Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

geriatrician

流動(dòng)才波動(dòng)

Anguish

Synthesizing Multi-tracer PET Images for Alzheimer’s Disease Patients Using a 3D Unified Anatomy-Awaide molecular characterization of patients with cognitive disorders. However, multiple tracers are needed to measure glucose metabolism (.F-FDG), synaptic vesicle protein (.C-UCB-J), and .-amyloid (.C-PiB). Administering multiple tracers to patient will lead to high radiation dose and cost. In addit

玉米棒子

sphincter

TransCT: Dual-Path Transformer for Low Dose Computed Tomographyuce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompos

變色龍

IREM: High-Resolution Magnetic Resonance Image Reconstruction via Implicit Neural Representationw-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction. In this work, we suppose the desired HR image as an implicit continuous function of the 3D image spatial coordinate, and the thick-slice LR images as several sparse discrete samplings of this function.

Cultivate

DA-VSR: Domain Adaptable Volumetric Super-Resolution for Medical Imagesderstanding, increasing robustness in downstream tasks, etc. However, applying deep-learning-based SR approaches for clinical applications often encounters issues of domain inconsistency, as the test data may be acquired by different machines or on different organs. In this work, we present a novel

脆弱么

Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolations extremely small. Both analytical and iterative models need more projections for effective modeling. Deep learning methods have gained prevalence due to their excellent reconstruction performances, but such success is mainly limited within the same dataset and does not generalize across datasets wi

CLEAR

Fast Magnetic Resonance Imaging on?Regions of Interest: From Sensing to?Reconstructiontely. However, few existing methods study ROI in both data acquisition and image reconstruction when accelerating MRI by partial k-space measurements. Aiming at utilizing limited sampling resources efficiently on most relevant and desirable imaging contents in fast MRI, we propose a deep network fra

座右銘

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reductionom two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain netwo