AIMC Topic: Magnetic Resonance Imaging

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Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorders by multiparametric quantitative MRI using convolutional neural network.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Multiple sclerosis and neuromyelitis optica spectrum disorders are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network model that differentiates between the two based on magnetic r...

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment).

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.

Journal of healthcare engineering
One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neura...

Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning.

Sensors (Basel, Switzerland)
Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions f...

Differentiating IDH status in human gliomas using machine learning and multiparametric MR/PET.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The purpose of this study was to develop a voxel-wise clustering method of multiparametric magnetic resonance imaging (MRI) and 3,4-dihydroxy-6-[F]-fluoro-L-phenylalanine (FDOPA) positron emission tomography (PET) images using an unsuperv...

Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.

Radiology
Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose To determine if synthesizing brain MRI scans using generative adversarial networks (GANs) allows for ...

DeepDose: a robust deep learning-based dose engine for abdominal tumours in a 1.5 T MRI radiotherapy system.

Physics in medicine and biology
We present a robust deep learning-based framework for dose calculations of abdominal tumours in a 1.5 T MRI radiotherapy system. For a set of patient plans, a convolutional neural network is trained on the dose of individual multi-leaf-collimator seg...

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Scientific reports
Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response...

Image-level supervised segmentation for human organs with confidence cues.

Physics in medicine and biology
Image segmentation for human organs is an important task for the diagnosis and treatment of diseases. Current deep learning-based methods are fully supervised and need pixel-level labels. Since the medical images are highly specialized and complex, t...

Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures.

Parkinsonism & related disorders
INTRODUCTION: Predictive biomarkers of Parkinson's Disease progression are needed to expedite neuroprotective treatment development and facilitate prognoses for patients. This work uses measures derived from resting-state functional magnetic resonanc...