Artifacts and pitfalls in diffusion MRI

Self-supervised learning for denoising of multidimensional MRI data

PURPOSE

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image.

THEORY AND METHODS

Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability.

RESULTS

The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images.

CONCLUSION

The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

Characterizing patterns of diffusion tensor imaging variance in aging brains

Purpose

As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here we characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions.

Approach

We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session.

Results

Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related ( p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively ( p ≪ 0.001) in the caudate nucleus. Males show significantly ( p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated ( p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δ μ = 0.045 millimeters per volume).

Conclusions

The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.

Comparison of T1-weighted landmark placement and ROI transfer onto diffusion-weighted EPI sequences for targeted tractography tasks in the optic nerve

Diffusion-based tractography in the optic nerve requires sampling strategies assisted by anatomical landmark information (regions of interest [ROIs]). We aimed to investigate the feasibility of expert-placed, high-resolution T1-weighted ROI-data transfer onto lower spatial resolution diffusion-weighted images. Slab volumes from 20 volunteers were acquired and preprocessed including distortion bias correction and artifact reduction. Constrained spherical deconvolution was used to generate a directional diffusion information grid (fibre orientation distribution-model [FOD]). Three neuroradiologists marked landmarks on both diffusion imaging variants and structural datasets. Structural ROI information (volumetric interpolated breath-hold sequence [VIBE]) was respectively registered (linear with 6/12 degrees of freedom [DOF]) onto single-shot EPI (ss-EPI) and readout-segmented EPI (rs-EPI) volumes, respectively. All eight ROI/FOD-combinations were compared in a targeted tractography task of the optic nerve pathway. Inter-rater reliability for placed ROIs among experts was highest in VIBE images (lower confidence interval 0.84 to 0.97, mean 0.91) and lower in both ss-EPI (0.61 to 0.95, mean 0.79) and rs-EPI (0.59 to 0.86, mean 0.70). Tractography success rate based on streamline selection performance was highest in VIBE-drawn ROIs registered (6-DOF) onto rs-EPI FOD (70.0% over 5%-threshold, capped to failed ratio 39/16) followed by both 12-DOF-registered (67.5%; 41/16) and nonregistered VIBE (67.5%; 40/23). On ss-EPI FOD, VIBE-ROI-datasets obtained fewer streamlines overall with each at 55.0% above 5%-threshold and with lower capped to failed ratio (6-DOF: 35/36; 12-DOF: 34/34, nonregistered 33/36). The combination of VIBE-placed ROIs (highest inter-rater reliability) with 6-DOF registration onto rs-EPI targets (best streamline selection performance) is most suitable for white matter template generation required in group studies.

Reverse Encoding Distortion Correction for Clinical Head Echo-Planar Diffusion-Weighted MRI: Initial Experience

OBJECTIVE

This study aimed to evaluate the feasibility of the recently commercialized reverse encoding distortion correction (RDC) method for echo-planar imaging (EPI) diffusion-weighted imaging (DWI) by applying clinical head MRI.

METHODS

This study included 50 consecutive patients who underwent head MRI, including single-shot (SS) EPI DWI and RDC-EPI DWI. For evaluation of normal structures, qualitative scores for image distortion, Dice similarity coefficient (DSC) values, distortion ratios, and mean apparent diffusion coefficient (ADC) values were assessed in the pons, temporal lobe at the skull base, and frontal lobe at the level of the lateral ventricles in 30 patients. To evaluate pathologies, qualitative scores for image distortion were assessed for 25 intracranial and 21 extracranial pathologies identified in 32 patients.

RESULTS

Qualitative scores for image distortion, DSC values, distortion ratios, and mean ADC values of the pons and temporal lobe were significantly different between SS-EPI DWI and RDC-EPI DWI, whereas those of the frontal lobe at the level of the lateral ventricles were not significantly different between the 2 DWIs. The qualitative scores for image distortion and mean ADC values of extracranial pathologies were significantly different between the DWIs, whereas those of intracranial pathologies were not significantly different.

CONCLUSIONS

RDC-EPI DWI significantly reduced image distortion and showed higher mean ADC values of the brain parenchyma in the skull base and extracranial pathologies.

Advancements in MR hardware systems and magnetic field control: B0 shimming, RF coils, and gradient techniques for enhancing magnetic resonance imaging and spectroscopy

High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.

Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences

Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent technologies in model self-configuration have promised greater performance and generalizability through automated optimization. We assessed the utility of DWI, ADC, and FLAIR sequences on ischemic stroke segmentation, compared self-configuring nnU-Net models to conventional U-Net models without manual optimization, and evaluated the generalizability of results on an external clinical dataset. 3D self-configuring nnU-Net models and standard 3D U-Net models with MONAI were trained on 200 infarcts using DWI, ADC, and FLAIR sequences separately and in all combinations. Segmentation results were compared between models using paired t-test comparison on a hold-out test set of 50 cases. The highest performing model was externally validated on a clinical dataset of 50 MRIs. nnU-Net with DWI sequences attained a Dice score of 0.810 ± 0.155. There was no statistically significant difference when DWI sequences were supplemented with ADC and FLAIR images (Dice score of 0.813 ± 0.150; p = 0.15). nnU-Net models significantly outperformed standard U-Net models for all sequence combinations (p < 0.001). On the external dataset, Dice scores measured 0.704 ± 0.199 for positive cases with false positives with intracranial hemorrhage. Highly optimized neural networks such as nnU-Net provide excellent stroke segmentation even when only provided DWI images, without significant improvement from other sequences. This differs from-and significantly outperforms-standard U-Net architectures. Results translated well to the external clinical environment and provide the groundwork for optimized acute stroke segmentation on MRI.

Self-supervised multicontrast super-resolution for diffusion-weighted prostate MRI

PURPOSE

This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions.

METHODS

We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans.

RESULTS

Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p< $$ < $$ 0.005). In blind qualitative experiments,96 . 1 % $$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images.

CONCLUSION

High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.

Image quality of DWI at breast MRI depends on the amount of fibroglandular tissue: implications for unenhanced screening

OBJECTIVES

To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density.

METHODS

Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A-D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D).

RESULTS

Image quality of DWI was higher in the case of increased breast density, with good scores (score 3-5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3-5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively).

CONCLUSIONS

Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts.

CLINICAL RELEVANCE STATEMENT

Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts.

KEY POINTS

• Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts.

Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA

Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets.

Unveiling the axonal connectivity between the precuneus and temporal pole: Structural evidence from the cingulum pathways

Neuroimaging studies have consistently demonstrated concurrent activation of the human precuneus and temporal pole (TP), both during resting-state conditions and various higher-order cognitive functions. However, the precise underlying structural connectivity between these brain regions remains uncertain despite significant advancements in neuroscience research. In this study, we investigated the connectivity of the precuneus and TP by employing parcellation-based fiber micro-dissections in human brains and fiber tractography techniques in a sample of 1065 human subjects and a sample of 41 rhesus macaques. Our results demonstrate the connectivity between the posterior precuneus area POS2 and the areas 35, 36, and TG of the TP via the fifth subcomponent of the cingulum (CB-V) also known as parahippocampal cingulum. This finding contributes to our understanding of the connections within the posteromedial cortices, facilitating a more comprehensive integration of anatomy and function in both normal and pathological brain processes. PRACTITIONER POINTS: Our investigation delves into the intricate architecture and connectivity patterns of subregions within the precuneus and temporal pole, filling a crucial gap in our knowledge. We revealed a direct axonal connection between the posterior precuneus (POS2) and specific areas (35, 35, and TG) of the temporal pole. The direct connections are part of the CB-V pathway and exhibit a significant association with the cingulum, SRF, forceps major, and ILF. Population-based human tractography and rhesus macaque fiber tractography showed consistent results that support micro-dissection outcomes.

Advances in DTI studies for diagnoses and treatment of obsessive-compulsive disorder

This review summarizes the current state of neuroimaging research on obsessive-compulsive disorder (OCD) using diffusion tensor imaging (DTI), which allows for the examination of white matter abnormalities in the brain. DTI studies on individuals with obsessive-compulsive disorder (OCD) consistently demonstrate widespread reductions in white matter integrity in various regions of the brain, including the corpus callosum, anterior and posterior cingulate cortex, and prefrontal cortex, which are involved in emotion regulation, decision-making, and cognitive control. However, the reviewed studies often have small sample sizes, and findings vary between studies, highlighting the need for larger and more standardized studies. Furthermore, discerning between causal and consequential effects of OCD on white matter integrity poses a challenge. Addressing this issue may be facilitated through longitudinal studies, including those evaluating the impact of treatment interventions, to enhance the accuracy of DTI data acquisition and processing, thereby improving the validity and comparability of study outcomes. In summary, DTI studies provide valuable insights into the neural circuits and connectivity disruptions in OCD, and future studies may benefit from standardized data analysis and larger sample sizes to determine whether structural abnormalities could be potential biomarkers for early identification and treatment of OCD.

High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model

BACKGROUND

7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts.

PURPOSE

To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI.

METHODS

The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0 ± 0.9 dB, SSIM: 0.945 ± 0.010, and MSE: 2.0E-3 ± 0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min.

CONCLUSION

We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.

Segmented RF shield design to minimize eddy currents for low-field Halbach MRI systems

MRI systems have a thin conducting layer placed between the gradient and RF coils, this acts as a shield at the RF-frequency, minimizing noise coupled into the experiment, and decreasing the coupling between the RF and gradient coils. Ideally, this layer should be transparent to the gradient fields to reduce eddy currents. In this work the design of such a shield, specifically for low-field point-of-care Halbach based MRI devices, is discussed. A segmented double layer shield is designed and constructed based on eddy current simulations. Subsequently, the performance of the improved shield is compared to a reference shield by measuring the eddy current decay times as well as using noise measurements. A maximum reduction factor of 2.9 in the eddy current decay time is observed. The segmented shield couples in an equivalent amount of noise when compared to the unsegmented reference shield. Turbo spin echo images of a phantom and the brain of a healthy volunteer show improvements in terms of blurring using the segmented shield.

Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences

PURPOSE

To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

METHOD

This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm2), a calculated ADC map, and a calculated b1400 sequence. Two accelerated MRI protocols were simulated, using only two acquired b-values to calculate the ADC and b1400. Deep learning models were trained to detect prostate cancer lesions on accelerated and full protocols. The diagnostic performances of the protocols were compared on the patient-level with the area under the receiver operating characteristic (AUROC), using DeLong's test, and on the lesion-level with the partial area under the free response operating characteristic (pAUFROC), using a permutation test. Validation of the results was performed among expert radiologists.

RESULTS

No significant differences in diagnostic performance were found between the accelerated protocols and the full bpMRI baseline. Omitting b800 reduced 53% DWI scan time, with a performance difference of + 0.01 AUROC (p = 0.20) and -0.03 pAUFROC (p = 0.45). Omitting b400 reduced 32% DWI scan time, with a performance difference of -0.01 AUROC (p = 0.65) and + 0.01 pAUFROC (p = 0.73). Multiple expert radiologists underlined the findings.

CONCLUSIONS

This study shows that deep learning can assess the diagnostic efficacy of MRI sequences by comparing prostate MRI protocols on diagnostic accuracy. Omitting either the b400 or the b800 DWI sequence can optimize the prostate MRI protocol by reducing scan time without compromising diagnostic quality.

Function-guided differences of arcuate fascicle and inferior fronto-occipital fascicle tractography as diagnostic indicators for surgical risk stratification

BACKGROUND

Several patients with language-eloquent gliomas face language deterioration postoperatively. Persistent aphasia is frequently associated with damage to subcortical language pathways. Underlying mechanisms still need to be better understood, complicating preoperative risk assessment. This study compared qualitative and quantitative functionally relevant subcortical differences pre- and directly postoperatively in glioma patients with and without aphasia.

METHODS

Language-relevant cortical sites were defined using navigated transcranial magnetic stimulation (nTMS) language mapping in 74 patients between 07/2016 and 07/2019. Post-hoc nTMS-based diffusion tensor imaging tractography was used to compare a tract's pre- and postoperative visualization, volume and fractional anisotropy (FA), and the preoperative distance between tract and lesion and postoperative overlap with the resection cavity between the following groups: no aphasia (NoA), tumor- or previous resection induced aphasia persistent pre- and postoperatively (TIA_P), and surgery-induced transient or permanent aphasia (SIA_T or SIA_P).

RESULTS

Patients with NoA, TIA_P, SIA_T, and SIA_P showed distinct fasciculus arcuatus (AF) and inferior-fronto-occipital fasciculus (IFOF) properties. The AF was more frequently reconstructable, and the FA of IFOF was higher in NoA than TIA_P cases (all p ≤ 0.03). Simultaneously, SIA_T cases showed higher IFOF fractional anisotropy than TIA_P cases (p < 0.001) and the most considerable AF volume loss overall. While not statistically significant, the four SIA_P cases showed complete loss of ventral language streams postoperatively, the highest resection-cavity-AF-overlap, and the shortest AF to tumor distance.

CONCLUSION

Functionally relevant qualitative and quantitative differences in AF and IFOF provide a pre- and postoperative pathophysiological and clinically relevant diagnostic indicator that supports surgical risk stratification.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Mean ADC values and arterial phase hyperintensity discriminate small (≤ 3 cm) well-differentiated hepatocellular carcinoma from dysplastic nodule

BACKGROUND/AIM

This research endeavor sought to distinguish small (≤ 3 cm) well-differentiated hepatocellular carcinoma (WD-HCC) from dysplastic nodules (DN) by employing traditional imaging features and mean apparent diffusion coefficient (mADC) values derived from diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

In this retrospective analysis, we assessed a cohort of ninety patients with confirmed dysplastic nodules (DNs) (n = 71) or well-differentiated hepatocellular carcinoma (WD-HCC) (n = 41) who had undergone dynamic contrast-enhanced magnetic resonance imaging between March 2018 and June 2021. Multivariable logistic regression analyses were executed to pinpoint characteristics that can effectively differentiate histologic grades. A region-of-interest (ROI) encompassing all lesion voxels was delineated on each slice containing the mass in the ADC map. Subsequently, the whole-lesion mean ADC (mADC) were computed from these delineations. A receiver operating characteristic (ROC) curve was generated to assess the discriminatory efficacy of the mADC values in distinguishing between WD-HCC and DN.

RESULTS

Among the histopathological types from benign to malignant, mADC showed a significant decrease (P < 0.001). The mADCs were effective in distinguishing WD-HCC from DN [AUC, 0.903 (95% CI 0.849-0.958)]. The best cutoffs for the Youden index were 0.0012 mm2/s for mADC, with moderate sensitivity (70.7%) and high specificity (94.4%). MRI features including hyperintensity at arterial phase (odds ratio, 21.2; P = 0.009), mADC < 0.0012 mm2/s (odds ratio, 52.2; P < 0.001) were independent predictors for WD-HCC at multivariable analysis. The AUC value of hyperintensity at arterial phase was 0.857 (95% CI 0.786-0.928). The composite diagnostic criterion of arterial hyperintensity + mADC < 0.0012 mm2/s showed good performance [AUC, 0.926 (95% CI 0.878-0.975)], displaying increased sensitivity compared to individual assessments involving arterial hyperintensity (P = 0.013), mADC < 0.0012 mm2/s (P = 0.004), or LR-5 (P < 0.001), with similar specificity compared to LR-5 (P = 0.193).

CONCLUSION

DN and WD-HCC displayed contrasting diffusion characteristics, attainable to distinguish with satisfactory accuracy. The utilization of arterial phase hyperintensity and mADC < 0.0012 on MRI facilitated the differentiation of WD-HCC from DN.

DTI-MR fingerprinting for rapid high-resolution whole-brain T1 , T2 , proton density, ADC, and fractional anisotropy mapping

PURPOSE

This study aims to develop a high-efficiency and high-resolution 3D imaging approach for simultaneous mapping of multiple key tissue parameters for routine brain imaging, including T1 , T2 , proton density (PD), ADC, and fractional anisotropy (FA). The proposed method is intended for pushing routine clinical brain imaging from weighted imaging to quantitative imaging and can also be particularly useful for diffusion-relaxometry studies, which typically suffer from lengthy acquisition time.

METHODS

To address challenges associated with diffusion weighting, such as shot-to-shot phase variation and low SNR, we integrated several innovative data acquisition and reconstruction techniques. Specifically, we used M1-compensated diffusion gradients, cardiac gating, and navigators to mitigate phase variations caused by cardiac motion. We also introduced a data-driven pre-pulse gradient to cancel out eddy currents induced by diffusion gradients. Additionally, to enhance image quality within a limited acquisition time, we proposed a data-sharing joint reconstruction approach coupled with a corresponding sequence design.

RESULTS

The phantom and in vivo studies indicated that the T1 and T2 values measured by the proposed method are consistent with a conventional MR fingerprinting sequence and the diffusion results (including diffusivity, ADC, and FA) are consistent with the spin-echo EPI DWI sequence.

CONCLUSION

The proposed method can achieve whole-brain T1 , T2 , diffusivity, ADC, and FA maps at 1-mm isotropic resolution within 10 min, providing a powerful tool for investigating the microstructural properties of brain tissue, with potential applications in clinical and research settings.

Direct Inside-Out Observation of Superficial White Matter Fasciculi in the Human Brain

Background: Recent methodological advances in the study of the cerebral white matter have left short association fibers relatively underexplored due to their compact and juxtacortical nature, which represent significant challenges for both post-mortem post-cortex removal dissection and magnetic resonance-based diffusion imaging. Objective: To introduce a novel inside-out post-mortem fiber dissection technique to assess short association fiber anatomy. Methods: Six cerebral specimens were obtained from a body donation program and underwent fixation in formalin. Following two freezing and thawing cycles, a standardized protocol involving peeling fibers from deep structures towards the cortex was developed. Results: The inside-out technique effectively exposed the superficial white matter. The procedure revealed distinguishable intergyral fibers, demonstrating their dissectability and enabling the identification of their orientation. The assessment of layer thickness was possible through direct observation and ex vivo morphological magnetic resonance imaging. Conclusion: The inside-out fiber technique effectively demonstrates intergyral association fibers in the post-mortem human brain. It adds to the neuroscience armamentarium, overcoming methodological obstacles and offering an anatomical substrate essential for neural circuit modeling and the evaluation of neuroimaging congruence. Impact statement The inside-out fiber dissection technique enables a totally new perception of cerebral connectivity as the observer navigates inside the parenchyma and looks toward the cerebral surface with the subcortical white matter and the cortical mantle in place. This approach has proven very effective for exposing intergyral association fibers, which have shown to be much more distinguishable from an inner perspective. It gave rise to unprecedented images of the human superficial white matter and allowed, for the first time, direct observation of this vast mantle of fascicles on entire cerebral hemisphere aspects.

Overlapping stimulation of subthalamic nucleus and dentato-rubro-thalamic tract in Parkinson's disease after deep brain stimulation

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces tremor, rigidity, and akinesia. According to the literature, the dentato-rubro-thalamic tract (DRTt) is verified target for DBS in essential tremor; however, its role in the treatment of Parkinson's disease is only vaguely described. The aim of our study was to identify the relationship between symptom alleviation in PD patients and the distance of the DBS electrode electric field (EF) to the DRTt.

METHODS

A single-center retrospective analysis of patients (N = 30) with idiopathic Parkinson's disease (PD) who underwent DBS between November 2018 and January 2020 was performed. DRTt and STN were visualized using diffusion-weighted imaging (DWI) and tractography protocol of magnetic resonance (MR). The EF was calculated and compared with STN and course of DRTt. Evaluation of patients before and after surgery was performed with use of UPDRS-III scale. The association between distance from EF to DRTt and clinical outcomes was examined. To confirm the anatomical variation between DRTt and STN observed in tractography, white matter dissection was performed with the Klingler technique on ten human brains.

RESULTS

Patients with EF overlapping STN and DRTt benefited from significant motor symptoms improvement. Anatomical findings confirmed the presence of population differences in variability of the DRTt course and were consistent with the DRTt visualized by MR.

CONCLUSIONS

DRTt proximity to STN, the main target in PD DBS surgery, confirmed by DWI with tractography protocol of MR combined with proper predefined stimulation parameters may improve efficacy of DBS-STN.

Development of statistical auto-segmentation method for diffusion restriction gray matter lesions in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease

Quantification of diffusion restriction lesions in sporadic Creutzfeldt-Jakob disease (sCJD) may provide information of the disease burden. We aim to develop an automatic segmentation model for sCJD and to evaluate the volume of disease extent as a prognostic marker for overall survival. Fifty-six patients (mean age ± SD, 61.2 ± 9.9 years) were included from February 2000 to July 2020. A threshold-based segmentation was used to obtain abnormal signal intensity masks. Segmented volumes were compared with the visual grade. The Dice similarity coefficient was calculated to measure the similarity between the automatic vs. manual segmentation. Cox proportional hazards regression analysis was performed to evaluate the volume of disease extent as a prognostic marker. The automatic segmentation showed good correlation with the visual grading. The cortical lesion volumes significantly increased as the visual grade aggravated (extensive: 112.9 ± 73.2; moderate: 45.4 ± 30.4; minimal involvement: 29.6 ± 18.1 mm3) (P < 0.001). The deep gray matter lesion volumes were significantly higher for positive than for negative involvement of the deep gray matter (5.6 ± 4.6 mm3 vs. 1.0 ± 1.3 mm3, P < 0.001). The mean Dice similarity coefficients were 0.90 and 0.94 for cortical and deep gray matter lesions, respectively. However, the volume of disease extent was not associated with worse overall survival (cortical extent: P = 0.07; deep gray matter extent: P = 0.12).

Comparison of HASTE versus EPI-Based DWI for Retinoblastoma and Correlation with Prognostic Histopathologic Parameters

BACKGROUND AND PURPOSE

Non-EPI-based DWI has shown better performance in head and neck pathologies owing to lesser susceptibility artifacts compared with EPI-DWI. However, only sporadic studies have investigated the feasibility of non-EPI-based DWI in retinoblastoma (RB). We qualitatively and quantitively compared EPI-DWI and HASTE-DWI in RB and correlated the tumor ADC values obtained from these 2 techniques with histopathologic markers.

MATERIALS AND METHODS

Twenty-one treatment-naive patients with RB underwent 1.5T orbital MR imaging. EPI-DWI and HASTE-DWI were acquired at 3 b-values (0, 500, and 1000 s/mm2). All patients subsequently underwent surgical enucleation. For qualitative image assessment, scoring of overall image quality, artifacts, tumor sharpness, and tumor conspicuity was done by using a 5-point Likert scale. Quantitative assessment included calculations of SNR, contrast-to-noise ratio (CNR), geometric distortion, and ADC. Qualitative scores were compared by using the Wilcoxon signed-rank test, and quantitative parameters were analyzed with a t test.

RESULTS

All 21 patients had unilateral RB; 15 were male and 6 were female with a median age of 36 months (range, 9-72 months). On histopathology, patients had either poorly differentiated (n = 13/21) or moderately differentiated (n = 8/21) RB. Other poor prognostic markers evaluated were optic nerve invasion (n = 10/21), choroidal invasion (n = 12/21), and anterior eye segment enhancement on MRI (n = 6/21). HASTE-DWI demonstrated higher image quality scores than EPI-DWI (P < .01), except for tumor conspicuity score, which was higher for EPI-DWI (P < .001). HASTE-DWI showed lower SNR, CNR, and geometric distortion than EPI-DWI (P < .001). The average acquisition times of EPI-DWI and HASTE-DWI were ∼1 and 14 minutes, respectively. The mean tumor ADC value on EPI-DWI was 0.62 ± 0.14 × 10-3 mm2/s and on HASTE-DWI was 0.83 ± 0.17 × 10-3 mm2/s. A significant correlation between EPI-DWI and HASTE-DWI ADC values (r = 0.8; P = .01) was found. Lower ADC values were found in tumors with poor prognostic markers, but none reached a statistically significant difference.

CONCLUSIONS

HASTE-DWI shows improved overall image quality; however, it lacks in terms of tumor conspicuity, SNR, CNR, and longer acquisition time compared with EPI-DWI. ADC values derived from HASTE-DWI show no advantage over EPI-DWI in correlation with histopathologic prognostic markers.

Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring

PURPOSE

To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI.

METHODS

A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera.

RESULTS

Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy."

CONCLUSION

Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.

Breast MRI in patients with implantable loop recorder: initial experience

OBJECTIVES

To investigate the feasibility of breast MRI exams and guided biopsies in patients with an implantable loop recorder (ILR) as well as the impact ILRs may have on image interpretation.

MATERIALS AND METHODS

This retrospective study examined breast MRIs of patients with ILR, from April 2008 to September 2022. Radiological reports and electronic medical records were reviewed for demographic characteristics, safety concerns, and imaging findings. MR images were analyzed and compared statistically for artifact quantification on the various pulse sequences.

RESULTS

Overall, 40/82,778 (0.049%) MRIs during the study period included ILR. All MRIs were completed without early termination. No patient-related or device-related adverse events occurred. ILRs were most commonly located in the left lower-inner quadrant (64.6%). The main artifact was a signal intensity (SI) void in a dipole formation in the ILR bed with or without areas of peripheral high SI. Artifacts appeared greatest in the cranio-caudal axis (p < 0.001), followed by the anterior-posterior axis (p < 0.001), and then the right-left axis. High peripheral rim-like SI artifacts appeared on the post-contrast and subtracted T1-weighted images, mimicking suspicious enhancement. Artifacts were most prominent on diffusion-weighted (p < 0.001), followed by T2-weighted and T1-weighted images. In eight patients, suspicious findings were found on MRI, resulting in four additional malignant lesions. Of six patients with left breast cancer, the tumor was completely visible in five cases and partially obscured in one.

CONCLUSION

Breast MRI is feasible and safe among patients with ILR and may provide a significant diagnostic value, albeit with localized, characteristic artifacts.

CLINICAL RELEVANCE STATEMENT

Indicated breast MRI exams and guided biopsies can be safely performed in patients with implantable loop recorder. Nevertheless, radiologists should be aware of associated limitations including limited assessment of the inner left breast and pseudo-enhancement artifacts.

KEY POINTS

• Breast MRI in patients with an implantable loop recorder is an infrequent, feasible, and safe procedure. • Despite limited breast visualization of the implantable loop recorder bed and characteristic artifacts, MRI depicted additional lesions in 8/40 (20%) of cases, half of which were malignant. • Breast MRI in patients with an implantable loop recorder should be performed when indicated, taking into consideration typical associated artifacts.

Imaging Assessment of the Efficacy of Chemotherapy in Primary Malignant Bone Tumors: Recent Advances in Qualitative and Quantitative Magnetic Resonance Imaging and Radiomics

Recent studies have shown that MRI demonstrates promising results for evaluating the chemotherapy efficacy in bone sarcomas. This article reviews current methods for evaluating the efficacy of malignant bone tumors and the application of MRI in this area, and emphasizes the advantages and limitations of each modality. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Correlation of Cognitive Reappraisal and the Microstructural Properties of the Forceps Minor: A Deductive Exploratory Diffusion Tensor Imaging Study

Cognitive reappraisal (CR) is a mechanism for emotion regulation, and the prefrontal cortex (PFC) plays a central role in the regulation of emotions. We tested the hypothesis of an association between CR function and microstructural properties of forceps minor (a commissural bundle within the PFC) in healthy subjects (HS). We analyzed a population of 65 young HS of a public dataset. The diffusion tensor imaging (DTI) sequence of every subject was analyzed to extract the derived shape (diameter and volume) and DTI metrics in terms of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the forceps minor. The CR subscale of the German version of the Emotion Regulation Questionnaire (ERQ) was used for CR assessment. The Shapiro-Wilk test was applied to test the assumption of normality in all these parameters, adopting a statistical threshold at p < 0.05. Whenever appropriate a non-parametric two-tailed partial correlation analysis was applied to test for correlations between the CR ERQ score and the derived shape and DTI metrics, including age and sex as confounders, adopting a statistical threshold at p < 0.05. The non-parametric two-tailed partial correlation analysis revealed a mildly significant correlation with FA (ρ = 0.303; p = 0.016), a weakly significant negative correlation with MD (ρ = - 0.269; p = 0.033), and a mildly significant negative correlation with RD (ρ = - 0.305; p = 0.015). These findings suggest a correlation between DTI microstructural properties of forceps minor and CR.

Simultaneous estimation of SAR, thermal diffusivity, and damping using periodic power modulation for MRgFUS quality assurance

Purpose: A crucial aspect of quality assurance in thermal therapy is periodic demonstration of the heating performance of the device. Existing methods estimate the specific absorption rate (SAR) from the temperature rise after a short power pulse, which yields a biased estimate as thermal diffusion broadens the apparent SAR pattern. To obtain an unbiased estimate, we propose a robust frequency-domain method that simultaneously identifies the SAR as well as the thermal dynamics.Methods: We propose a method consisting of periodic modulation of the FUS power while recording the response with MR thermometry (MRT). This approach enables unbiased measurements of spatial Fourier coefficients that encode the thermal response. These coefficients are substituted in a generic thermal model to simultaneously estimate the SAR, diffusivity, and damping. The method was tested using a cylindrical phantom and a 3 T clinical MR-HIFU system. Three scenarios with varying modulation strategies are chosen to challenge the method. The results are compared to the well-known power pulse technique.Results: The thermal diffusivity is estimated at 0.151 mm2s-1 with a standard deviation of 0.01 mm2s-1 between six experiments. The SAR estimates are consistent between all experiments and show an excellent signal-to-noise ratio (SNR) compared to the well established power pulse method. The frequency-domain method proved to be insensitive to B0-drift and non steady-state initial temperature distributions.Conclusion: The proposed frequency-domain estimation method shows a high SNR and provided reproducible estimates of the SAR and the corresponding thermal diffusivity. The findings suggest that frequency-domain tools can be highly effective at estimating the SAR from (biased) MRT data acquired during periodic power modulation.

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

PURPOSE

Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla.

METHOD

In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant.

RESULTS

A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001).

CONCLUSIONS

DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

Retrospective Motion Artifact Reduction by Spatial Scaling of Liver Diffusion-Weighted Images

Cardiac motion causes unpredictable signal loss in respiratory-triggered diffusion-weighted magnetic resonance imaging (DWI) of the liver, especially inside the left lobe. The left liver lobe may thus be frequently neglected in the clinical evaluation of liver DWI. In this work, a data-driven algorithm that relies on the statistics of the signal in the left liver lobe to mitigate the motion-induced signal loss is presented. The proposed data-driven algorithm utilizes the exclusion of severely corrupted images with subsequent spatially dependent image scaling based on a signal-loss model to correctly combine the multi-average diffusion-weighted images. The signal in the left liver lobe is restored and the liver signal is more homogeneous after applying the proposed algorithm. Furthermore, overestimation of the apparent diffusion coefficient (ADC) in the left liver lobe is reduced. The proposed algorithm can therefore contribute to reduce the motion-induced bias in DWI of the liver and help to increase the diagnostic value of DWI in the left liver lobe.

Combining b2500 diffusion-weighted imaging with BI-RADS improves the specificity of breast MRI

PURPOSE

The purpose of this study was to evaluate the diagnostic performance of visual assessment of diffusion-weighted images (DWI) obtained with a b value of 2500 s/mm2 in addition to a conventional magnetic resonance imaging (MRI) protocol to characterize breast lesions.

MATERIALS AND METHODS

This single-institution retrospective study included participants who underwent clinically indicated breast MRI and breast biopsy from May 2017 to February 2020. The examination included a conventional MRI protocol including DWI obtained with a b value of 50 s/mm2 (b50DWI) and a b value of 800 s/mm2 (b800DWI) and DWI obtained with a b value of 2500 s/mm2 (b2500DWI). Lesions were classified using Breast Imaging Reporting and Data Systems (BI-RADS) categories. Three independent radiologists assessed qualitatively the signal intensity within the breast lesions relative to breast parenchyma on b2500DW and b800DWI and measured the b50-b800-derived apparent diffusion coefficient (ADC) value. The diagnostic performances of BI-RADS, b2500DWI, b800DWI, ADC and of a model combining b2500DWI and BI-RADS were evaluated using receiver operating characteristic (ROC) curves analysis.

RESULTS

A total of 260 patients with 212 malignant and 100 benign breast lesions were included. There were 259 women and one man with a median age of 53 years (Q1, Q3: 48, 66 years). b2500DWI was assessable in 97% of the lesions. Interobserver agreement for b2500DWI was substantial (Fleiss kappa = 0.77). b2500DWI yielded larger area under the ROC curve (AUC, 0.81) than ADC with a 1 × 10-3 mm2/s threshold (AUC, 0.58; P = 0.005) and than b800DWI (AUC, 0.57; P = 0.02). The AUC of the model combining b2500DWI and BI-RADS was 0.84 (95% CI: 0.79-0.88). Adding b2500DWI to BI-RADS resulted in a significant increase in specificity from 25% (95% CI: 17-35) to 73% (95% CI: 63-81) (P < 0.001) with a decrease in sensitivity from 100% (95% CI: 97-100) to 94% (95% CI: 90-97), (P < 0.001).

CONCLUSION

Visual assessment of b2500DWI has substantial interobserver agreement. Visual assessment of b2500DWI offers better diagnostic performance than ADC and b800DWI. Adding visual assessment of b2500DWI to BI-RADS improves the specificity of breast MRI and could avoid unnecessary biopsies.

Comparison of EPI DWI and STEAM DWI in Early Postoperative MRI Controls After Resection of Tumors of the Central Nervous System

PURPOSE

Diffusion-weighted imaging (DWI) is important for differentiating residual tumor and subacute infarctions in early postoperative magnetic resonance imaging (MRI) of central nervous system (CNS) tumors. In cases of pneumocephalus and especially in the presence of intraventricular trapped air, conventional echo-planar imaging (EPI) DWI is distorted by susceptibility artifacts. The performance and robustness of a newly developed DWI sequence using the stimulated echo acquisition mode (STEAM) was evaluated in patients after neurosurgical operations with early postoperative MRI.

METHODS

We compared EPI and STEAM DWI of 43 patients who received 3‑Tesla MRI within 72 h after a neurosurgical operation between 1 October 2019 and 30 September 2021. We analyzed susceptibility artifacts originating from air and blood and whether these artifacts compromised the detection of ischemic changes after surgery. The DWI sequences were (i) visually rated and (ii) volumetrically analyzed.

RESULTS

In 28 of 43 patients, we found severe and diagnostically relevant artifacts in EPI DWI, but none in STEAM DWI. In these cases, in which artifacts were caused by intracranial air, they led to a worse detection of ischemic lesions and thus to a possible failed diagnosis or lack of judgment using EPI DWI. Additionally, volumetric analysis demonstrated a 14% smaller infarct volume detected with EPI DWI. No significant differences in visual rating and volumetric analysis were detected among the patients without severe artifacts.

CONCLUSION

The newly developed version of STEAM DWI with highly undersampled radial encodings is superior to EPI DWI in patients with postoperative pneumocephalus.

Diffusion-weighted Breast MRI at 3 Tesla: Improved Lesion Visibility and Image Quality with a Combination of Water-excitation and Spectral Fat Saturation

RATIONALE AND OBJECTIVES

In breast MRI with diffusion-weighted imaging (DWI), fat suppression is essential for eliminating the dominant lipid signal. This investigation evaluates a combined water-excitation-spectral-fatsat method (WEXfs) versus standard spectral attenuated inversion recovery (SPAIR) in high-resolution 3-Tesla breast MRI.

MATERIALS AND METHODS

Multiparametric breast MRI with 2 echo-planar DWI sequences was performed in 83 patients (50.1 ± 12.6 years) employing either WEXfs or SPAIR for fat signal suppression. Three radiologists assessed overall DWI quality and delineability of 88 focal lesions (28 malignant, 60 benign) on images with b values of 800 and 1600 s/mm2, as well as apparent diffusion coefficient (ADC) maps. For each fat suppression method and b value, the longest lesion diameter was determined in addition to measuring the signal intensity in DWI and ADC value in standardized regions of interest.

RESULTS

Regardless of b values, image quality (all p < 0.001) and lesion delineability (all p ≤ 0.003) with WEXfs-DWI were deemed superior compared to SPAIR-DWI in benign and malignant lesions. Irrespective of lesion characterization, WEXfs-DWI provided superior signal-to-noise, contrast-to-noise and signal-intensity ratios with 1600 s/mm2 (all p ≤ 0.05). The lesion size difference between contrast-enhanced T1 subtraction images and DWI was smaller for WEXfs compared to SPAIR fat suppression (all p ≤ 0.007). The mean ADC value in malignant lesions was lower for WEXfs-DWI (p < 0.001), while no significant ADC difference was ascertained between both techniques in benign lesions (p = 0.947).

CONCLUSION

WEXfs-DWI provides better subjective and objective image quality than standard SPAIR-DWI, resulting in a more accurate estimation of benign and malignant lesion size.

Time-dependent diffusion MRI using multiple stimulated echoes

PURPOSE

To develop a time-efficient pulse sequence that acquires multiple diffusion-weighted images with distinct diffusion times in a single shot by using multiple stimulated echoes (mSTE) with variable flip angles (VFA).

METHODS

The proposed diffusion-weighted mSTE with VFA (DW-mSTE-VFA) sequence begins with two 90° RF pulses that straddle a diffusion gradient lobe (GD ) to excite and restore one half of the magnetization into the longitudinal axis. The restored longitudinal magnetization was successively re-excited by a series of RF pulses with VFA, each followed by another GD , to generate a set of stimulated echoes. Each of the multiple stimulated echoes was acquired with an EPI echo train. As such, the train of multiple stimulated echoes produced a set of diffusion-weighted images with varying diffusion times in a single shot. This technique was experimentally demonstrated on a diffusion phantom, a fruit, and healthy human brain and prostate at 3 T.

RESULTS

In the phantom experiment, the mean ADC measured at different diffusion times using DW-mSTE-VFA were highly consistent (r = 0.999) with those from a commercial spin-echo diffusion-weighted EPI sequence. In the fruit and brain experiments, DW-mSTE-VFA exhibited similar diffusion-time dependence to a standard diffusion-weighted stimulated echo sequence. The ADC showed significant time dependence in the human brain (p = 0.003 in both white matter and gray matter) and prostate tissues (p = 0.003 in both peripheral zone and central gland).

CONCLUSION

DW-mSTE-VFA offers a time-efficient tool for investigating the diffusion-time dependency in diffusion MRI studies.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

BACKGROUND

To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion.

METHODS

This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant.

RESULTS

The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion.

CONCLUSIONS

The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC.

KEY POINTS

• Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Recommendations for improved reproducibility of ADC derivation on behalf of the Elekta MRI-linac consortium image analysis working group

BACKGROUND AND PURPOSE

The apparent diffusion coefficient (ADC), a potential imaging biomarker for radiotherapy response, needs to be reproducible before translation into clinical use. The aim of this study was to evaluate the multi-centre delineation- and calculation-related ADC variation and give recommendations to minimize it.

MATERIALS AND METHODS

Nine centres received identical diffusion-weighted and anatomical magnetic resonance images of different cancerous tumours (adrenal gland, pelvic oligo metastasis, pancreas, and prostate). All centres delineated the gross tumour volume (GTV), clinical target volume (CTV), and viable tumour volume (VTV), and calculated ADCs using both their local calculation methods and each of the following calculation conditions: b-values 0-500 vs. 150-500 s/mm2, region-of-interest (ROI)-based vs. voxel-based calculation, and mean vs. median. ADC variation was assessed using the mean coefficient of variation across delineations (CVD) and calculation methods (CVC). Absolute ADC differences between calculation conditions were evaluated using Friedman's test. Recommendations for ADC calculation were formulated based on observations and discussions within the Elekta MRI-linac consortium image analysis working group.

RESULTS

The median (range) CVD and CVC were 0.06 (0.02-0.32) and 0.17 (0.08-0.26), respectively. The ADC estimates differed 18% between b-value sets and 4% between ROI/voxel-based calculation (p-values < 0.01). No significant difference was observed between mean and median (p = 0.64). Aligning calculation conditions between centres reduced CVC to 0.04 (0.01-0.16). CVD was comparable between ROI types.

CONCLUSION

Overall, calculation methods had a larger impact on ADC reproducibility compared to delineation. Based on the results, significant sources of variation were identified, which should be considered when initiating new studies, in particular multi-centre investigations.

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review

Background and purpose

Improving the accuracy of brain tumour radiotherapy (RT) treatment planning is important to optimise patient outcomes. This systematic review investigates primary studies providing clinical evidence for the integration of quantitative magnetic resonance imaging (qMRI) biomarkers and MRI radiomics to optimise brain tumour RT planning.

Materials and methods

PubMed, Scopus, Embase and Web of Science databases were searched for all years until June 21, 2022. The search identified original articles demonstrating clinical evidence for the use of qMRI biomarkers and MRI radiomics for the optimization of brain cancer RT planning. Relevant information was extracted and tabulated, including qMRI metrics and techniques, impact on RT plan optimization and changes in target and normal tissue contouring and dose distribution.

Results

Nineteen articles met the inclusion criteria. Studies were grouped according to the qMRI biomarkers into: 1) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI; five studies); 2) diffusion tensor imaging (DTI; seven studies); and 3) MR spectroscopic imaging (MRSI; seven studies). No relevant MRI-based radiomics studies were identified. Integration of DTI maps offers the potential for improved organs at risk (OAR) sparing. MRSI metabolic maps are a promising technique for improving delineation accuracy in terms of heterogeneity and infiltration, with OAR sparing. No firm conclusions could be drawn regarding the integration of DWI metrics and PWI maps.

Conclusions

Integration of qMRI metrics into RT planning offers the potential to improve delineation and OAR sparing. Clinical trials and consensus guidelines are required to demonstrate the clinical benefits of such approaches.

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI

The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future.

An Unenhanced Breast MRI Protocol Based on Diffusion-Weighted Imaging: A Retrospective Single-Center Study on High-Risk Population for Breast Cancer

PURPOSE

This study aimed to investigate the use of contrast-free magnetic resonance imaging (MRI) as an innovative screening method for detecting breast cancer in high-risk asymptomatic women. Specifically, the researchers evaluated the diagnostic performance of diffusion-weighted imaging (DWI) in this population.

METHODS

MR images from asymptomatic women, carriers of a germline mutation in either the BRCA1 or BRCA2 gene, collected in a single center from January 2019 to December 2021 were retrospectively evaluated. A radiologist with experience in breast imaging (R1) and a radiology resident (R2) independently evaluated DWI/ADC maps and, in case of doubts, T2-WI. The standard of reference was the pathological diagnosis through biopsy or surgery, or ≥1 year of clinical and radiological follow-up. Diagnostic performances were calculated for both readers with a 95% confidence interval (CI). The agreement was assessed using Cohen's kappa (κ) statistics.

RESULTS

Out of 313 women, 145 women were included (49.5 ± 12 years), totaling 344 breast MRIs with DWI/ADC maps. The per-exam cancer prevalence was 11/344 (3.2%). The sensitivity was 8/11 (73%; 95% CI: 46-99%) for R1 and 7/11 (64%; 95% CI: 35-92%) for R2. The specificity was 301/333 (90%; 95% CI: 87-94%) for both readers. The diagnostic accuracy was 90% for both readers. R1 recalled 40/344 exams (11.6%) and R2 recalled 39/344 exams (11.3%). Inter-reader reproducibility between readers was in moderate agreement (κ = 0.43).

CONCLUSIONS

In female carriers of a BRCA1/2 mutation, breast DWI supplemented with T2-WI allowed breast cancer detection with high sensitivity and specificity by a radiologist with extensive experience in breast imaging, which is comparable to other screening tests. The findings suggest that DWI and T2-WI have the potential to serve as a stand-alone method for unenhanced breast MRI screening in a selected population, opening up new perspectives for prospective trials.

Influence of adaptive denoising on Diffusion Kurtosis Imaging at 3T and 7T

BACKGROUND AND OBJECTIVE

Choosing the most appropriate denoising method to improve the quality of diagnostic images maximally is key in pre-processing of diffusion MRI images. Recent advancements in acquisition and reconstruction techniques have questioned traditional noise estimation methods favoring adaptive denoising frameworks, circumventing the need to know a priori information that is hardly available in a clinical setting. In this observational study, we compared two innovative adaptive techniques sharing some features, Patch2Self and Nlsam, through application on reference adult data at 3T and 7T. The primary aim was identifying the most effective method in case of Diffusion Kurtosis Imaging (DKI) data - particularly susceptible to noise and signal fluctuations - at 3T and 7T fields. A side goal consisted of investigating the dependence of kurtosis metrics' variability with respect to the magnetic field on the adopted denoising methodology.

METHODS

For comparison purposes, we focused on qualitative and quantitative analysis of DKI data and related microstructural maps before and after applying the two denoising approaches. Specifically, we assessed computational efficiency, preservation of anatomical details via perceptual metrics, consistency of microstructure model fitting, alleviation of degeneracies in model estimation, and joint variability with varying field strength and denoising method.

RESULTS

Accounting for all these factors, Patch2Self framework has turned out to be specifically suitable for DKI data, with improving performance at 7T. Nlsam method is more robust in alleviating degenerate black voxels while introducing some blurring, which in turn is reflected in an overall loss of image sharpness. Regarding the impact of denoising on field-dependent variability, both methods have been shown to make variations from standard to Ultra-High Field more concordant with theoretical evidence, claiming that kurtosis metrics are sensitive to susceptibility-induced background gradients, directly proportional to the magnetic field strength and sensitive to the microscopic distribution of iron and myelin.

CONCLUSIONS

This study serves as a proof-of-concept stressing the need for an accurate choice of a denoising methodology, specifically tailored for the data under analysis and allowing higher spatial resolution acquisition within clinically compatible timings, with all the potential benefits that improving suboptimal quality of diagnostic images entails.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Dependence of apparent diffusion coefficient on slice position in magnetic resonance diffusion imaging

PURPOSE

The position dependence of the apparent diffusion coefficient (ADC) in magnetic resonance imaging (MRI) by echo-planar imaging (EPI)- and turbo spin echo (TSE)-diffusion-weighted imaging (DWI) was assessed using phantoms.

METHODS

Six pure water-filled containers were placed parallel to the direction of the static magnetic field from the center of the magnetic field to the foot direction (five containers) and the head direction (one container). Six slice positions were set, and a cross-section image was scanned at the center of each container using a 1.5-T MRI scanner. Diffusion times for both EPI- and TSE-DWI were matched as much as possible. The slice thickness was adjusted to match the signal-to-noise ratio (SNR) at the center of the magnetic field for both sequences. A B₁ map was analyzed. The ADC and SNR at each position of both sequences were tested using the Wilcoxon signed-rank test (P = 0.05) and compared using Friedman and Steel-Dwass multiple comparison tests (P = 0.05). Pearson correlation coefficients between ADC and SNR and between ADC and flip angle (FA) were calculated.

RESULTS

ADC decreased significantly with distance from the center of the magnetic field for both EPI-DWI and TSE-DWI (P < 0.05). TSE-ADC was significantly higher than EPI-ADC for all combinations (P < 0.01). Based on the Friedman test, the SNR of EPI- and TSE-DWI was significantly different and depended on the slice position (P < 0.01). The Pearson correlation coefficient between ADC and SNR was 0.78 in EPI-DWI and 0.60 in TSE-DWI, whereas that between ADC and FA was 0.97 in EPI-DWI and 0.94 in TSE-DWI. The FA decreased by 0.048 and 0.047° per mm from the center of the magnetic field to head and foot directions, respectively.

CONCLUSION

ADC depends on the slice position and decreases with an increase in distance from the magnetic field center. Caution should be taken when comparing and quantitatively evaluating the ADC at sites shifted in the long-axis direction.

Acquisition Parameters Influence Diffusion Metrics Effectiveness in Probing Prostate Tumor and Age-Related Microstructure

This study aimed to investigate the Diffusion-Tensor-Imaging (DTI) potential in the detection of microstructural changes in prostate cancer (PCa) in relation to the diffusion weight (b-value) and the associated diffusion length l_D. Thirty-two patients (age range = 50-87 years) with biopsy-proven PCa underwent Diffusion-Weighted-Imaging (DWI) at 3T, using single non-zero b-value or groups of b-values up to b = 2500 s/mm². The DTI maps (mean-diffusivity, MD; fractional-anisotropy, FA; axial and radial diffusivity, D// and D┴), visual quality, and the association between DTI-metrics and Gleason Score (GS) and DTI-metrics and age were discussed in relation to diffusion compartments probed by water molecules at different b-values. DTI-metrics differentiated benign from PCa tissue (p ≤ 0.0005), with the best discriminative power versus GS at b-values ≥ 1500 s/mm², and for b-values range 0-2000 s/mm², when the l_D is comparable to the size of the epithelial compartment. The strongest linear correlations between MD, D//, D┴, and GS were found at b = 2000 s/mm² and for the range 0-2000 s/mm². A positive correlation between DTI parameters and age was found in benign tissue. In conclusion, the use of the b-value range 0-2000 s/mm² and b-value = 2000 s/mm² improves the contrast and discriminative power of DTI with respect to PCa. The sensitivity of DTI parameters to age-related microstructural changes is worth consideration.

Comparison of whole-body DW-MRI with 2-[18F]FDG PET for staging and treatment monitoring of children with Langerhans cell histiocytosis

PURPOSE

To assess and compare the diagnostic accuracy of whole-body (WB) DW-MRI with 2-[18F]FDG PET for staging and treatment monitoring of children with Langerhans cell histiocytosis (LCH).

METHODS

Twenty-three children with LCH underwent 2-[18F]FDG PET and WB DW-MRI at baseline. Two nuclear medicine physicians and two radiologists independently assessed presence/absence of tumors in 8 anatomical areas. Sixteen children also performed 2-[18F]FDG PET and WB DW-MRI at follow-up. One radiologist and one nuclear medicine physician revised follow-up scans and collected changes in tumor apparent diffusion (ADC) and standardized uptake values (SUV) before and after therapy in all detectable lesions. 2-[18F]FDG PET results were considered the standard of reference for tumor detection and evaluation of treatment response according to Lugano criteria. Sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of WB DW-MRI at baseline were calculated, and the 95% confidence intervals were estimated by using the Clopper-Pearson (exact) method; changes in tumor SUVs and ADC were compared using a Mann-Whitney U test. Agreement between reviewers was assessed with a Cohen's weighted kappa coefficient. Analyses were conducted using SAS software version 9.4.

RESULTS

Agreement between reviewers was perfect (kappa coefficient = 1) for all analyzed regions but spine and neck (kappa coefficient = 0.89 and 0.83, respectively) for 2-[18F]FDG PET images, and abdomen and pelvis (kappa coefficient = 0.65 and 0.88, respectively) for WB DW-MRI. Sensitivity and specificity were 95.5% and 100% for WB DW-MRI compared to 2-[18F]FDG PET. Pre to post-treatment changes in SUVratio and ADCmean were inversely correlated for all lesions (r: -0.27, p = 0·06) and significantly different between responders and non-responders to chemotherapy (p = 0.0006 and p = 0·003 for SUVratio and ADCmean, respectively).

CONCLUSION

Our study showed that WB DW-MRI has similar accuracy to 2-[18F]FDG PET for staging and treatment monitoring of LCH in children. While 2-[18F]FDG PET remains an approved radiological examination for assessing metabolically active disease, WB DW-MRI could be considered as an alternative approach without radiation exposure. The combination of both modalities might have advantages over either approach alone.

Unsupervised cycle-consistent network using restricted subspace field map for removing susceptibility artifacts in EPI

PURPOSE

To design an unsupervised deep neural model for correcting susceptibility artifacts in single-shot Echo Planar Imaging (EPI) and evaluate the model for preclinical and clinical applications.

METHODS

This work proposes an unsupervised cycle-consistent model based on the restricted subspace field map to take advantage of both the deep learning (DL) and the reverse polarity-gradient (RPG) method for single-shot EPI. The proposed model consists of three main components: (1) DLRPG neural network (DLRPG-net) to obtain field maps based on a pair of images acquired with reversed phase encoding; (2) spin physical model-based modules to obtain the corrected undistorted images based on the learned field map; and (3) cycle-consistency loss between the input images and back-calculated images from each cycle is explored for network training. In addition, the field maps generated by DLRPG-net belong to a restricted subspace, which is a span of predefined cubic splines to ensure the smoothness of the field maps and avoid blurring in the corrected images. This new method is trained and validated on both preclinical and clinical datasets for diffusion MRI.

RESULTS

The proposed network could effectively generate smooth field maps and correct susceptibility artifacts in single-shot EPI. Simulated and in vivo preclinical/clinical experiments demonstrated that our method outperforms the state-of-the-art susceptibility artifact correction methods. Furthermore, the ablation experiments of the cycle-consistent network and the restricted subspace in generating field maps did show the advantages of DLRPG-net.

CONCLUSION

The proposed method (DLRPG-net) can effectively correct susceptibility artifacts for preclinical and clinical single-shot EPI sequences.

Technical Advancements in Abdominal Diffusion-weighted Imaging

Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

Evaluation of apparent diffusion coefficient of two-dimensional BLADE turbo gradient- and spin-echo diffusion-weighted imaging with a breast phantom

This study aimed to evaluate the reliability of apparent diffusion coefficient (ADC) values generated with two-dimensional turbo gradient- and spin-echo with BLADE trajectory diffusion-weighted imaging (TGSE-BLADE-DWI) sequence using a breast diffusion phantom. TGSE-BLADE-DWI and single-shot spin-echo echo-planar imaging (SS-EPI-DWI) were performed using a 3.0 T magnetic resonance imaging scanner. Concordance rates of ADC values and the signal-to-noise ratio (SNR) were compared between TGSE-BLADE-DWI and SS-EPI-DWI. TGSE-BLADE-DWI provided a higher concordance rate for ADC values than SS-EPI-DWI when b-values > 2000s/mm² and a slice thickness of 1 mm were used. TGSE-BLADE-DWI showed less image distortion than SS-EPI-DWI. The SNR of TGSE-BLADE-DWI was higher than that of SS-EPI-DWI, except at a number of excitations of 7 and a slice thickness of 1 mm. In conclusion, TGSE-BLADE-DWI can offer a better SNR, less distortion, and more reliable ADC measurements than SS-EPI-DWI in a breast phantom.

Apparent Diffusion Coefficient Reproducibility Across 3 T Scanners in a Breast Diffusion Phantom

BACKGROUND

To date, the accuracy and variability of diffusion-weighted MRI (DW-MRI) metrics have been reported in a limited number of scanner/protocol/coil combinations.

PURPOSE

To evaluate the reproducibility of DW-MRI estimates across multiple scanners and DW-MRI protocols and to assess the effects of using an 8-channel vs. 16-channel breast coil in a breast phantom.

STUDY TYPE

Prospective.

PHANTOM

Breast phantom containing tubes of water and differing polyvinylpyrrolidone (PVP) concentrations with apparent diffusion coefficients (ADCs) matching breast tissue.

FIELD STRENGTH/SEQUENCE

3 T (three standard and one wide bore), three DW-MRI single-shot echo planar imaging protocols of varying acquired spatial resolution.

ASSESSMENT

Accuracy of estimated ADCs was assessed using percent differences (PD) relative to nuclear magnetic resonance spectroscopy-derived reference values. Coefficients of variation (CV) were calculated to determine variation across scanners. CVs based on the median standard deviation (CV_M ) were used to evaluate tube-specific dispersion using 8- or 16-channel coils at a single scanner. ADCs of PVP-containing tubes were additionally normalized by the median water tube ADC to account for temperature effects.

STATISTICAL TESTS

Two-way repeated measures analysis of variance and post hoc tests were used to evaluate differences in ADC, CV, and CV_M across scanners and protocols (α = 0.05).

RESULTS

ADCs were within 11% (interquartile range [IQR] 7%) of reference values and significantly improved to 2% (IQR 7%) after normalization to an internal water reference. Normalization significantly reduced interscanner variability of ADC estimates from 7% to 4%. DW-MRI protocol did not affect ADC accuracy; however, the clinical and higher-resolution clinical protocols resulted in the greatest (9%) and least (6%) interscanner variability, respectively. The 8- and 16-channel receive coils yielded similar accuracy (PD: 12% vs. 16%) and precision (CV_M : 2.7% vs. 2.9%).

DATA CONCLUSION

Normalization by an internal reference improved interscanner ADC reproducibility. High-resolution protocols yielded comparably accurate and significantly less variable ADCs compared to a clinical standard protocol.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Eddy current-induced artifacts correction in high gradient strength diffusion MRI with dynamic field monitoring: demonstration in ex vivo human brain imaging

Purpose

To demonstrate the advantages of spatiotemporal magnetic field monitoring to correct eddy current-induced artifacts (ghosting and geometric distortions) in high gradient strength diffusion MRI (dMRI).

Methods

A dynamic field camera with 16 NMR field probes was used to characterize eddy current fields induced from diffusion gradients for different gradients strengths (up to 300 mT/m), diffusion directions, and shots in a 3D multi-shot EPI sequence on a 3T Connectom scanner. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-resolution whole brain ex vivo dMRI. A 3D multi-shot image reconstruction framework was informed with the actual nonlinear phase evolution measured with the dynamic field camera, thereby accounting for high-order eddy currents fields on top of the image encoding gradients in the image formation model.

Results

Eddy current fields from diffusion gradients at high gradient strength in a 3T Connectom scanner are highly nonlinear in space and time, inducing high-order spatial phase modulations between odd/even echoes and shots that are not static during the readout. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting approaches such as navigator- and structured low-rank-based methods or MUSE, followed by image-based distortion correction with eddy. Improved dMRI analysis is demonstrated with diffusion tensor imaging and high-angular resolution diffusion imaging.

Conclusion

Strong eddy current artifacts characteristic of high gradient strength dMRI can be well corrected with dynamic field monitoring-based image reconstruction, unlike the two-step approach consisting of ghosting correction followed by geometric distortion reduction with eddy.