Image formation in diffusion MRI: A review of recent technical developments

Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI

PURPOSE

Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion-weighted MRI (DW-MRI), are nonspecific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumor grading and tumor response assessment.

METHODS

DW-MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 months after (n = 25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW-MRI signals were simulated from packings of synthetic cells built with well-defined geometrical and diffusion properties. Patients' ADC was modeled as a weighted sum of selected simulated signals. The weights that best described a patient's ADC were determined through an optimization procedure and used to estimate a set of markers of tumor microstructure: diffusion coefficient (D), volume fraction (vf), and radius (R). Apparent cellularity (ρ_app ) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low- and high-grade meningiomas were assessed with nonparametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low or high risk of an adverse outcome to preliminary evaluate response to treatment.

RESULTS

Significant (P < 0.05) differences in median ADC, D, vf, R, and ρ_app values were found when comparing meningiomas' subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs 0.80 for ADC) and grading (0.75 for R vs 0.67 for ADC). High- and low-risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρ_app was the parameter with highest AUC (0.90) and sensitivity (0.75).

CONCLUSIONS

Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumor microstructure implying important advantages for patient-specific proton therapy workflows.

The value of DTI: achieving high diagnostic performance for brain metastasis

BACKGROUND

The evaluation of brain metastases generally requires post-contrast MRI exam, but some patients have contraindication to contrast medium administration.

PURPOSE

To investigate the value of the MRI diffusion tensor imaging (DTI) for detection of metastatic brain tumor.

MATERIALS AND METHODS

We retrospectively analyzed the MRI data from 23 patients (13 males and 10 females) with brain metastases. The MRI protocol consisted in T1WI, T2WI, post-contrast 3DT1WI and DTI images (b = 1000) sequences. The brain metastatic lesions were counted in each of these sequences. We compared the advantages and limitations of different sequences in the brain metastases detection. The number of metastatic lesions identified on the contrast-enhanced 3DT1WI image is used as the reference. FA values were measured in the intratumoral, adjacent peritumoral and distant peritumoral edema area (PTEA) of brain metastasis, and the differences were statistically analyzed.

RESULTS

DTI can detect more brain metastatic lesions rather than T1WI and T2WI. The number of brain metastases on DTI is similar to post-contrast 3D T1WI. There is no statistical difference in the FA value change between the adjacent and distant PTEA.

CONCLUSION

The DTI original image can be used as an alternative examination for patients with contraindications to contrast-enhanced MRI. It has high sensitivity to intratumoral hemorrhage, which has advantage to detect brain metastatic lesions as compared with T1WI or T2WI images.

RUN-UP: Accelerated multishot diffusion-weighted MRI reconstruction using an unrolled network with U-Net as priors

PURPOSE

To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning.

METHODS

An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation.

RESULTS

The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts.

CONCLUSION

A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.

Whole-body magnetic resonance imaging in inflammatory diseases: Where are we now? Results of an International Survey by the European Society of Musculoskeletal Radiology

PURPOSE

To investigate the current role of WB-MRI for rheumatic inflammatory diseases in clinical practice using a survey addressed to musculoskeletal radiologists.

METHODS

A survey composed of 61 questions, subdivided in three sections, demographics (five questions), application of WB-MRI for inflammatory musculoskeletal diseases in adults and children (28 questions: 7 open and 21 multiple choice for each subgroup) was distributed via the European Society of Musculoskeletal Radiology (ESSR) from July 2 to December 31, 2018 to radiologists working in academic, private, and public workplaces. Comparisons among the different workplaces were performed using the Chi-squared and the Kruskal-Wallis test for nominal and ordinal data, respectively (p < 0.05).

RESULTS

Seventy-two participants out of the 1779 (4%) members of the ESSR with 10.4 ± 7.9 years of experience in musculoskeletal imaging, replied to at least one question. 30.6% and 12.3% of the respondents performed at least 50 WB-MRI examinations per year in adults and children, respectively. The most frequent indications were myositis in adults and chronic recurrent multifocal osteomyelitis (CRMO) in children, the latter mostly in academic centers (p = 0.013). The ESSR Arthrits Subcommitte's protocol was applied by half of the participants and especially radiologists working in private practice used it for adults (p = 0.025). Contrast medium was rarely used for adults particularly by academics (p = 0.04). Diffusion Weighted Imaging was applied for children mostly in private practice (p = 0.01) although, overall, it plays a marginal role. Scoring systems were rarely used. Ongoing research is limited.

CONCLUSION

WB-MRI is not routinely applied for musculoskeletal inflammatory diseases. The most frequent indications are myositis and CRMO.

Comparing the clinical utility of single-shot echo-planar imaging and readout-segmented echo-planar imaging in diffusion-weighted imaging of the liver at 3 tesla

PURPOSE

To compare the clinical utility of single-shot echo-planar imaging (SS-EPI) using different breathing schemes and readout-segmented EPI (RS-EPI) in the repeatability of apparent diffusion coefficient (ADC) measurements, signal-to-noise ratio (SNR) and image quality.

METHODS

In this institutional review board-approved prospective study, hepatic DWIs (b = 50, 300, 600 s/mm²) were performed in 22 volunteers on 3.0 T MRI using SS-EPI with free-breathing diffusion-weighted imaging (FB-DWI), breath-hold (BH-DWI), respiratory-triggered (RT-DWI) and navigator-triggered (NT-DWI), and readout-segmented EPI (RS-DWI). ADC and surrogate SNR (sSNR) were measured in nine anatomic locations in the right lobe, and image quality was assessed on all FB-DWI, BH-DWI, RT-DWI, NT-DWI, and RS-DWI sequences. The sequence with the optimal clinical utility was decided by systematically comparing the ADC repeatability, sSNR and image quality of the above DWIs.

RESULTS

In all the five sequences, NT-DWI had the most reliable intra-observer agreement (intraclass correlation coefficient (ICC): 0.900-0.922; all P > 0.05), and a better interobserver agreement (ICC: 0.853-0.960; all p > 0.05) than RS-DWI (ICC:0.881-0.916; some P < 0.05). NT-DWI had the best ADC repeatability in the nine locations (mean ADC absolute differences: 38.47-56.38 × 10^-6 mm²/s, limits of agreement (LOA): 17.33-22.52 × 10^-6 mm²/s). Also, NT-DWI had the highest sSNR (Reader 1: 50.58 ± 20.11 (Superior), 74.06 ± 28.37 (Central), 80.99 ± 38.11(Inferior)); Reader 2: 48.07 ± 23.92 (Superior), 68.23 ± 32.91 (Central), 76.78 ± 33.07 (Inferior)) in three representative sections except for RS-DWI. Furthermore, NT-DWI had a better image quality than RS-DWI (P < 0.05) and was superior to FB-DWI and BH-DWI in sharpness of the liver (at b = 300 s/mm²) (P < 0.05) CONCLUSION: RS-DWI has the best SNR. However, NT-DWI can provide sufficient SNR, excellent image quality, and the best ADC repeatability on 3.0 T MRI. It is thus the recommended sequence for the clinical application of hepatic DWI.

Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) for brain echo-planar imaging

Multi-shot techniques offer improved resolution and signal-to-noise ratio for diffusion- weighted imaging, but make the acquisition vulnerable to shot-specific phase variations and inter-shot macroscopic motion. Several model-based reconstruction approaches with iterative phase correction have been proposed, but robust macroscopic motion estimation is still challenging. Segmented diffusion imaging with iterative motion-corrected reconstruction (SEDIMENT) uses iteratively refined data-driven shot navigators based on sensitivity encoding to cure phase and rigid in-plane motion artifacts. The iterative scheme is compared in simulations and in vivo with a non-iterative reference algorithm for echo-planar imaging with up to sixfold segmentation. The SEDIMENT framework supports partial Fourier acquisitions and furthermore includes options for data rejection and learning-based modules to improve robustness and convergence.

Comparison of multiplexed sensitivity encoding and single-shot echo-planar imaging for diffusion-weighted imaging of the liver

PURPOSE

To compare multiplexed sensitivity encoding (MUSE) and conventional diffusion-weighted magnetic resonance imaging (cDWI) techniques in liver MRI.

METHODS

Fifty-nine patients who underwent both two-shot echo-planar DWI using MUSE and single-shot echo-planar cDWI at a 3.0-T MRI system were included. Qualitative parameters were independently evaluated by three radiologists, and quantitative parameters were calculated on the basis of region of interest measurements. Receiver operating characteristic curve analysis and McNemar's test were used to compare solid lesion characterization results and lesion detectability, respectively.

RESULTS

All reviewers found less image noise, sharper liver contours, milder susceptibility artifacts, and better lesion conspicuity in MUSE-DWI than in cDWI (reader average mean, 4.1-4.5 vs. 3.5-4.0; p < 0.05). The signal-to-noise ratio (SNR) of the liver was significantly higher in MUSE-DWI than in cDWI (right lobe: mean, 9.39 vs. 8.10, p < 0.001; left lobe: mean, 8.34 vs. 7.19, p < 0.001), while the SNR of the lesion (mean, 23.72 vs. 23.88, p = 0.911) and lesion-to-liver contrast-to-noise ratio (mean, 14.65 vs. 15.41, p = 0.527) were comparable between MUSE-DWI and cDWI. Solid lesion characterization results were comparably accurate between MUSE-DWI and cDWI (reader average area under the receiver operating characteristic curve, 0.985 vs. 0.986, p = 0.480). The detectability of lesions was better in MUSE-DWI than in cDWI (reader consensus, 83.7 % [41/49] vs. 67.3 % [33/49], p = 0.021).

CONCLUSION

MUSE-DWI can provide multi-shot liver DWI with less noise, fewer distortions, improved SNR of the liver, and better lesion detectability.

Diffusion and perfusion MRI of normal, preeclamptic and growth-restricted mice models reveal clear fetoplacental differences

Diffusion-weighted MRI on rodents could be valuable to evaluate pregnancy-related dysfunctions, particularly in knockout models whose biological nature is well understood. Echo Planar Imaging’s sensitivity to motions and to air/water/fat heterogeneities, complicates these studies in the challenging environs of mice abdomens. Recently developed MRI methodologies based on SPatiotemporal ENcoding (SPEN) can overcome these obstacles, and deliver diffusivity maps at ≈150 µm in-plane resolutions. The present study exploits these capabilities to compare the development in wildtype vs vascularly-altered mice. Attention focused on the various placental layers—deciduae, labyrinth, trophoblast, fetal vessels—that the diffusivity maps could resolve. Notable differences were then observed between the placental developments of wildtype vs diseased mice; these differences remained throughout the pregnancies, and were echoed by perfusion studies relying on gadolinium-based dynamic contrast-enhanced MRI. Longitudinal monitoring of diffusivity in the animals throughout the pregnancies also showed differences between the development of the fetal brains in the wildtype and vascularly-altered mice, even if these disparities became progressively smaller as the pregnancies progressed. These results are analyzed on the basis of the known physiology of normal and preeclamptic pregnancies, as well as in terms of the potential that they might open for the early detection of disorders in human pregnancies.

Minimizing the echo time in diffusion imaging using spiral readouts and a head gradient system

PURPOSE

Diffusion weighted imaging (DWI) is commonly limited by low signal-to-noise ratio (SNR) as well as motion artifacts. To address this limitation, a method that allows to maximize the achievable signal yield and increase the resolution in motion robust single-shot DWI is presented.

METHODS

DWI was performed on a 3T scanner equipped with a recently developed gradient insert (gradient strength: 200 mT/m, slew rate: 600 T/m/s). To further shorten the echo time, Stejskal-Tanner diffusion encoding with a single-shot spiral readout was implemented. To allow non-Cartesian image reconstruction using such strong and fast gradients, the characterization of eddy current and concomitant field effects was performed based on field-camera measurements.

RESULTS

An echo time of only 19 ms was achieved for a b-factor of 1000 s/mm² . An in-plane resolution of 0.68 mm was encoded by a single-shot spiral readout of 40.5 ms using 3-fold undersampling. The resulting images did not suffer from off-resonance artifacts and T 2 ∗ blurring that are common to single-shot images acquired with regular gradient systems.

CONCLUSION

Spiral diffusion imaging using a head gradient system, together with an accurate characterization of the encoding process allows for a substantial reduction of the echo time, and improves the achievable resolution in motion-insensitive single-shot DWI.

High-Spatial-Resolution Multishot Multiplexed Sensitivity-encoding Diffusion-weighted Imaging for Improved Quality of Breast Images and Differentiation of Breast Lesions: A Feasibility Study

Multishot multiplexed sensitivity-encoding diffusion-weighted imaging is a feasible and easily implementable routine breast MRI protocol that yields high-quality diffusion-weighted breast images.Purpose: To compare multiplexed sensitivity-encoding (MUSE) diffusion-weighted imaging (DWI) and single-shot DWI for lesion visibility and differentiation of malignant and benign lesions within the breast.Materials and Methods: In this prospective institutional review board-approved study, both MUSE DWI and single-shot DWI sequences were first optimized in breast phantoms and then performed in a group of patients. Thirty women (mean age, 51.1 years ± 10.1 [standard deviation]; age range, 27-70 years) with 37 lesions were included in this study and underwent scanning using both techniques. Visual qualitative analysis of diffusion-weighted images was accomplished by two independent readers; images were assessed for lesion visibility, adequate fat suppression, and the presence of artifacts. Quantitative analysis was performed by calculating apparent diffusion coefficient (ADC) values and image quality parameters (signal-to-noise ratio [SNR] for lesions and fibroglandular tissue; contrast-to-noise ratio) by manually drawing regions of interest within the phantoms and breast tumor tissue. Interreader variability was determined using the Cohen κ coefficient, and quantitative differences between MUSE DWI and single-shot DWI were assessed using the Mann-Whitney U test; significance was defined at P < .05.Results: MUSE DWI yielded significantly improved image quality compared with single-shot DWI in phantoms (SNR, P = .001) and participants (lesion SNR, P = .009; fibroglandular tissue SNR, P = .05; contrast-to-noise ratio, P = .008). MUSE DWI ADC values showed a significant difference between malignant and benign lesions (P < .001). No significant differences were found between MUSE DWI and single-shot DWI in the mean, maximum, and minimum ADC values (P = .96, P = .28, and P = .49, respectively). Visual qualitative analysis resulted in better lesion visibility for MUSE DWI over single-shot DWI (κ = 0.70).Conclusion: MUSE DWI is a promising high-spatial-resolution technique that may enhance breast MRI protocols without the need for contrast material administration in breast screening.Keywords: Breast, MR-Diffusion Weighted Imaging, OncologySupplemental material is available for this article.© RSNA, 2020.

MoDL-MUSSELS: Model-Based Deep Learning for Multishot Sensitivity-Encoded Diffusion MRI

We introduce a model-based deep learning architecture termed MoDL-MUSSELS for the correction of phase errors in multishot diffusion-weighted echo-planar MR images. The proposed algorithm is a generalization of the existing MUSSELS algorithm with similar performance but significantly reduced computational complexity. In this work, we show that an iterative re-weighted least-squares implementation of MUSSELS alternates between a multichannel filter bank and the enforcement of data consistency. The multichannel filter bank projects the data to the signal subspace, thus exploiting the annihilation relations between shots. Due to the high computational complexity of the self-learned filter bank, we propose replacing it with a convolutional neural network (CNN) whose parameters are learned from exemplary data. The proposed CNN is a hybrid model involving a multichannel CNN in the k-space and another CNN in the image space. The k-space CNN exploits the annihilation relations between the shot images, while the image domain network is used to project the data to an image manifold. The experiments show that the proposed scheme can yield reconstructions that are comparable to state-of-the-art methods while offering several orders of magnitude reduction in run-time.

Accelerated Acquisition of High-resolution Diffusion-weighted Imaging of the Brain with a Multi-shot Echo-planar Sequence: Deep-learning-based Denoising

To accelerate high-resolution diffusion-weighted imaging with a multi-shot echo-planar sequence, we propose an approach based on reduced averaging and deep learning. Denoising convolutional neural networks can reduce amplified noise without requiring extensive averaging, enabling shorter scan times and high image quality. The preliminary experimental results demonstrate the superior performance of the proposed denoising method over state-of-the-art methods such as the widely used block-matching and 3D filtering.

High-resolution diffusion MRI studies of development in pregnant mice visualized by novel spatiotemporal encoding schemes

This study introduces an MRI approach to map diffusion of water in vivo with high resolution under challenging conditions; the approach's potential is then used in diffusivity characterizations of embryos and fetoplacental units in pregnant mice, as well as of newborn mice in their initial postnatal period. The method relies on performing self-referenced spatiotemporal encoded MRI acquisitions, which can achieve the motional and susceptibility immunities needed to target challenging regions such as a mouse's abdominal cavity in a single shot. When suitably combined with zooming-in and novel interleaving procedures, these scans can overcome the inhomogeneity and sensitivity challenges arising upon targeting ≈100 μm in-plane resolutions, and thereby enable longitudinal development studies of abdominal organs that have hitherto eluded in vivo diffusion-weighted imaging. This is employed here to follow processes related to embryonic implantation and placentation, including the final stages of mouse gastrulation, the development of white matter in fetal brains, the maturation of fetal spines, and the evolution of the different layers making up mouse hemochorial placentas. The protocol's ability to extract diffusivity information in challenging regions as a function of embryonic mouse development is thus demonstrated, and its usefulness as a tool for visualizing pregnancy-related developmental changes in rodents is discussed.

Locally advanced rectal cancer: 3D diffusion-prepared stimulated-echo turbo spin-echo versus 2D diffusion-weighted echo-planar imaging

Background

Diffusion-weighted imaging (DWI) has shown great value in rectal cancer imaging. However, traditional DWI with echo-planar imaging (DW-EPI) often suffers from geometrical distortions. We applied a three-dimensional diffusion-prepared stimulated-echo turbo spin-echo sequence (DPsti-TSE), allowing geometrically undistorted rectal DWI. We compared DPsti-TSE with DW-EPI for locally advanced rectal cancer DWI.

Methods

For 33 prior-to-treatment patients, DWI images of the rectum were acquired with DPsti-TSE and DW-EPI at 3 T using b-values of 200 and 1000 s/mm². Two radiologists conducted a blinded scoring of the images considering nine aspects of image quality and anatomical quality. Tumour apparent diffusion coefficient (ADC) and distortions were compared quantitatively.

Results

DPsti-TSE scored significantly better than DW-EPI in rectum distortion (p = 0.005) and signal pileup (p = 0.001). DPsti-TSE had better tumour Dice similarity coefficient compared to DW-EPI (0.84 versus 0.80, p = 0.010). Tumour ADC values were higher for DPsti-TSE compared to DW-EPI (1.47 versus 0.86 × 10^-3 mm²/s, p < 0.001). Radiologists scored DPsti-TSE significantly lower than DW-EPI on aspects of overall image quality (p = 0.001), sharpness (p < 0.001), quality of fat suppression (p < 0.001), tumour visibility (p = 0.009), tumour conspicuity (p = 0.010) and rectum wall visibility (p = 0.005).

Conclusions

DPsti-TSE provided geometrically less distorted rectal cancer diffusion-weighted images. However, the image quality of DW-EPI over DPsti-TSE was referred on the basis of several image quality criteria. A significant bias in tumour ADC values from DPsti-TSE was present. Further improvements of DPsti-TSE are needed until it can replace DW-EPI.

Improved MUSSELS reconstruction for high-resolution multi-shot diffusion weighted imaging

PURPOSE

MUSSELS is a one-step iterative reconstruction method for multishot diffusion weighted (msDW) imaging. The current work presents an efficient implementation, termed IRLS MUSSELS, that enables faster reconstruction to enhance its utility for high-resolution diffusion MRI studies.

METHODS

The recently proposed MUSSELS reconstruction belongs to a new class of parallel imaging-based methods that recover artifact-free DWIs from msDW data without needing phase compensation. The reconstruction is achieved via structured low-rank matrix completion algorithms, which are computationally demanding due to the large size of the Hankel matrices and their associated computations involving singular value decompositions. Because of this, computational demands of the MUSSELS reconstruction scales as the matrix size and the number of shots increases, which hinders its practical utility for high-resolution applications. In this work, we derive a computationally efficient MUSSELS formulation by modifying the iterative reweighted least squares (IRLS) method that were proposed earlier to solve such problems. Using whole-brain in vivo data, we show the utility of the IRLS MUSSELS for routine high-resolution studies with reduced computational burden.

RESULTS

IRLS MUSSELS provides about five times faster reconstruction for matrix sizes 192 × 192 and 256 × 256 compared to the earlier MUSSELS implementation. The widely employed conjugate symmetry priors can also be incorporated into IRLS MUSSELS to reduce blurring of the partial Fourier acquisitions, without incurring much computational burden.

CONCLUSIONS

The proposed method is observed to be computationally efficient to enable routine high-resolution studies. The computational complexity matches the traditional msDWI reconstruction methods and provides improved reconstruction results with the additional constraints.

Prostate diffusion MRI with minimal echo time using eddy current nulled convex optimized diffusion encoding

BACKGROUND

Prostate diffusion-weighted imaging (DWI) using monopolar encoding is sensitive to eddy-current-induced distortion artifacts. Twice-refocused bipolar encoding suppresses eddy current artifacts, but increases echo time (TE), leading to lower signal-to-noise ratio (SNR). Optimization of the diffusion encoding might improve prostate DWI.

PURPOSE

To evaluate eddy current nulled convex optimized diffusion encoding (ENCODE) for prostate DWI with minimal TE.

STUDY TYPE

Prospective cohort study.

POPULATION

A diffusion phantom, an ex vivo prostate specimen, 10 healthy male subjects (27 ± 3 years old), and five prostate cancer patients (62 ± 7 years old).

FIELD STRENGTH/SEQUENCE

3T; single-shot spin-echo echoplanar DWI.

ASSESSMENT

Eddy-current artifacts, TE, SNR, apparent diffusion coefficient (ADC), and image quality scores from three independent readers were compared between monopolar, bipolar, and ENCODE prostate DWI for standard-resolution (1.6 × 1.6 mm² , partial Fourier factor [pF] = 6/8) and higher-resolution protocols (1.6 × 1.6 mm² , pF = off; 1.0 × 1.0 mm² , pF = 6/8).

STATISTICAL TESTING

SNR and ADC differences between techniques were tested with Kruskal-Wallis and Wilcoxon signed-rank tests (P < 0.05 considered significant).

RESULTS

Eddy current suppression with ENCODE was comparable to bipolar encoding (mean coefficient of variation across three diffusion directions of 9.4% and 9%). For a standard-resolution protocol, ENCODE achieved similar TE as monopolar and reduced TE by 14 msec compared to bipolar, resulting in 27% and 29% higher mean SNR in prostate transition zone (TZ) and peripheral zone (PZ) (P < 0.05) compared to bipolar, respectively. For higher-resolution protocols, ENCODE achieved the shortest TE (67 msec), with 17-21% and 58-70% higher mean SNR compared to monopolar (TE = 77 msec) and bipolar (TE = 102 msec) in PZ and TZ (P < 0.05). No significant differences were found in mean TZ (P = 0.91) and PZ ADC (P = 0.94) between the three techniques. ENCODE achieved similar or higher image quality scores than bipolar DWI in patients, with mean intraclass correlation coefficient of 0.77 for overall quality between three independent readers.

DATA CONCLUSION

ENCODE minimizes TE (improves SNR) and reduces eddy-current distortion for prostate DWI compared to monopolar and bipolar encoding.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1526-1539.

Multi-shot diffusion-weighted MRI reconstruction with magnitude-based spatial-angular locally low-rank regularization (SPA-LLR)

PURPOSE

To resolve the motion-induced phase variations in multi-shot multi-direction diffusion-weighted imaging (DWI) by applying regularization to magnitude images.

THEORY AND METHODS

A nonlinear model was developed to estimate phase and magnitude images separately. A locally low-rank regularization (LLR) term was applied to the magnitude images from all diffusion-encoding directions to exploit the spatial and angular correlation. In vivo experiments with different resolutions and b-values were performed to validate the proposed method.

RESULTS

The proposed method significantly reduces the noise level compared to the conventional reconstruction method and achieves submillimeter (0.8mm and 0.9mm isotropic resolutions) DWI with a b-value of 1,000  s / mm 2 and 1-mm isotropic DWI with a b-value of 2,000  s / mm 2 without modification of the sequence.

CONCLUSIONS

A joint reconstruction method with spatial-angular LLR regularization on magnitude images substantially improves multi-direction DWI reconstruction, simultaneously removes motion-induced phase artifacts, and denoises images.

SMS MUSSELS: A navigator-free reconstruction for simultaneous multi-slice-accelerated multi-shot diffusion weighted imaging

PURPOSE

To introduce a novel reconstruction method for simultaneous multi-slice (SMS)-accelerated multi-shot diffusion weighted imaging (ms-DWI).

METHODS

SMS acceleration using blipped-CAIPI schemes have been proposed to speed up the acquisition of ms-DWIs. The reconstruction of the data requires (a) phase compensation to combine data from different shots and (b) slice unfolding to separate the data of different slices. The traditional approaches first estimate the phase maps corresponding to each shot and slice which are then employed to iteratively recover the slice unfolded DWIs without phase artifacts. In contrast, the proposed reconstruction directly recovers the slice-unfolded k-space data of the multiple shots for each slice in a single-step recovery scheme. The proposed method is enabled by the low-rank property inherent in the k-space samples of ms-DW acquisition. This enabled to formulate a joint recovery scheme that simultaneously (a) unfolds the k-space data of each slice using a SENSE-based scheme and (b) recover the missing k-space samples in each slice of the multi-shot acquisition employing a structured low-rank matrix completion. Additional smoothness regularization is also utilized for higher acceleration factors. The proposed joint recovery is tested on simulated and in vivo data and compared to similar un-navigated methods.

RESULTS

Our experiments show effective slice unfolding and successful recovery of DWIs with minimal phase artifacts using the proposed method. The performance is comparable to existing methods at low acceleration factors and better than existing methods for higher acceleration factors.

CONCLUSIONS

For the slice accelerations considered in this study, the proposed method can successfully recover DWIs from SMS-accelerated ms-DWI acquisitions.

FAConstructor: an interactive tool for geometric modeling of nerve fiber architectures in the brain

Purpose

The technique 3D polarized light imaging (3D-PLI) allows to reconstruct nerve fiber orientations of postmortem brains with ultra-high resolution. To better understand the physical principles behind 3D-PLI and improve the accuracy and reliability of the reconstructed fiber orientations, numerical simulations are employed which use synthetic nerve fiber models as input. As the generation of fiber models can be challenging and very time-consuming, we have developed the open source FAConstructor tool which enables a fast and efficient generation of synthetic fiber models for 3D-PLI simulations.

Methods

The program was developed as an interactive tool, allowing the user to define fiber pathways with interpolation methods or parametric functions and providing visual feedback.

Results

Performance tests showed that most processes scale almost linearly with the amount of fiber points in FAConstructor. Fiber models consisting of < 1.6 million data points retain a frame rate of more than 30 frames per second, which guarantees a stable and fluent workflow. The applicability of FAConstructor was demonstrated on a well-defined fiber model (Fiber Cup phantom) for two different simulation approaches, reproducing effects known from 3D-PLI measurements.

Conclusion

We have implemented a user-friendly and efficient tool that enables an interactive and fast generation of synthetic nerve fiber configurations for 3D-PLI simulations. Already existing fiber models can easily be modified, allowing to simulate many different fiber models in a reasonable amount of time.

Electronic supplementary material

The online version of this article (10.1007/s11548-019-02053-6) contains supplementary material, which is available to authorized users.

The many (sur)faces of B cells

This issue of the Biomedical Journal is dedicated to the latest findings concerning the complex development and functions of B lymphocytes, including their origins during embryogenesis, their meticulous control by the CD22 receptor and different types of T cells, as well as the immunosuppressive abilities of certain B cell subsets. Furthermore, we learn about the complicated genetic background of a rare cardiac disease, the surgical outcomes of pure conus medullaris syndrome and occurrences of tuberculous spondylitis after percutaneous vertebroplasty. Finally, we discover that brain waves could very well be used for biometric authentication and that diffusion imaging displays good reproducibility through a spectrum of spatial resolutions.

Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER)

PURPOSE

Image acceleration provides multiple benefits to diffusion MRI, with in-plane acceleration reducing distortion and slice-wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill-conditioned, particularly when using both in-plane acceleration and simultaneous multislice imaging. In this work, we develop a novel reconstruction method for in vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve.

THEORY AND METHODS

We propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q-space using Gaussian processes, as has previously been exploited in post-reconstruction analysis. We demonstrate in-plane undersampling exceeding the theoretical parallel imaging limits, and simultaneous multislice combined with in-plane undersampling at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper-parameters, with no need for manual tuning.

RESULTS

Simulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences.

CONCLUSION

The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high simultaneous multislice acceleration with in-plane undersampling.

Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition

PURPOSE

To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI.

METHODS

Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction.

RESULTS

We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion.

CONCLUSION

The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

Isotropic resolution diffusion tensor imaging of lumbosacral and sciatic nerves using a phase-corrected diffusion-prepared 3D turbo spin echo

PURPOSE

To perform in vivo isotropic-resolution diffusion tensor imaging (DTI) of lumbosacral and sciatic nerves with a phase-navigated diffusion-prepared (DP) 3D turbo spin echo (TSE) acquisition and modified reconstruction incorporating intershot phase-error correction and to investigate the improvement on image quality and diffusion quantification with the proposed phase correction.

METHODS

Phase-navigated DP 3D TSE included magnitude stabilizers to minimize motion and eddy-current effects on the signal magnitude. Phase navigation of motion-induced phase errors was introduced before readout in 3D TSE. DTI of lower back nerves was performed in vivo using 3D TSE and single-shot echo planar imaging (ss-EPI) in 13 subjects. Diffusion data were phase-corrected per kz plane with respect to T2 -weighted data. The effects of motion-induced phase errors on DTI quantification was assessed for 3D TSE and compared with ss-EPI.

RESULTS

Non-phase-corrected 3D TSE resulted in artifacts in diffusion-weighted images and overestimated DTI parameters in the sciatic nerve (mean diffusivity [MD] = 2.06 ± 0.45). Phase correction of 3D TSE DTI data resulted in reductions in all DTI parameters (MD = 1.73 ± 0.26) of statistical significance (P ≤ 0.001) and in closer agreement with ss-EPI DTI parameters (MD = 1.62 ± 0.21).

CONCLUSION

DP 3D TSE with phase correction allows distortion-free isotropic diffusion imaging of lower back nerves with robustness to motion-induced artifacts and DTI quantification errors. Magn Reson Med 80:609-618, 2018. © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Clinical applications of diffusion weighted imaging in neuroradiology

Abstract

Diffusion-weighted imaging (DWI) has revolutionised stroke imaging since its introduction in the mid-1980s, and it has also become a pillar of current neuroimaging. Diffusion abnormalities represent alterations in the random movement of water molecules in tissues, revealing their microarchitecture, and occur in many neurological conditions. DWI provides useful information, increasing the sensitivity of MRI as a diagnostic tool, narrowing the differential diagnosis, providing prognostic information, aiding in treatment planning and evaluating response to treatment. Recently, there have been several technical improvements in DWI, leading to reduced acquisition time and artefacts and enabling the development of diffusion tensor imaging (DTI) as a tool for assessing white matter. We aim to review the main clinical uses of DWI, focusing on the physiological mechanisms that lead to diffusion abnormalities. Common pitfalls will also be addressed.

Teaching Points

• DWI includes EPI, TSE, RESOLVE or EPI combined with reduced volume excitation.

• DWI is the most sensitive sequence in stroke diagnosis and provides information about prognosis.

• DWI helps in the detection of intramural haematomas (arterial dissection).

• In diffusion imaging, ADC is inversely proportional to tumour cellularity.

• DWI and DTI derived parameters can be used as biomarkers in different pathologies.

Oesophageal squamous cell carcinoma: histogram-derived ADC parameters are not predictive of tumour response to chemoradiotherapy

Objectives

To evaluate correlations between tumour response to definitive chemoradiotherapy (CRT) in oesophageal squamous cell carcinoma (SCC) and histogram-derived apparent diffusion coefficient (ADC) parameters on diffusion-weighted MR images.

Methods

Forty patients with clinical T3–4 oesophageal SCC underwent concurrent CRT. MR examination at 3 T was performed 1–3 days prior to CRT. Readout-segmented echo-planar diffusion imaging was used to acquire ADC maps. Pre- and post-treatment CT examinations were performed. Histogram parameters (mean, 10th, 25th, 50th, 75th, 90th percentiles, skewness and kurtosis) of the ADC values were compared with post-treatment disease status based on RECIST and the tumour regression ratio.

Results

None of the ADC parameters showed significant correlation with post-treatment status (range of Spearman’s ρ values − 0.19 to 0.14, range of p values 0.22–0.47) or tumour regression ratio (range of Spearman’s ρ values − 0.045 to 0.18, range of p values 0.26–0.96). Neither progression-free survival (PFS) (p = 0.17) nor overall survival (OS) (p = 0.15) was significantly different between the two groups corresponding to the lower (< median) and upper arms (≥ median) of the mean ADC values.

Conclusions

Histogram-derived pretreatment ADC parameters were not predictive imaging biomarkers for tumour response to CRT in patients with oesophageal SCC.

Key Points

• Apparent diffusion coefficient (ADC) values are derived from diffusion-weighted MR imaging.

• High-resolution diffusion-weighted images are generated by readout-segmented echo-planar diffusion imaging.

• Readout-segmented echo-planar diffusion-weighted imaging enabled evaluation of ADC parameters.

• Pretreatment ADC parameters do not predict chemoradiotherapy response in patients with oesophageal carcinoma.

Electronic supplementary material

The online version of this article (10.1007/s00330-018-5439-6) contains supplementary material, which is available to authorized users.

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.

On Myelinated Axon Plasticity and Neuronal Circuit Formation and Function

Studies of activity-driven nervous system plasticity have primarily focused on the gray matter. However, MRI-based imaging studies have shown that white matter, primarily composed of myelinated axons, can also be dynamically regulated by activity of the healthy brain. Myelination in the CNS is an ongoing process that starts around birth and continues throughout life. Myelin in the CNS is generated by oligodendrocytes and recent evidence has shown that many aspects of oligodendrocyte development and myelination can be modulated by extrinsic signals including neuronal activity. Because modulation of myelin can, in turn, affect several aspects of conduction, the concept has emerged that activity-regulated myelination represents an important form of nervous system plasticity. Here we review our increasing understanding of how neuronal activity regulates oligodendrocytes and myelinated axons in vivo, with a focus on the timing of relevant processes. We highlight the observations that neuronal activity can rapidly tune axonal diameter, promote re-entry of oligodendrocyte progenitor cells into the cell cycle, or drive their direct differentiation into oligodendrocytes. We suggest that activity-regulated myelin formation and remodeling that significantly change axonal conduction properties are most likely to occur over timescales of days to weeks. Finally, we propose that precise fine-tuning of conduction along already-myelinated axons may also be mediated by alterations to the axon itself. We conclude that future studies need to analyze activity-driven adaptations to both axons and their myelin sheaths to fully understand how myelinated axon plasticity contributes to neuronal circuit formation and function.

A review of GPU-based medical image reconstruction

Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing.