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Found 8 papers, 3 papers with code
Towards a quantitative assessment of neurodegeneration in Alzheimer's disease
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that progressively destroys memory and other cognitive domains of the brain.
Towards learned optimal q-space sampling in diffusion MRI
Fiber tractography is an important tool of computational neuroscience that enables reconstructing the spatial connectivity and organization of white matter of the brain.
PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI
Such schemes have already demonstrated substantial effectiveness, leading to considerably shorter acquisition times and improved quality of image reconstruction.
Self-supervised learning of inverse problem solvers in medical imaging
In light of this, we propose a self-supervised approach to training inverse models in medical imaging in the absence of aligned data.
Joint learning of cartesian undersampling and reconstruction for accelerated MRI
On the other hand, recent works in optical computational imaging have demonstrated growing success of the simultaneous learning-based design of the acquisition and reconstruction schemes manifesting significant improvement in the reconstruction quality with a constrained time budget.
Learning beamforming in ultrasound imaging
Medical ultrasound (US) is a widespread imaging modality owing its popularity to cost efficiency, portability, speed, and lack of harmful ionizing radiation.
High frame-rate cardiac ultrasound imaging with deep learning
The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.
High quality ultrasonic multi-line transmission through deep learning
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography.
