Modifications of pancreatic diffusion MRI by tissue characteristics: what are we weighting for?

Nissan, Noam

doi:10.1002/nbm.3728

Cited by 13 publications

(13 citation statements)

References 85 publications

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“…A 3D T 1 -weighted MPRAGE (magnetization prepared rapid acquisition gradient echo) sequence (T R /T E = 2300 ms/2.32 ms; echo spacing time = 7 ms; flip angle = 8°; T I = 900 ms; parallel imaging using generalized autocalibrating partial parallel acquisition (GRAPPA) acceleration factor 2; voxel size = 1 × 1 × 1 mm 3 ; FOV = 300 × 240 mm 2 ) was acquired for anatomical orientation. DW images were acquired using a fat-saturated EPI (echo-planar imaging) sequence with twice refocused diffusion preparation, as reported in Reference 24 (T R = 4100 ms; T E = 90 ms; echo spacing = 0.68 ms; bandwidth in EPI readout direction = 1685 Hz/px; GRAPPA acceleration factor = 2; partial Fourier = 7/8; FOV = 228 × 228 mm 2 ; voxel size = 2 × 2 × 2 mm 3 ; number of slices = 24; b-values 0, 5,10,20,35,55,80,110,150,200, 300, 500, 750, 1000, 1200 s/mm 2 ; 20 diffusion-encoding directions). The use of a bipolar diffusion preparation mitigated or prevented image distortions due to eddy current effects.…”

Section: Mrimentioning

confidence: 99%

“…15,16 The thermally driven mobility of water molecules as well as the flow of water molecules into randomly distributed capillaries and tubular structures result in an intra-voxel dephasing of the transverse magnetization, which contributes to the signal loss. [17][18][19] Besides providing measures of diffusion anisotropy and of the total degree of diffusion in the voxel, the sampling of the DWI signal within an optimal range of b-values and along at least six non-collinear diffusion-encoding directions within one single scan potentially allows tissue differentiation in terms of cellularity, [20][21][22] microstructure, 23,24 and perfusion. 25 The intrinsic T 2 weight of the low b-value volumes offers an additional tissue-specific element for classification.…”

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confidence: 99%

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Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

2018

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The diffusion-weighted (DW) MR signal sampled over a wide range of b-values potentially allows for tissue differentiation in terms of cellularity, microstructure, perfusion, and T relaxivity. This study aimed to implement a machine learning algorithm for automatic brain tissue segmentation from DW-MRI datasets, and to determine the optimal sub-set of features for accurate segmentation. DWI was performed at 3 T in eight healthy volunteers using 15 b-values and 20 diffusion-encoding directions. The pixel-wise signal attenuation, as well as the trace and fractional anisotropy (FA) of the diffusion tensor, were used as features to train a support vector machine classifier for gray matter, white matter, and cerebrospinal fluid classes. The datasets of two volunteers were used for validation. For each subject, tissue classification was also performed on 3D T -weighted data sets with a probabilistic framework. Confusion matrices were generated for quantitative assessment of image classification accuracy in comparison with the reference method. DWI-based tissue segmentation resulted in an accuracy of 82.1% on the validation dataset and of 82.2% on the training dataset, excluding relevant model over-fitting. A mean Dice coefficient (DSC) of 0.79 ± 0.08 was found. About 50% of the classification performance was attributable to five features (i.e. signal measured at b-values of 5/10/500/1200 s/mm and the FA). This reduced set of features led to almost identical performances for the validation (82.2%) and the training (81.4%) datasets (DSC = 0.79 ± 0.08). Machine learning techniques applied to DWI data allow for accurate brain tissue segmentation based on both morphological and functional information.

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Section: Mrimentioning

confidence: 99%

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confidence: 99%

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

2018

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“…5,14 The main MR-based procedure clinically recommended for the (relatively late-stage) diagnosis of PDAC remains magnetic resonance cholangio pancreatography (MRCP), a long-echo-time sequence that leaves solely the biliary ducts in the image, and can therefore point towards the presence of tumors if they distort the ducts' normal shapes. 15 A case has also been made for the inclusion of diffusion-weighted imaging (DWI) in these diagnoses 10,16 ; apparently, due to the fibrotic nature of the stroma and the tumor's high cellularity, 17 these will have lower diffusivity and appear bright in DWI data. 16,18,19 This, however, has also been described as not always being the case for a large study of human subjects.…”

Section: Introductionmentioning

confidence: 99%

“…15 A case has also been made for the inclusion of diffusion-weighted imaging (DWI) in these diagnoses 10,16 ; apparently, due to the fibrotic nature of the stroma and the tumor's high cellularity, 17 these will have lower diffusivity and appear bright in DWI data. 16,18,19 This, however, has also been described as not always being the case for a large study of human subjects. 20 Some diffusion tensor imaging (DTI) studies have demonstrated that the tumor's stroma has some preferential orientation.…”

Section: Introductionmentioning

confidence: 99%

Identification of variable stages in murine pancreatic tumors by a multiparametric approach employing hyperpolarized ¹³C MRSI, ¹H diffusivity and ¹H T₁ MRI

et al. 2020

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This study explored the usefulness of multiple quantitative MRI approaches to detect pancreatic ductal adenocarcinomas in two murine models, PAN-02 and KPC. Methods assayed included 1 H T 1 and T 2 measurements, quantitative diffusivity mapping, magnetization transfer (MT) 1 H MRI throughout the abdomen and hyperpolarized 13 C spectroscopic imaging. The progress of the disease was followed as a function of its development; studies were also conducted for wildtype control mice and for mice with induced mild acute pancreatitis. Customized methods developed for scanning the motion-and artifact-prone mice abdomens allowed us to obtain quality 1 H images for these targeted regions. Contrasts between tumors and surrounding tissues, however, were significantly different. Anatomical images, T 2 maps and MT did not yield significant contrast unless tumors were large. By contrast, tumors showed statistically lower diffusivities than their surroundings (≈8.3 ± 0.4 x 10 −4 for PAN-02 and ≈10.2 ± 0.6 x 10 −4 for KPC vs 13 ± 1 x 10 −3 mm 2 s −1 for surroundings), longer T 1 relaxation times (≈1.44 ± 0.05 for PAN-02 and ≈1.45 ± 0.05 for KPC vs 0.95 ± 0.10 seconds for surroundings) and significantly higher lactate/ pyruvate ratios by hyperpolarized 13 C MR (0.53 ± 0.2 for PAN-02 and 0.78 ± 0.2 for KPC vs 0.11 ± 0.04 for control and 0.31 ± 0.04 for pancreatitis-bearing mice). Although the latter could also distinguish early-stage tumors from healthy animal controls, their response was similar to that in our pancreatitis model. Still, this ambiguity could be lifted using the 1 H-based reporters. If confirmed for other kinds of pancreatic tumors this means that these approaches, combined, can provide a route to an early detection of pancreatic cancer.

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“…number of b-values and modelling methods for ADC/IVIM) 105 remain a major barrier to deriving diagnostic quantitative cut-off values that can be applied across different sites and scanners. 106 The lack of standardized protocols is a common barrier for methods such as T 1 mapping (where different methods can yield small but significant differences in quantification) and DCE MRI. Finally, quantitative MR sequences must be time-efficient to enable them to be included within normal anatomical imaging protocols.…”

Section: Chouhan Et Almentioning

confidence: 99%

Quantitative pancreatic MRI: a pathology-based review

Chouhan

Firmin

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et al. 2019

The British Journal of Radiology

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MRI plays an important role in the clinical management of pancreatic disorders and interpretation is reliant on qualitative assessment of anatomy. Conventional sequences capturing pancreatic structure can however be adapted to yield quantitative measures which provide more diagnostic information, with a view to increasing diagnostic accuracy, improving patient stratification, providing robust non-invasive outcome measures for therapeutic trials and ultimately personalizing patient care. In this review, we evaluate the use of established techniques such as secretin-enhanced MR cholangiopancreatography, diffusion-weighted imaging, T 1, T 2* and fat fraction mapping, but also more experimental methods such as MR elastography and arterial spin labelling, and their application to the assessment of diffuse pancreatic disease (including chronic, acute and autoimmune pancreatitis/IgG4 disease, metabolic disease and iron deposition disorders) and cystic/solid focal pancreatic masses. Finally, we explore some of the broader challenges to their implementation and future directions in this promising area.

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Modifications of pancreatic diffusion MRI by tissue characteristics: what are we weighting for?

Cited by 13 publications

References 85 publications

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

Identification of variable stages in murine pancreatic tumors by a multiparametric approach employing hyperpolarized ¹³C MRSI, ¹H diffusivity and ¹H T₁ MRI

Quantitative pancreatic MRI: a pathology-based review

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Modifications of pancreatic diffusion MRI by tissue characteristics: what are we weighting for?

Cited by 13 publications

References 85 publications

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

Automated pixel‐wise brain tissue segmentation of diffusion‐weighted images via machine learning

Identification of variable stages in murine pancreatic tumors by a multiparametric approach employing hyperpolarized 13C MRSI, 1H diffusivity and 1H T1 MRI

Quantitative pancreatic MRI: a pathology-based review

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Identification of variable stages in murine pancreatic tumors by a multiparametric approach employing hyperpolarized ¹³C MRSI, ¹H diffusivity and ¹H T₁ MRI