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Purpose: To develop a new method for high-fidelity, high-resolution 3D multi-slab diffusion MRI with minimal distortion and boundary slice aliasing.
Methods: Our method modifies 3D multi-slab imaging to integrate blip-reversed acquisitions for distortion correction and oversampling in the slice direction (k ) for reducing boundary slice aliasing. Our aim is to achieve robust acceleration to keep the scan time the same as conventional 3D multi-slab acquisitions, in which data are acquired with a single direction of blip traversal and without k -oversampling. We employ a two-stage reconstruction. In the first stage, the blip-up/down images are respectively reconstructed and analyzed to produce a field map for each diffusion direction. In the second stage, the blip-reversed data and the field map are incorporated into a joint reconstruction to produce images that are corrected for distortion and boundary slice aliasing.
Results: We conducted experiments at 7T in six healthy subjects. Stage 1 reconstruction produces images from highly under-sampled data (R = 7.2) with sufficient quality to provide accurate field map estimation. Stage 2 joint reconstruction substantially reduces distortion artifacts with comparable quality to fully-sampled blip-reversed results (2.4× scan time). Whole-brain in-vivo results acquired at 1.22 mm and 1.05 mm isotropic resolutions demonstrate improved anatomical fidelity compared to conventional 3D multi-slab imaging. Data demonstrate good reliability and reproducibility of the proposed method over multiple subjects.
Conclusion: The proposed acquisition and reconstruction framework provide major reductions in distortion and boundary slice aliasing for 3D multi-slab diffusion MRI without increasing the scan time, which can potentially produce high-quality, high-resolution diffusion MRI.
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http://dx.doi.org/10.1002/mrm.29741 | DOI Listing |
Asian Biomed (Res Rev News)
August 2025
Department of Health Information Management and Technology, University of Hafr Al-Batin College of Applied Medical Sciences, Hafar Al Batin 39953, Saudi Arabia.
Background: The prefrontal cortex (PFC) is vital for cognitive and emotional functions and is vulnerable to disruptions in preterm infants. Reliable volume estimation methods are needed to study its development.
Objective: To develop and validate a novel method for estimating the volume of PFC subfields in very preterm infants using magnetic resonance imaging (MRI) combined with stereological techniques.
Comput Biol Med
September 2025
London South Bank University, Department of Computer Science & Informatics, 103 Borough Rd, London, SE1 0AA, United Kingdom.
Delineating and classifying individual cells in microscopy tissue images is inherently challenging yet remains essential for advances in medical and neuroscientific research. In this work, we propose a new deep learning framework, CISCA, for automatic cell instance segmentation and classification in histological slices. At the core of CISCA is a network architecture featuring a lightweight U-Net with three heads in the decoder.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, Taiwan.
Background: Intracranial aneurysms, particularly saccular types, are localized dilations of cerebral vessels prone to rupture, leading to life-threatening complications such as subarachnoid hemorrhage.
Purpose: This study aimed to characterize the localized hemodynamic environment within the aneurysm dome and evaluate how spatial interactions among key flow parameters contribute to rupture risk, using a synergistic analytical framework.
Methods: We applied the targeted evaluation of synergistic links in aneurysms (TESLA) framework to analyze 18 intracranial aneurysms from 15 patients.
BMC Cancer
August 2025
Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, 330029, China.
Background: Accurate delineation of Gross Tumor Volume (GTV) in lung cancer is critical for effective radiotherapy and surgical planning. However, segmentation of GTV in high-resolution CT images remains challenging, particularly when tumors are small or have indistinct boundaries.
Methods: We propose D-S-Net, a novel dual-stage strategy to enhance both the accuracy and efficiency of lung cancer GTV segmentation.
Bioengineering (Basel)
July 2025
School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON N6A 3K7, Canada.
Accurate segmentation in medical imaging is essential for disease diagnosis and monitoring, particularly in lung imaging using proton and hyperpolarized gas MRI. However, image degradation due to noise and artifacts-especially in hyperpolarized gas MRI, where scans are acquired during breath-holds-poses challenges for conventional segmentation algorithms. This study evaluates the robustness of deep learning segmentation models under varying Gaussian noise levels, comparing traditional convolutional neural networks (CNNs) with modern Vision Transformer (ViT)-based models.
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