3D MRI Data Sets Research Articles

High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain

Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.

A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.

A large normative connectome for exploring the tractographic correlates of focal brain interventions

Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics – the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts – have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.

Optimization and validation of the DESIGNER preprocessing pipeline for clinical diffusion MRI in white matter aging

Abstract Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline in a large clinical dMRI dataset and using ground-truth phantoms. DESIGNER, a preprocessing pipeline targeting various imaging artifacts in diffusion MRI data, has been modified to improve denoising and target Gibbs ringing for partial Fourier acquisitions. We compared the revised DESIGNER (Dv2) (including denoising, Gibbs removal, correction for motion, echo planar imaging (EPI) distortion, and eddy currents) against the original DESIGNER (Dv1) pipeline, minimal preprocessing (including correction for motion, EPI distortion, and eddy currents only), and no preprocessing on a large clinical dMRI dataset of 524 control subjects with ages between 25 and 75 years old. We evaluated the effect of specific processing steps on age correlations in white matter with diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics. We also evaluated the added effect of minimal Gaussian smoothing to deal with noise and to reduce outliers in parameter maps compared to DESIGNER-v2’s noise removal method. Moreover, Dv2’s updated noise and Gibbs removal methods were assessed using a ground truth dMRI phantom to evaluate accuracy. Results show age correlations of DTI and DKI metrics in white matter were affected by the preprocessing pipeline, causing systematic differences in absolute parameter values and loss or gain of statistical significance. Both in clinical dMRI and ground-truth phantoms, Dv2 pipeline resulted in the smallest number of outlier voxels and improved accuracy in DTI and DKI metrics as noise was reduced and Gibbs removal was improved. Thus, DESIGNER-v2 provides more accurate and robust DTI and DKI parameter maps by targeting common artifacts present in dMRI data acquired in clinical settings, as compared to no preprocessing or minimal preprocessing.

Voxel-Wise Fusion of 3T and 7T Diffusion MRI Data to Extract more Accurate Fiber Orientations.

While 7T diffusion magnetic resonance imaging (dMRI) has high spatial resolution, its diffusion imaging quality is usually affected by signal loss due to B1 inhomogeneity, T2 decay, susceptibility, and chemical shift. In contrast, 3T dMRI has relative higher diffusion angular resolution, but lower spatial resolution. Combination of 3T and 7T dMRI, thus, may provide more detailed and accurate information about the voxel-wise fiber orientations to better understand the structural brain connectivity. However, this topic has not yet been thoroughly explored until now. In this study, we explored the feasibility of fusing 3T and 7T dMRI data to extract voxel-wise quantitative parameters at higher spatial resolution. After 3T and 7T dMRI data was preprocessed, respectively, 3T dMRI volumes were coregistered into 7T dMRI space. Then, 7T dMRI data was harmonized to the coregistered 3T dMRI B0 (b = 0) images. Last, harmonized 7T dMRI data was fused with 3T dMRI data according to four fusion rules proposed in this study. We employed high-quality 3T and 7T dMRI datasets (N = 24) from the Human Connectome Project to test our algorithms. The diffusion tensors (DTs) and orientation distribution functions (ODFs) estimated from the 3T-7T fused dMRI volumes were statistically analyzed. More voxels containing multiple fiber populations were found from the fused dMRI data than from 7T dMRI data set. Moreover, extra fiber directions were extracted in temporal brain regions from the fused dMRI data at Otsu's thresholds of quantitative anisotropy, but could not be extracted from 7T dMRI dataset. This study provides novel algorithms to fuse intra-subject 3T and 7T dMRI data for extracting more detailed information of voxel-wise quantitative parameters, and a new perspective to build more accurate structural brain networks.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer.

The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.

BlobCUT: A Contrastive Learning Method to Support Small Blob Detection in Medical Imaging.

Medical imaging-based biomarkers derived from small objects (e.g., cell nuclei) play a crucial role in medical applications. However, detecting and segmenting small objects (a.k.a. blobs) remains a challenging task. In this research, we propose a novel 3D small blob detector called BlobCUT. BlobCUT is an unpaired image-to-image (I2I) translation model that falls under the Contrastive Unpaired Translation paradigm. It employs a blob synthesis module to generate synthetic 3D blobs with corresponding masks. This is incorporated into the iterative model training as the ground truth. The I2I translation process is designed with two constraints: (1) a convexity consistency constraint that relies on Hessian analysis to preserve the geometric properties and (2) an intensity distribution consistency constraint based on Kullback-Leibler divergence to preserve the intensity distribution of blobs. BlobCUT learns the inherent noise distribution from the target noisy blob images and performs image translation from the noisy domain to the clean domain, effectively functioning as a denoising process to support blob identification. To validate the performance of BlobCUT, we evaluate it on a 3D simulated dataset of blobs and a 3D MRI dataset of mouse kidneys. We conduct a comparative analysis involving six state-of-the-art methods. Our findings reveal that BlobCUT exhibits superior performance and training efficiency, utilizing only 56.6% of the training time required by the state-of-the-art BlobDetGAN. This underscores the effectiveness of BlobCUT in accurately segmenting small blobs while achieving notable gains in training efficiency.

Continuous time-resolved estimated synthetic 4D-CTs for dose reconstruction of lung tumor treatments at a 0.35 T MR-linac

Objective. To experimentally validate a method to create continuous time-resolved estimated synthetic 4D-computed tomography datasets (tresCTs) based on orthogonal cine MRI data for lung cancer treatments at a magnetic resonance imaging (MRI) guided linear accelerator (MR-linac). Approach. A breathing porcine lung phantom was scanned at a CT scanner and 0.35 T MR-linac. Orthogonal cine MRI series (sagittal/coronal orientation) at 7.3 Hz, intersecting tumor-mimicking gelatin nodules, were deformably registered to mid-exhale 3D-CT and 3D-MRI datasets. The time-resolved deformation vector fields were extrapolated to 3D and applied to a reference synthetic 3D-CT image (sCTref), while accounting for breathing phase-dependent lung density variations, to create 82 s long tresCTs at 3.65 Hz. Ten tresCTs were created for ten tracked nodules with different motion patterns in two lungs. For each dataset, a treatment plan was created on the mid-exhale phase of a measured ground truth (GT) respiratory-correlated 4D-CT dataset with the tracked nodule as gross tumor volume (GTV). Each plan was recalculated on the GT 4D-CT, randomly sampled tresCT, and static sCTref images. Dose distributions for corresponding breathing phases were compared in gamma (2%/2 mm) and dose–volume histogram (DVH) parameter analyses. Main results. The mean gamma pass rate between all tresCT and GT 4D-CT dose distributions was 98.6%. The mean absolute relative deviations of the tresCT with respect to GT DVH parameters were 1.9%, 1.0%, and 1.4% for the GTV D 98%, D 50%, and D 2%, respectively, 1.0% for the remaining nodules D 50%, and 1.5% for the lung V 20Gy. The gamma pass rate for the tresCTs was significantly larger (p < 0.01), and the GTV D 50% deviations with respect to the GT were significantly smaller (p < 0.01) than for the sCTref. Significance. The results suggest that tresCTs could be valuable for time-resolved reconstruction and intrafractional accumulation of the dose to the GTV for lung cancer patients treated at MR-linacs in the future.

Deformable medical image registration with global-local transformation network and region similarity constraint.

Deformable medical image registration can achieve fast and accurate alignment between two images, enabling medical professionals to analyze images of different subjects in a unified anatomical space. As such, it plays an important role in many medical image studies. Current deep learning (DL)-based approaches for image registration directly learn spatial transformation from one image to another, relying on a convolutional neural network and ground truth or similarity metrics. However, these methods only use a global similarity energy function to evaluate the similarity of a pair of images, which ignores the similarity of regions of interest (ROIs) within the images. This can limit the accuracy of the image registration and affect the analysis of specific ROIs. Additionally, DL-based methods often estimate global spatial transformations of images directly, without considering local spatial transformations of ROIs within the images. To address this issue, we propose a novel global-local transformation network with a region similarity constraint that maximizes the similarity of ROIs within the images and estimates both global and local spatial transformations simultaneously. Experiments conducted on four public 3D MRI datasets demonstrate that the proposed method achieves the highest registration performance in terms of accuracy and generalization compared to other state-of-the-art methods.

Short-term brain atrophy evolution after initiation of immunotherapy in a real-world multiple sclerosis cohort.

In multiple sclerosis (MS), brain atrophy measurements have emerged as an important biomarker reflecting neurodegeneration and disability progression. However, due to several potential confounders, investigation of brain atrophy in clinical routine and even in controlled clinical studies can be challenging. The aim of this study was to investigate the short-term dynamics of brain atrophy development after initiation of disease-modifying therapy (DMT) in a "real-world setting." In this retrospective study, we included MS patients starting DMT (natalizumab, fingolimod, dimethyl fumarate, or interferon-ß1a) or without DMT, availability of a baseline MRI, and two annual follow-up scans on the same MRI system. Two-timepoint percentage brain volume changes (PBVCs) were calculated. Fifty-five MS patients (12 patients starting DMT with natalizumab, 7 fingolimod, 14 dimethyl fumarate, 11 interferon-ß1a, and 11 patients without DMT) were included. We found the highest PBVCs in the first 12 months after initiation of natalizumab treatment. Furthermore, the PBVCs in our study were very much comparable to the results observed by other groups, as well as for fingolimod, dimethyl fumarate, and interferon-ß1a. We found PBVCs that are comparable to the results of previous studies, suggesting that brain atrophy, assessed on 3D MRI data sets acquired on the same 3T MRI, provides a robust MS biomarker.

Assessing neuroinflammatory differences in FTLD‐Tau vs FTLD‐TDP using free water diffusion

AbstractBackgroundFrontotemporal lobar degeneration (FTLD) is associated with two pathological substrates: Tau and TAR‐DNA‐binding protein 43 (TDP‐43). In‐vivo detection of the pathological substrate is still not possible in FTLD‐related syndromes but progressive supranuclear palsy (PSP) is consistently associated with FTLD‐Tau while semantic‐variant primary progressive aphasia (svPPA) and FTD‐MND (FTD‐ALS) are associated with FTLD‐TDP. In the absence of disease‐specific markers in FTLD, it is necessary to identify biomarkers that can help distinguish FTLD‐Tau and FTLD‐TDP. Recent research has implicated neuroinflammation as a common feature across FTLD subtypes (Bright et al., 2019), however further research is required to investigate neuroinflammatory differences between FTLD‐Tau and FTLD‐TDP, given that microglial burden and activation may differ in FTLD subtypes (Woollacott et al., 2020). The free water fraction (FW), a metric generated from diffusion MRI (dMRI) (Pasternak et al., 2009), is a non‐specific marker of neuroinflammation (Bergamino, Walsh and Stokes, 2021). We will investigate differences in FW diffusion between patients with presumed FTLD‐Tau versus FTLD‐TDP. We hypothesize that differences in neuroinflammation between FTLD‐Tau and FTLD‐TDP can be identified using the free water measure, which may infer neuroinflammatory differences.MethodFree‐water maps were generated from dMRI datasets obtained from 43 subjects: 19 with presumed FTLD‐Tau (PSP, mean age = 70), 6 with presumed FTLD‐TDP (mean age = 67, 4 svPPA, 2 FTD‐ALS), and 19 healthy controls (mean age 63). Grey matter (GM) was segmented from T1‐weighted MRIs using FSL’s ‘fast’, projected onto the diffusion MRI space, where free‐water maps were averaged across the GM. The average free water estimates from FTLD‐Tau, FTLD‐TDP and control datasets were tested for group differences using student’s t‐tests and analysis of covariance (ANCOVA), with age as a covariate.ResultsANCOVA revealed significant differences between the groups (F = 6.47, p &lt; 0.01). Post‐hoc tests showed significantly higher free‐water in FTLD‐Tau (p&lt;0.01) and FTLD‐TDP (p&lt;0.01) compared to controls. There was also higher free water in FTLD‐TDP in comparison to FTLD‐Tau, approaching significance (p = 0.0503)ConclusionOur preliminary data shows significant differences between healthy controls and patients with FTLD‐Tau and FTLD‐TDP and a trend for increased free water in FTLD‐TDP patients vs FTLD‐Tau, which may be indicative of neuroinflammatory differences.

Three-Dimensional Echocardiography Derived Printing: A Review of Workflow, Current, and Future Applications

While most of the work pertaining to 3D printing in cardiology has been based on 3D CT and MRI datasets, due to the recent advents in 3D printing and 3D echocardiography, 3D printing from echocardiography has now become feasible. In this review, we discuss the workflow, applications, limitations, and potential future directions of 3D echocardiography-based 3D printing. 3D printing using 3D echocardiographic datasets has been successfully deployed in the field of cardiovascular medicine in the twenty-first century. It was shown to provide a significant additive value particularly with regards to visualization of valves and subvalvar apparatus. Given its limitations of limited field of view and somewhat lower spatial resolution, this approach is likely ideally suited for a combined multi-modality-based printing. Patient-specific, 3D echocardiography-derived 3D-printed models are now quite feasible. This can be used for surgical/procedural planning and training for optimal outcomes.

ABCD_Harmonizer: An Open-source Tool for Mapping and Controlling for Scanner Induced Variance in the Adolescent Brain Cognitive Development Study.

Data from multisite magnetic resonance imaging (MRI) studies contain variance attributable to the scanner that can reduce statistical power and potentially bias results if not appropriately managed. The Adolescent Cognitive Brain Development (ABCD) study is an ongoing, longitudinal neuroimaging study acquiring data from over 11,000 children starting at 9-10years of age. These scans are acquired on 29 different scanners of 5 different model types manufactured by 3 different vendors. Publicly available data from the ABCD study include structural MRI (sMRI) measures such as cortical thickness and diffusion MRI (dMRI) measures such as fractional anisotropy. In this work, we 1) quantify the variance attributable to scanner effects in the sMRI and dMRI datasets, 2) demonstrate the effectiveness of the data harmonization approach called ComBat to address scanner effects, and 3) present a simple, open-source tool for investigators to harmonize image features from the ABCD study. Scanner-induced variance was present in every image feature and varied in magnitude by feature type and brain location. For almost all features, scanner variance exceeded variability attributable to age and sex. ComBat harmonization was shown to effectively remove scanner induced variance from all image features while preserving the biological variability in the data. Moreover, we show that for studies examining relatively small subsamples of the ABCD dataset, the use of ComBat harmonized data provides more accurate estimates of effect sizes compared to controlling for scanner effects using ordinary least squares regression.

WASSERSTEIN GAN-GRADIENT PENALTY WITH DEEP TRANSFER LEARNING FOR 3D MRI ALZHEIMER DISEASE CLASSIFICATION

MRI scans for Alzheimer's disease (AD) detection are popular. Recent computer vision (CV) and deep learning (DL) models help construct effective computer assisted diagnosis (CAD) models for AD detection and categorization. Due to their dependence on huge training datasets and effective hyper parameter tuning procedures, most models failed. Transfer learning adjusts the final fully linked layers to use trained DL models on smaller datasets. It handles picture categorization problems well. This research introduces a Wasserstein GAN-Gradient Penalty with Deep Transfer Learning (WGANGP-DTL)-based AD classification model on 3D MRI data. WGANGP-DTL aims to accurately identify and classify AD. WGANGP increases dataset size. The WGANGP-DTL model pre-processes MRI images using image enhancement and segments them using 3DSFCM. Feature extraction uses ant lion optimizer (ALO) with Inception v3 model. For classifying AD, deep belief network (DBN) model is used. The WGANGP-DTL model is experimentally validated using benchmark 3D MRI datasets. WGANGP-DTL outperformed recent methods in experiments.

The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density

Objective. To document the bias of the simplified free water model of diffusion MRI (dMRI) signal vis-à-vis a specific model which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE). Approach. Both models assume that volume fraction f of the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). The specific and simplified models are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to an in vivo healthy brain dMRI dataset. For both synthetic and in vivo data we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles. Main results. From the simulations we see that the difference between the estimated simplified f and specific f is largest for mid-range ground-truth f, and it increases as SNR increases. The estimation of volume fraction f is sensitive to the choice of model, simplified or specific, but the estimated diffusion parameters are robust to small perturbations in the simulation. Specific f is more accurate and precise than simplified f. In the white matter (WM) regions of the in vivo images, specific f is lower than simplified f. Significance. In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.

Reconstruction for 7T high-resolution whole-brain diffusion MRI using two-stage N/2 ghost correction and L1-SPIRiT without single-band reference.

To combine a new two-stage N/2 ghost correction and an adapted L1-SPIRiT method for reconstruction of 7T highly accelerated whole-brain diffusion MRI (dMRI) using only autocalibration scans (ACS) without the need of additional single-band reference (SBref) scans. The proposed ghost correction consisted of a 3-line reference approach in stage 1 and the reference-free entropy method in stage 2. The adapted L1-SPIRiT method was formulated within the 3D k-space framework. Its efficacy was examined by acquiring two dMRI data sets at 1.05-mm isotropic resolutions with a total acceleration of 6 or 9 (i.e., 2-fold or 3-fold slice and 3-fold in-plane acceleration). Diffusion analysis was performed to derive DTI metrics and estimate fiber orientation distribution functions (fODFs). The results were compared with those of 3D k-space GRAPPA using only ACS, all in reference to 3D k-space GRAPPA using both ACS and SBref (serving as a reference). The proposed ghost correction eliminated artifacts more robustly than conventional approaches. Our adapted L1-SPIRiT method outperformed 3D k-space GRAPPA when using only ACS, improving image quality to what was achievable with 3D k-space GRAPPA using both ACS and SBref scans. The improvement in image quality further resulted in an improvement in estimation performances for DTI and fODFs. The combination of our new ghost correction and adapted L1-SPIRiT method can reliably reconstruct 7T highly accelerated whole-brain dMRI without the need of SBref scans, increasing acquisition efficiency and reducing motion sensitivity.

Reliability and accuracy of dental MRI for measuring root canal length of incisors and canines: a clinical pilot study

To evaluate whether high-resolution, non-contrast-enhanced dental MRI (dMRI) can reliably and accurately measure the canal length of incisors and canines compared with cone-beam computed tomography (CBCT). Three-Tesla dMRI was performed in 31 participants (mean age: 50.1 ± 14.2 years) with CBCT data. In total, 67 teeth were included (28 from the upper jaw and 39 from the lower jaw; 25 central incisors, 22 lateral incisors, and 20 canines). CBCT and dMRI datasets were reconstructed to visualize the root canal pathway in a single slice in the vestibulo-oral (V-O) and mesio-distal (M-D) direction. Root canal length was measured twice by two radiologists using dMRI and CBCT. Data were statistically analyzed by calculating intraclass correlation coefficients (ICCs) and performing Bland–Altman analysis. The reliability of dMRI measurements was excellent and comparable to that of CBCT measurements (intra-rater I/intra-rater II/inter-rater was 0.990/0.965/0.951 for dMRI vs. 0.990/0.994/0.992 for CBCT in the M-D direction and 0.991/0.956/0.967 for dMRI vs. 0.998/0.994/0.996 for CBCT in the V-O direction). According to Bland–Altman analysis, the mean (95% confidence interval) underestimation of root canal lengths was 0.67 mm (− 1.22 to 2.57) for dMRI and 0.87 mm (− 0.29 to 2.04) for CBCT in the M-D direction/V-O direction. In 92.5% of cases, dMRI measurements of canal length had an accuracy within 0–2 mm. Visualization and measurement of canal length in vivo using dMRI is feasible. The reliability of dMRI measurements was high and comparable to that of CBCT measurements. However, the spatial and temporal resolution of dMRI is lower than that of CBCT, which means dMRI measurements are less accurate than CBCT measurements. This means dMRI is currently unsuitable for measuring canal length in clinical practice.

GAN Training Acceleration Using Fréchet Descriptor-Based Coreset

Generative Adversarial Networks (GANs) are a class of deep learning models being applied to image processing. GANs have demonstrated state-of-the-art performance in applications such as image generation and image-to-image translation, just to name a few. However, with this success comes the realization that the training of GANs takes a long time and is often limited by available computing resources. In this research, we propose to construct a Coreset using Fréchet Descriptor Distances (FDD-Coreset) to accelerate the training of GAN for blob identification. We first propose a Fréchet Descriptor Distance (FDD) to measure the difference between each pair of blob images based on the statistics derived from blob distribution. The Coreset is then employed using our proposed FDD metric to select samples from the entire dataset for GAN training. A 3D-simulated dataset of blobs and a 3D MRI dataset of human kidneys are studied. Using computation time and eight performance metrics, the GAN trained on the FDD-Coreset is compared against the model trained on the entire dataset and an Inception and Euclidean Distance-based Coreset (IED-Coreset). We conclude that the FDD-Coreset not only significantly reduces the training time, but also achieves higher denoising performance and maintains approximate performance of blob identification compared with training on the entire dataset.

Endodontic working length measurements of premolars and molars in high-resolution dental MRI: a clinical pilot study for assessment of reliability and accuracy

ObjectivesTo prospectively assess the reliability and accuracy of high-resolution, dental MRI (dMRI) for endodontic working length (WL) measurements of premolars and molars under clinical conditions.Materials and methodsThree-Tesla dMRI was performed in 9 subjects who also had undergone cone-beam computed tomography (CBCT) (mean age: 47 ± 13.5 years). A total of 34 root canals from 12 molars (4/8, upper/lower jaw; 22 root canals) and 11 premolars (2/9 upper/lower jaw; 12 root canals) were included. CBCT and dMRI datasets were reconstructed to visualize the root canal in one single slice. Subsequently, two radiologists measured the root canal lengths in both modalities twice in blinded fashion. Reliability and accuracy for both modalities were assessed using intraclass correlation coefficients (ICCs) and Bland–Altman analysis, respectively.ResultsReliability (intra-rater I/II; inter-rater) of dental MRI measurements was excellent and comparable to CBCT for premolars (0.993/0.900; 0.958 vs. 0.993/0.956; 0.951) and for molars (0.978/0.995; 0.986 vs. 0.992/0.996; 0.989). Bland–Altman analysis revealed a mean underestimation/bias (95% confidence interval) of dMRI measurements of 0.8 (− 1.44/3.05) mm for premolars and 0.4 (− 1.55/2.39) mm for molars. In up to 59% of the cases, the accuracy of dMRI for WL measurements was within the underestimation margin of 0 to 2 mm short of the apical foramen AF.ConclusionsIn vivo demonstration and measurement of WL are feasible using dMRI. The reliability of measurements is high and equivalent to CBCT. Nonetheless, due to lower spatial resolution and longer acquisition time, the accuracy of dMRI is inferior to CBCT, impeding its current use for clinical treatment planning.Clinical relevancedMRI is a promising radiation-free imaging technique. Its reliability for endodontic working length measurements is high, but its accuracy is not satisfactory enough yet.