AIMC Topic: Magnetic Resonance Imaging

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Breast MRI Segmentation and Ki-67 High- and Low-Expression Prediction Algorithm Based on Deep Learning.

Computational and mathematical methods in medicine Oct 4, 2022
RESULTS: The DSC, PPV, and sensitivity of our combined model are 0.94, 0.93, and 0.94, respectively, with better segmentation performance. And we compare with the segmentation frameworks of other papers and find that our combined model can make accur...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Ki-67 Antigen Female Humans Breast Neoplasms Algorithms
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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...
Humans Retrospective Studies Deep Learning Diffusion Magnetic Resonance Imaging Multiple Myeloma Whole Body Imaging Magnetic Resonance Imaging Bone Marrow
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MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation.

Medical & biological engineering & computing Oct 3, 2022
The precise segmentation of multimodal MRI images is the primary stage of tumor diagnosis and treatment. Current segmentation strategies often underutilize multiscale features, which can easily lead to loss of contextual information, reduction of low...
Magnetic Resonance Imaging Deep Learning Image Processing, Computer-Assisted Humans Brain Neoplasms Disease Progression Algorithms
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Ultrafast lumbar spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol.

Skeletal radiology Oct 1, 2022
OBJECTIVE: To evaluate the diagnostic equivalency between an ultrafast (1 min 53 s) lumbar MRI protocol using deep learning-based reconstruction and a conventional lumbar MRI protocol (12 min 31 s).
Humans Lumbar Vertebrae Signal-To-Noise Ratio Lumbosacral Region Deep Learning Magnetic Resonance Imaging
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Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM.

IEEE transactions on medical imaging Sep 30, 2022
OBJECTIVE: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a ...
Liver Neoplasms Carcinoma, Hepatocellular Neural Networks, Computer Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans
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Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

Physics in medicine and biology Sep 30, 2022
. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer's disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep le...
Positron-Emission Tomography Fluorodeoxyglucose F18 Neuroimaging Magnetic Resonance Imaging Alzheimer Disease Humans Deep Learning
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Two-Stage CNN Whole Heart Segmentation Combining Image Enhanced Attention Mechanism and Metric Classification.

Journal of digital imaging Sep 29, 2022
Accurate segmentation of multiple tissues and organs in cardiac medical imaging is of great value in computer-aided cardiovascular diagnosis. However, it is challenging due to the complex distribution of various tissues and organs in cardiac MRI (mag...
Diagnosis, Computer-Assisted Image Processing, Computer-Assisted Humans Neural Networks, Computer Magnetic Resonance Imaging Heart
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Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging.

AJNR. American journal of neuroradiology Sep 29, 2022
BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learni...
Neuroimaging Child Humans Magnetic Resonance Imaging Brain Deep Learning
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Estimation of Cerebral Blood Flow and Arterial Transit Time From Multi-Delay Arterial Spin Labeling MRI Using a Simulation-Based Supervised Deep Neural Network.

Journal of magnetic resonance imaging : JMRI Sep 28, 2022
BACKGROUND: An inherently poor signal-to-noise ratio (SNR) causes inaccuracy and less precision in cerebral blood flow (CBF) and arterial transit time (ATT) when using arterial spin labeling (ASL). Deep neural network (DNN)-based parameter estimation...
Reproducibility of Results Retrospective Studies Male Humans Cerebrovascular Circulation Female Neural Networks, Computer Magnetic Resonance Imaging Spin Labels
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Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection.

Abdominal radiology (New York) Sep 28, 2022
PURPOSE: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI...
Deep Learning Liver Neoplasms Magnetic Resonance Imaging Artifacts Image Processing, Computer-Assisted Humans
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