Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
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http://dx.doi.org/10.1016/j.neuroimage.2018.10.029 | DOI Listing |
J Imaging Inform Med
January 2025
Department of Radiology, UC Davis School of Medicine, University of California, Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817-2307, USA.
Purpose: To explore the information in routine digital subtraction angiography (DSA) and evaluate deep learning algorithms for automated identification of anatomic location in DSA sequences.
Methods: DSA of the abdominal aorta, celiac, superior mesenteric, inferior mesenteric, and bilateral external iliac arteries was labeled with the anatomic location from retrospectively collected endovascular procedures performed between 2010 and 2020 at a tertiary care medical center. "Key" images within each sequence demonstrating the parent vessel and the first bifurcation were additionally labeled.
Sci Rep
January 2025
Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, 50200, Thailand.
The formula-based estimation of the right internal jugular venous (IJV) catheterization depth can be inaccurate when using ultrasound guidance. External landmark-based and radiological landmark-based methods have been proposed as alternatives to estimate the insertion depth. This study aimed to evaluate these methods using transesophageal echocardiography (TEE)-guided insertion depth as the reference.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Radiation Oncology, Inha University Hospital, Incheon, Republic of Korea.
Background: High-dose-rate (HDR) brachytherapy using Iridium-192 as a radiation source is widely employed in cancer treatment to deliver concentrated radiation doses while minimizing normal tissue exposure. In this treatment, the precision with which the sealed radioisotope source is delivered significantly impacts clinical outcomes.
Purpose: This study aims to evaluate the feasibility of a new four-dimensional (4D) in vivo source tracking and treatment verification system for HDR brachytherapy using a patient-specific approach.
Comput Med Imaging Graph
January 2025
University of Electronic Science and Technology of China, Chengdu, Sichuan, China. Electronic address:
In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI.
View Article and Find Full Text PDFBackground: The importance of detecting amyloid β (Aβ) in the early stages of Alzheimer's disease has markedly increased following the approval of Lecanemab, a disease-modifying drug. MRI is a non-invasive and less expensive rather than amyloid PET as gold standard for Aβ biomarker, but its clinical ability to detect Aβ has not been demonstrated. MRI phase information reflects paramagnetic substance including iron associated with Aβ aggregation.
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