AIMC Topic:
Magnetic Resonance Imaging
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Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3.
Journal of magnetic resonance imaging : JMRI
Feb 16, 2024
BACKGROUND: For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required.
Aged
Male
Middle Aged
Magnetic Resonance Imaging
Prostatic Neoplasms
Risk Assessment
ROC Curve
Humans
Machine Learning
Retrospective Studies
Reproducibility of Results
Image Interpretation, Computer-Assisted
Prostate-Specific Antigen
Diffusion Magnetic Resonance Imaging
Prostate
Echo-Planar Imaging
Biopsy
Automated Patient Registration in Magnetic Resonance Imaging Using Deep Learning-Based Height and Weight Estimation with 3D Camera: A Feasibility Study.
Academic radiology
Feb 16, 2024
RATIONALE AND OBJECTIVES: Accurate and efficient estimation of patient height and weight is crucial to ensure patient safety and optimize the quality of magnetic resonance imaging (MRI) procedures. Several height and weight estimation methods have be...
Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.
Journal of magnetic resonance imaging : JMRI
Feb 15, 2024
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution...
Attenuation correction and truncation completion for breast PET/MR imaging using deep learning.
Physics in medicine and biology
Feb 15, 2024
. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust atte...
Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress.
Scientific reports
Feb 14, 2024
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed...
A machine-learning model based on dynamic contrast-enhanced MRI for preoperative differentiation between hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma.
Clinical radiology
Feb 13, 2024
AIM: To establish a machine-learning model based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from hepatocellular carcinoma (HCC) before surgery.
Preliminary data on artificial intelligence tool in magnetic resonance imaging assessment of degenerative pathologies of lumbar spine.
La Radiologia medica
Feb 13, 2024
PURPOSE: To evaluate the ability of an artificial intelligence (AI) tool in magnetic resonance imaging (MRI) assessment of degenerative pathologies of lumbar spine using radiologist evaluation as a gold standard.
Deep learning-accelerated image reconstruction in back pain-MRI imaging: reduction of acquisition time and improvement of image quality.
La Radiologia medica
Feb 13, 2024
INTRODUCTION: Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intellige...
Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.
Fluids and barriers of the CNS
Feb 13, 2024
BACKGROUND: Peri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunc...
Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.
BMC medical imaging
Feb 13, 2024
BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase ...
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