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| http://dx.doi.org/10.1590/0004-282X-ANP-2020-0341 | DOI Listing |
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Deep-learning-based extraction of circle of Willis topology with anatomical priors.
Sci Rep
December 2024
Department of Applied Mathematics, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
The circle of Willis (CoW) is a circular arrangement of arteries in the human brain, exhibiting significant anatomical variability. The CoW is extensively studied in relation to neurovascular pathologies, with certain anatomical variants previously linked to ischemic stroke and intracranial aneurysms. In an individual CoW, arteries might be absent (aplasia) or underdeveloped (hypoplasia, diameter < 1 mm).
View Article and Find Full Text PDFGenerative modeling of the Circle of Willis using 3D-StyleGAN.
Neuroimage
December 2024
CLAIM - Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Germany; Department of Neurosurgery, Charité Universitätsmedizin Berlin, Germany. Electronic address:
The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anatomical variations. Deep learning has been used to characterize and quantify the status of the CoW in various applications for the diagnosis and treatment of cerebrovascular disease. In medical imaging, the performance of deep learning models is limited by the diversity and size of training datasets.
View Article and Find Full Text PDFA review of intracranial aneurysm imaging modalities, from CT to state-of-the-art MR.
AJNR Am J Neuroradiol
October 2024
From the Department of Radiology (S.A., T.C.G.), Indiana University School of Medicine, Indianapolis, IN, USA; Department of Surgery (K.K.), University of California, Los Angeles David Geffen School of Medicine, Los Angeles, CA, USA; Department of Neurological Surgery (D.M.M., R.J.A., A.S., T.K., S.P.P.), University of Chicago Pritzker School of Medicine, Chicago, IL, USA; Department of Radiology (T.J.C., M.C.H.), The University of Chicago, Chicago, IL, USA.
Traditional guidance for intracranial aneurysm (IA) management is dichotomized by rupture status. Fundamental to ruptured aneurysm management is the detection and treatment of subarachnoid hemorrhage, along with securing the aneurysm by the safest technique. On the other hand, unruptured aneurysms first require a careful assessment of natural history versus treatment risk, including an imaging assessment of aneurysm size, location, and morphology, along with additional evidence-based risk factors such as smoking, hypertension, and family history.
View Article and Find Full Text PDFHigh-resolution magnetic resonance imaging features of time-of-flight magnetic resonance angiography signal loss and its relevance to ischemic stroke.
Quant Imaging Med Surg
September 2024
Department of Neurology, China-Japan Union Hospital, Jilin University, Changchun, China.
Article Synopsis
- The study investigates the reasons behind focal signal loss in intracranial artery stenosis using 3D magnetic resonance angiography (MRA) and its link to recent ischemic strokes.
- A total of 401 patients with moderate-to-severe stenosis of the middle cerebral artery were analyzed, comparing features of affected vessels with and without signal loss.
- Results indicated that patients with signal loss not only had higher degrees of stenosis but were also more likely to have enhanced plaque characteristics, correlating significantly with recent strokes.*
Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA.
Neuroinformatics
October 2024
Department of Radiology, University of Washington, Seattle, WA, USA.
This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment planning. We aim to overcome the inherent instance imbalance and morphological variability by introducing a novel morphology and texture loss reweighting approach. Our innovative method involves the incorporation of tailored weights within the loss function of deep neural networks.
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