • the b-value of the computed MIP is varied until the image contrast is adequate to see any lesions.
  • the new image can take approximately 1 second to compute for each change in b-value .
  • the entire patient volume can then be segmented according to a threshold which is set e.g. to within two standard deviations of the mean calculated for the grid.
  • Figure 5 shows that, relative to the conventionally acquired images, the calculated higher b-value image has a reduced signal from the background tissue and hence provides improved visual delineation of the metastases.
  • thresholding can additionally increase the contrast between metastases and background tissue. For example, in Figure 5(b) much of the soft tissue anatomy has been removed such as the spleen, kidneys and spinal cord, making tumour visualisation easier.
  • noise is reduced at high b-values in cDWI, thus improving the SNR.
  • the freedom to freely generate images at any b-value can improve the contrast observed between diseased and healthy tissues.
  • Artefacts such as eddy- current distortions, ghosting and susceptibility induced geometric distortions typically plague conventionally acquired high b-value images.
  • the effects of these artefacts are usually amplified at higher b-values (see LeBihan, D. , Poupon, C. Amadon, A. Lethimonnier, F.: Artifacts and Pitfalls in Diffusion MRI, J. Magn. Reson. Imaging 24:478-488 (2006)) .
  • the ADC calculations are performed using images acquired at lower b-values and preferably with minimal image distortions, the use of cDWI should improve image integrity.
  • any diffusion model such as bi-exponential decay (which allows intra-voxel incoherent motion to be modelled)
  • cDWI e.g. by appropriate modification of the Stejskal-Tanner equation
  • a bi-exponential decay model would require acquired images at four or more different b-values in order to determine the ADC map.
  • cDWI is not limited to cancer detection or to the visualisation of the specific regions imaged in the examples.
  • cDWI is not limited to any specific acquisition protocol or geometry, and can be applied to single slices, multiple slices, non-contiguous slices as well as full 3D acquisitions with any combination of sampling. Accordingly, the exemplary embodiments of the invention set forth above are considered to be illustrative and not limiting. Various changes to the described embodiments may be made without departing from the spirit and scope of the invention.

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  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Vascular Medicine (AREA)
  • General Health & Medical Sciences (AREA)
  • Radiology & Medical Imaging (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Condensed Matter Physics & Semiconductors (AREA)
  • General Physics & Mathematics (AREA)
  • Magnetic Resonance Imaging Apparatus (AREA)

Abstract

L'invention porte sur un procédé de production d'une image d'une région d'intérêt par résonance magnétique à diffusion pondérée. Le procédé comprend les étapes de : (a) acquisition d'au moins deux images de la région d'intérêt, pour des valeurs b respectives et différentes, (b) détermination d'une carte du coefficient de diffusion apparente dans la région d'intérêt à partir des images acquises et (c) calcul, à partir de la carte déterminée à l'étape (b), d'une image de la région d'intérêt correspondant à une valeur b.
PCT/GB2010/000647 2009-04-08 2010-03-31 Imagerie par résonance magnétique nucléaire à diffusion pondérée WO2010116124A1 (fr)

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Cited By (13)

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US9053534B2 (en) 2011-11-23 2015-06-09 The Regents Of The University Of Michigan Voxel-based approach for disease detection and evolution
US9289140B2 (en) 2008-02-29 2016-03-22 The Regents Of The University Of Michigan Systems and methods for imaging changes in tissue
WO2017097656A1 (fr) * 2015-12-09 2017-06-15 Koninklijke Philips N.V. Procédé d'imagerie par résonance magnétique à diffusion permettant de générer une image par diffusion synthétique à une valeur b élevée
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WO2018210233A1 (fr) * 2017-05-15 2018-11-22 The Chinese University Of Hong Kong Détection quantitative tridimensionnelle par irm du mouvement incohérent intra-voxel d'une anomalie tissulaire avec traitement de données amélioré
CN110231255A (zh) * 2019-06-18 2019-09-13 东南大学 可控制温度场的用于测试土体扩散系数的3d打印装置
US10650512B2 (en) 2016-06-14 2020-05-12 The Regents Of The University Of Michigan Systems and methods for topographical characterization of medical image data
CN114983389A (zh) * 2022-06-15 2022-09-02 浙江大学 基于磁共振扩散张量成像的人脑轴突密度定量评估方法

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Cited By (22)

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Publication number Priority date Publication date Assignee Title
US8768431B2 (en) 2007-04-13 2014-07-01 The Regents Of The University Of Michigan Systems and methods for tissue imaging
US9289140B2 (en) 2008-02-29 2016-03-22 The Regents Of The University Of Michigan Systems and methods for imaging changes in tissue
US12020432B2 (en) 2011-06-29 2024-06-25 The Regents Of The University Of Michigan Tissue phasic classification mapping system and method
US9773311B2 (en) 2011-06-29 2017-09-26 The Regents Of The University Of Michigan Tissue phasic classification mapping system and method
US8918160B2 (en) 2011-07-07 2014-12-23 Alan Penn Computer aided diagnostic method and device
US9053534B2 (en) 2011-11-23 2015-06-09 The Regents Of The University Of Michigan Voxel-based approach for disease detection and evolution
EP2844142A4 (fr) * 2012-05-03 2016-05-11 Alan Penn & Associates Inc Procédé et dispositif de diagnostic assisté par ordinateur
WO2013165454A1 (fr) * 2012-05-03 2013-11-07 Alan Penn & Associates, Inc. Procédé et dispositif de diagnostic assisté par ordinateur
US9851426B2 (en) 2012-05-04 2017-12-26 The Regents Of The University Of Michigan Error analysis and correction of MRI ADC measurements for gradient nonlinearity
WO2013166416A1 (fr) * 2012-05-04 2013-11-07 The Regents Of The University Of Michigan Corrections de mesures de diffusivité moyenne pour une non-linéarité de gradient
DE102013224264B4 (de) 2013-11-27 2018-12-27 Siemens Healthcare Gmbh Verfahren zur Verarbeitung von Magnetresonanz-Diffusionsbilddaten
DE102013224264A1 (de) * 2013-11-27 2015-05-28 Siemens Aktiengesellschaft Verfahren zur Verarbeitung von Magnetresonanz-Diffusionsbilddaten
US9443315B2 (en) 2013-11-27 2016-09-13 Siemens Aktiengesellschaft Method and system to process magnetic resonance diffusion image data
CN108885246A (zh) * 2015-12-09 2018-11-23 皇家飞利浦有限公司 用于生成高b值处的合成扩散图像的扩散MRI方法
WO2017097656A1 (fr) * 2015-12-09 2017-06-15 Koninklijke Philips N.V. Procédé d'imagerie par résonance magnétique à diffusion permettant de générer une image par diffusion synthétique à une valeur b élevée
US10698062B2 (en) 2015-12-09 2020-06-30 Koninklijke Philips N.V. Diffusion MRI method for generating a synthetic diffusion image at a high B-value
CN108885246B (zh) * 2015-12-09 2021-04-09 皇家飞利浦有限公司 用于生成高b值处的合成扩散图像的扩散MRI方法
US10650512B2 (en) 2016-06-14 2020-05-12 The Regents Of The University Of Michigan Systems and methods for topographical characterization of medical image data
WO2018210233A1 (fr) * 2017-05-15 2018-11-22 The Chinese University Of Hong Kong Détection quantitative tridimensionnelle par irm du mouvement incohérent intra-voxel d'une anomalie tissulaire avec traitement de données amélioré
CN110231255A (zh) * 2019-06-18 2019-09-13 东南大学 可控制温度场的用于测试土体扩散系数的3d打印装置
CN114983389A (zh) * 2022-06-15 2022-09-02 浙江大学 基于磁共振扩散张量成像的人脑轴突密度定量评估方法
CN114983389B (zh) * 2022-06-15 2023-01-10 浙江大学 基于磁共振扩散张量成像的人脑轴突密度定量评估方法

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