AIMC Topic: Magnetic Resonance Imaging
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Classification of Irritable Bowel Syndrome Using Brain Functional Connectivity Strength and Machine Learning.
Neurogastroenterology and motility
Jan 3, 2025
BACKGROUND: Irritable Bowel Syndrome (IBS) is a prevalent condition characterized by dysregulated brain-gut interactions. Despite its widespread impact, the brain mechanism of IBS remains incompletely understood, and there is a lack of objective diag...
Deep learning-based pelvimetry in pelvic MRI volumes for pre-operative difficulty assessment of total mesorectal excision.
Surgical endoscopy
Jan 3, 2025
BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are...
Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis.
Current oncology (Toronto, Ont.)
Jan 3, 2025
Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective ...
Automatic segmentation of white matter lesions on multi-parametric MRI: convolutional neural network versus vision transformer.
BMC neurology
Jan 3, 2025
BACKGROUND AND PURPOSE: White matter hyperintensities in brain MRI are key indicators of various neurological conditions, and their accurate segmentation is essential for assessing disease progression. This study aims to evaluate the performance of a...
Explainable artificial intelligence with UNet based segmentation and Bayesian machine learning for classification of brain tumors using MRI images.
Scientific reports
Jan 3, 2025
Detecting brain tumours (BT) early improves treatment possibilities and increases patient survival rates. Magnetic resonance imaging (MRI) scanning offers more comprehensive information, such as better contrast and clarity, than any alternative scann...
An end-to-end implicit neural representation architecture for medical volume data.
PloS one
Jan 3, 2025
Medical volume data are rapidly increasing, growing from gigabytes to petabytes, which presents significant challenges in organisation, storage, transmission, manipulation, and rendering. To address the challenges, we propose an end-to-end architectu...
Preoperative discrimination of absence or presence of myometrial invasion in endometrial cancer with an MRI-based multimodal deep learning radiomics model.
Abdominal radiology (New York)
Jan 2, 2025
OBJECTIVE: Accurate preoperative evaluation of myometrial invasion (MI) is essential for treatment decisions in endometrial cancer (EC). However, the diagnostic accuracy of commonly utilized magnetic resonance imaging (MRI) techniques for this assess...
Radiomics and Deep Learning Model for Benign and Malignant Soft Tissue Tumors Differentiation of Extremities and Trunk.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
An Automatic Deep-Radiomics Framework for Prostate Cancer Diagnosis and Stratification in Patients with Serum Prostate-Specific Antigen of 4.0-10.0 ng/mL: A Multicenter Retrospective Study.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Automatic Segmentation of Vestibular Schwannoma From MRI Using Two Cascaded Deep Learning Networks.
The Laryngoscope
Jan 2, 2025
OBJECTIVE: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of V...
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