OBJECTIVE: To investigate how the presence/side of hippocampal sclerosis (HS) are related to the white matter structure of cingulum bundle (CB), arcuate fasciculus (AF), and inferior longitudinal fasciculus (ILF) in mesial temporal lobe epilepsy (MTLE).

METHODS: We acquired diffusion-weighted magnetic resonance imaging (MRI) from 86 healthy and 71 individuals with MTLE (22 righ-HS; right-HS, 34 left-HS; left-HS, and 15 nonlesional MTLE). We utilized two-tensor tractography and fiber clustering to compare fractional anisotropy (FA) of each side/tract between groups. Additionally, we examined the association between FA and nonverbal (WMS-R) and verbal (WMS-R, RAVLT codification) memory performance for MTLE individuals.

RESULTS: White matter abnormalities depended on the side and presence of HS. The left-HS demonstrated widespread abnormalities for all tracts, the right-HS showed lower FA for ipsilateral tracts and the nonlesional MTLE group did not differ from healthy individuals. Results indicate no differences in verbal/nonverbal memory performance between the groups, but trend-level associations between higher FA of visual memory and the left CB (r = 0.286, P = 0.018), verbal memory (RAVLT) and -left CB (r = 0.335, P = 0.005), -right CB (r = 0.286, P = 0.016), and -left AF (r = 0.287, P = 0.017).

SIGNIFICANCE: Our results highlight that the presence and side of HS are crucial to understand the pathophysiology of MTLE. Specifically, left-sided HS seems to be related to widespread bilateral white matter abnormalities. Future longitudinal studies should focus on developing diagnostic and treatment strategies dependent on HS's presence/side.

Atlases are crucial to imaging statistics as they enable the standardization of inter-subject and inter-population analyses. While existing atlas estimation methods based on fluid/elastic/diffusion registration yield high-quality results for the human brain, these deformation models do not extend to a variety of other challenging areas of neuroscience such as the anatomy of C. elegans worms and fruit flies. To this end, this work presents a general probabilistic deep network-based framework for atlas estimation and registration which can flexibly incorporate various deformation models and levels of keypoint supervision that can be applied to a wide class of model organisms. Of particular relevance, it also develops a deformable piecewise rigid atlas model which is regularized to preserve inter-observation distances between neighbors. These modeling considerations are shown to improve atlas construction and key-point alignment across a diversity of datasets with small sample sizes including neuron positions in C. elegans hermaphrodites, fluorescence microscopy of male C. elegans, and images of fruit fly wings. Code is accessible at https://github.com/amin-nejat/Deformable-Atlas.

Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost phantoms were provided. Overall, this paper aims to summarize a breadth of ultrasound-compatible phantom research to enable informed phantom methods selection.

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.

BACKGROUND: Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level.

PURPOSE: To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters.

MATERIALS AND METHODS: We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively.

RESULTS: Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise.

CONCLUSION: Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment (f) is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes infhas not been validated. In this work we put aside the biological interpretation offand study the sensitivity implications of fitting single-shell data to a two-compartment model. We identify a nonlinear transformation between the one-compartment model (diffusion tensor imaging, DTI) and a two-compartment model in which the mean diffusivities of both compartments are effectively fixed. While the analytic relationship implies that fitting this two-compartment model does not offer any more information than DTI, it explains why metrics derived from a two-compartment model can exhibit enhanced sensitivity over DTI to certain types of WM processes, such as age-related WM differences. The sensitivity enhancement should not be viewed as a substitute for acquiring multi-shell data. Rather, the results of this study provide insight into the consequences of choosing a two-compartment model when only single-shell data is available.

INTRODUCTION: As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish.

METHODS: Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements.

RESULTS: The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml).

DISCUSSION: The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.

PURPOSE: Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations.

METHODS: We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI.

RESULTS: Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI.

CONCLUSION: Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.

BACKGROUND: Motivational symptoms such as apathy and anhedonia are common in Parkinson's disease (PD), respond poorly to treatment, and are hypothesised to share underlying neural mechanisms. Striatal dopaminergic dysfunction is considered central to motivational symptoms in PD but the association has never been examined longitudinally. We investigated whether progression of dopaminergic dysfunction was associated with emergent apathy and anhedonia symptoms in PD.

METHODS: Longitudinal cohort study of 412 newly diagnosed patients with PD followed over 5 years as part of the Parkinson's Progression Markers Initiative cohort.Apathy and anhedonia were measured using a composite score derived from relevant items of the 15-item Geriatric Depression Scale (GDS-15) and part I of the MDS-Unified Parkinson's Disease Rating Scale. Dopaminergic neurodegeneration was measured using repeated striatal dopamine transporter (DAT) imaging.

RESULTS: Linear mixed-effects modelling across all contemporaneous data points identified a significant negative relationship between striatal DAT specific binding ratio (SBR) and apathy/anhedonia symptoms, which emerged as PD progressed (interaction:β=-0.09, 95% CI (-0.15 to -0.03), p=0.002). Appearance and subsequent worsening of apathy/anhedonia symptoms began on average 2 years after diagnosis and below a threshold striatal DAT SBR level. The interaction between striatal DAT SBR and time was specific to apathy/anhedonia symptoms, with no evidence of a similar interaction for general depressive symptoms from the GDS-15 (excluding apathy/anhedonia items) (β=-0.06, 95% CI (-0.13 to 0.01)) or motor symptoms (β=0.20, 95% CI (-0.25 to 0.65)).

CONCLUSIONS: Our findings support a central role for dopaminergic dysfunction in motivational symptoms in PD. Striatal DAT imaging may be a useful indicator of apathy/anhedonia risk that could inform intervention strategies.

BACKGROUND: Diffusion magnetic resonance imaging white matter tractography, an increasingly popular preoperative planning modality used for pre-surgical planning in brain tumor patients, is employed with the goal of maximizing tumor resection while sparing postoperative neurological function. Clinical translation of white matter tractography has been limited by several shortcomings of standard diffusion tensor imaging (DTI), including poor modeling of fibers crossing through regions of peritumoral edema and low spatial resolution for typical clinical diffusion MRI (dMRI) sequences. Track density imaging (TDI) is a post-tractography technique that uses the number of tractography streamlines and their long-range continuity to map the white matter connections of the brain with enhanced image resolution relative to the acquired dMRI data, potentially offering improved white matter visualization in patients with brain tumors. The aim of this study was to assess the utility of TDI-based white matter maps in a neurosurgical planning context compared to the current clinical standard of DTI-based white matter maps.

METHODS: Fourteen consecutive brain tumor patients from a single institution were retrospectively selected for the study. Each patient underwent 3-Tesla dMRI scanning with 30 gradient directions and a b-value of 1000 s/mm2. For each patient, two directionally encoded color (DEC) maps were produced as follows. DTI-based DEC-fractional anisotropy maps (DEC-FA) were generated on the scanner, while DEC-track density images (DEC-TDI) were generated using constrained spherical deconvolution based tractography. The potential clinical utility of each map was assessed by five practicing neurosurgeons, who rated the maps according to four clinical utility statements regarding different clinical aspects of pre-surgical planning. The neurosurgeons rated each map according to their agreement with four clinical utility statements regarding if the map 1 identified clinically relevant tracts, (2) helped establish a goal resection margin, (3) influenced a planned surgical route, and (4) was useful overall. Cumulative link mixed effect modeling and analysis of variance were performed to test the primary effect of map type (DEC-TDI vs. DEC-FA) on rater score. Pairwise comparisons using estimated marginal means were then calculated to determine the magnitude and directionality of differences in rater scores by map type.

RESULTS: A majority of rater responses agreed with the four clinical utility statements, indicating that neurosurgeons found both DEC maps to be useful. Across all four investigated clinical utility statements, the DEC map type significantly influenced rater score. Rater scores were significantly higher for DEC-TDI maps compared to DEC-FA maps. The largest effect size in rater scores in favor of DEC-TDI maps was observed for clinical utility statement 2, which assessed establishing a goal resection margin.

CONCLUSION: We observed a significant neurosurgeon preference for DEC-TDI maps, indicating their potential utility for neurosurgical planning.