Mobile ependymoma diagnosed with cine MRI.
Ontology highlight
ABSTRACT:
SUBMITTER: Kotani T
PROVIDER: S-EPMC3931962 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Publications
Mobile ependymoma diagnosed with cine MRI.
Kotani Toshiaki T Okawa Akihiko A Akazawa Tsutomu T Sakuma Tsuyoshi T
BMJ case reports 20140219
Migration of neurogenic spinal tumours is uncommon. However, such possible mobility should be kept in mind during surgery for neurogenic tumours whenever the lesion is not found at the anticipated level. Conventional static imaging techniques, such as myelography and MRI, have not documented dynamic motion of tumours. A 12-year-old boy was diagnosed with a neurogenic spinal tumour in the thoracolumbar region. To assess the migratory tendency of the tumour, cine MRI was performed to acquire dynam ...[more]
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Project description:PurposeTo propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.MethodsWe propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions. The network is trained on fully sampled 2D cardiac cine datasets collected from 11 healthy volunteers with IRB approval. DL-ESPIRiT is compared against a state-of-the-art parallel imaging and compressed sensing method known as l1 -ESPIRiT. The reconstruction accuracy of both methods is evaluated on retrospectively undersampled datasets (R = 12) with respect to standard image quality metrics as well as automatic deep learning-based segmentations of left ventricular volumes. Feasibility of DL-ESPIRiT is demonstrated on two prospectively undersampled datasets acquired in a single heartbeat per slice.ResultsThe (2+1)D DL-ESPIRiT method produces higher fidelity image reconstructions when compared to l1 -ESPIRiT reconstructions with respect to standard image quality metrics (P < .001). As a result of improved image quality, segmentations made from (2+1)D DL-ESPIRiT images are also more accurate than segmentations from l1 -ESPIRiT images.ConclusionsDL-ESPIRiT synergistically combines a robust parallel imaging model and deep learning-based priors to produce high-fidelity reconstructions of retrospectively undersampled 2D cardiac cine data acquired with reduced FOV. Although a proof-of-concept is shown, further experiments are necessary to determine the efficacy of DL-ESPIRiT in prospectively undersampled data.
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