While the etiology of hippocampal sclerosis (HS) in epilepsy patients remains unknown, distinct phenotypes of hippocampal subfield atrophy have been associated with different clinical presentations and surgical outcomes. The advent of novel techniques including ultra-high field 7T magnetic resonance imaging (MRI) and automated subfield volumetry have further enabled detection of hippocampal pathology in patients with epilepsy, however, studies combining both 7T MRI and automated segmentation in epilepsy patients with normal-appearing clinical MRI are limited. In this study, we present a novel application of the automated segmentation of hippocampal subfields (ASHS) software to determine subfield volumes of the CA1, CA2/3, CA4/DG, and the subiculum using ultra high-field 7T MRI scans, including T1-weighted MP2RAGE and T2-TSE sequences, in 27 patients with either mesial temporal lobe epilepsy (mTLE) or neocortical epilepsy (NE) compared to age and gender matched healthy controls. We found that 7T improved visualization of structural abnormalities not otherwise seen on clinical strength MRIs in patients with unilateral mTLE. Additionally, our automated segmentation algorithm was able to detect structural differences in volume and asymmetry across hippocampal subfields in unilateral mTLE patients compared to controls. Specifically, amongst unilateral mTLE patients with longer disease durations, volume loss was observed in the ipsilateral CA1 and CA2/3 subfields and contralateral CA1. There were no differences in subfield volumes in patients with NE compared to controls. We report the first application of 7T with automated segmentation to characterize the relationship between disease duration burden and asymmetry across specific hippocampal subfields in this population. Disease duration was found to have a statistically significant positive relationship with subfield asymmetry within the unilateral mTLE cohort. These findings highlight the ability of 7T MRI and automated segmentation to provide novel qualitative and quantitative information in epilepsy patients who are otherwise MRI-negative at clinical field strengths.
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http://dx.doi.org/10.3389/fneur.2021.682615 | DOI Listing |
Int J Comput Assist Radiol Surg
January 2025
Department of Medical Biophysics, University of Toronto, Toronto, Canada.
Purpose: During endovascular revascularization interventions for peripheral arterial disease, the standard modality of X-ray fluoroscopy (XRF) used for image guidance is limited in visualizing distal segments of infrapopliteal vessels. To enhance visualization of arteries, an image registration technique was developed to align pre-acquired computed tomography (CT) angiography images and to create fusion images highlighting arteries of interest.
Methods: X-ray image metadata capturing the position of the X-ray gantry initializes a multiscale iterative optimization process, which uses a local-variance masked normalized cross-correlation loss to rigidly align a digitally reconstructed radiograph (DRR) of the CT dataset with the target X-ray, using the edges of the fibula and tibia as the basis for alignment.
J Neurosci Methods
January 2025
Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA. Electronic address:
Background: The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.
View Article and Find Full Text PDFOrthod Craniofac Res
January 2025
Department of Health Sciences, School of Dentistry, Magna Graecia University of Catanzaro, Catanzaro, Italy.
Objective: This retrospective study aimed to evaluate morphometric changes in mandibular condyles of patients with skeletal Class III malocclusion following two-jaw orthognathic surgery planned using virtual surgical planning (VSP) and analysed with automated three-dimensional (3D) image analysis based on deep-learning techniques.
Materials And Methods: Pre-operative (T1) and 12-18 months post-operative (T2) Cone-Beam Computed Tomography (CBCT) scans of 17 patients (mean age: 24.8 ± 3.
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Ophthalmology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach.
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