Automated Acquisition Planning for Magnetic Resonance Spectroscopy in Brain Cancer.
Patrick J BolanFrancesca BranzoliAnna Luisa Di StefanoLucia NichelliRomain ValabregueSara L SaundersMehmet AkçakayaMarc SansonStéphane LehéricyMalgorzata MarjańskaPublished in: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (2020)
In vivo magnetic resonance spectroscopy (MRS) can provide clinically valuable metabolic information from brain tumors that can be used for prognosis and monitoring response to treatment. Unfortunately, this technique has not been widely adopted in clinical practice or even clinical trials due to the difficulty in acquiring and analyzing the data. In this work we propose a computational approach to solve one of the most critical technical challenges: the problem of quickly and accurately positioning an MRS volume of interest (a cuboid voxel) inside a tumor using MR images for guidance. The proposed automated method comprises a convolutional neural network to segment the lesion, followed by a discrete optimization to position an MRS voxel optimally within the lesion. In a retrospective comparison, the novel automated method is shown to provide improved lesion coverage compared to manual voxel placement.
Keyphrases
- deep learning
- convolutional neural network
- machine learning
- clinical trial
- clinical practice
- artificial intelligence
- high throughput
- papillary thyroid
- resting state
- squamous cell
- magnetic resonance
- big data
- electronic health record
- multiple sclerosis
- randomized controlled trial
- functional connectivity
- contrast enhanced
- squamous cell carcinoma
- health information
- young adults
- replacement therapy
- computed tomography
- magnetic resonance imaging
- blood brain barrier
- study protocol
- lymph node metastasis
- affordable care act
- optical coherence tomography
- double blind