AIMC Topic: Diffusion Magnetic Resonance Imaging
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Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey.
Physics in medicine and biology
Nov 11, 2022
Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the environment. Combining deep learning and reinforcement learning can empower the agent to learn the interac...
Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.
Investigative radiology
Oct 3, 2022
OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body...
Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Sep 28, 2022
Advances in deep learning can be applied to acute stroke imaging to build powerful and explainable prediction models that could supersede traditionally used biomarkers. We aimed to evaluate the performance and interpretability of a deep learning mode...
Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images.
Clinical radiology
Sep 15, 2022
AIM: To evaluate 1.5 T magnetic resonance imaging (MRI) brain images with denoising procedures using deep learning-based reconstruction (dDLR) relative to the original 1.5 and 3 T images.
Denoising diffusion weighted imaging data using convolutional neural networks.
PloS one
Sep 15, 2022
Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural...
Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation.
La Radiologia medica
Sep 7, 2022
PURPOSE: To compare liver MRI with AIR Recon Deep Learning™(ARDL) algorithm applied and turned-off (NON-DL) with conventional high-resolution acquisition (NAÏVE) sequences, in terms of quantitative and qualitative image analysis and scanning time.
Identifying acute ischemic stroke patients within the thrombolytic treatment window using deep learning.
Journal of neuroimaging : official journal of the American Society of Neuroimaging
Sep 6, 2022
BACKGROUND AND PURPOSE: Treatment of acute ischemic stroke is heavily contingent upon time, as there is a strong relationship between time clock and tissue progression. Work has established imaging biomarker assessments as surrogates for time since s...
Deep learning-guided weighted averaging for signal dropout compensation in DWI of the liver.
Magnetic resonance in medicine
Aug 2, 2022
PURPOSE: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.
Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging.
European journal of radiology
Jun 30, 2022
PURPOSE: To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI.
Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer.
Magnetic resonance imaging
Jun 27, 2022
PURPOSE: To assess the possibility of reducing the image acquisition time for diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) by denoising with deep learning-based reconstruction (dDLR).
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