AIMC Topic: Magnetic Resonance Imaging

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More slices, less truth: effects of different test-set design strategies for magnetic resonance image classification.

Croatian medical journal
AIM: To assess the effects of different test-set design strategies for magnetic resonance (MR) image classification using deep learning.

Automatic quantification and grading of hip bone marrow oedema in ankylosing spondylitis based on deep learning.

Modern rheumatology
OBJECTIVE: This study has developed a new automatic algorithm for the quantificationy and grading of ankylosing spondylitis (AS)-hip arthritis with magnetic resonance imaging (MRI).

Deep Learning for Synthetic CT from Bone MRI in the Head and Neck.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Bone MR imaging techniques enable visualization of cortical bone without the need for ionizing radiation. Automated conversion of bone MR imaging to synthetic CT is highly desirable for downstream image processing and eventual...

Deep Learning Prediction and Visualization of Gender Related Brain Changes from Longitudinal Structural MRI Data in the ABCD Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning algorithms for predicting from neuroimaging data have shown considerable promise. Deep learning models that take advantage of the data's 3D structure have been proven to outperform ordinary machine learning on a number of learning tasks...

A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 dif...

Deep Learning using Pre-Brachytherapy MRI to Automatically Predict Applicator Induced Complex Uterine Deformation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy by the presence of an intrauterine (tandem)/intravaginal (ring) applicator during brachytherapy (BT...

Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Dynamic contrast enhanced (DCE) MRI acquires a series of images following the administration of a contrast agent, and plays an important clinical role in diagnosing various diseases. DCE MRI typically necessitates rapid imaging to provide sufficient ...

An MR-Conditional Needle Driver for Robot-Assisted Spinal Injections: Design Modifications and Evaluations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper introduces design modifications to our MR-Conditional, 2-degree-of-freedom (DOF), remotely-actuated needle driver for MRI-guided spinal injections. The new needle driver should better meet cleaning and sterilization guidelines needed for r...

MSGAN: Multi-Stage Generative Adversarial Networks for Cross-Modality Domain Adaptation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for cross-modality medical images. In this work, we present a ne...

nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-th...