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

Redundant

語(yǔ)源學(xué)

Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscormed to recover the image. Much work has gone into optimizing the sensing and reconstruction portions separately. We propose a method of jointly optimizing both sensing and reconstruction end-to-end under a total measurement constraint, enabling learning of the optimal sensing scheme concurrently w

粘

啟發(fā)

Generator Versus Segmentor: Pseudo-healthy Synthesisgical one. Recent approaches based on Generative Adversarial Network (GAN) have been developed for this task. However, these methods will inevitably fall into the trade-off between preserving the subject-specific identity and generating healthy-like appearances. To overcome this challenge, we propos

緊張過(guò)度

Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprintingructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e.g., . and . relaxation times). In this paper, we introduce a novel end-to-end deep learning framework to seamlessly map the tissue

opinionated

大量殺死

RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusingand complex movements. Designing such an optical system is constrained by the inherent trade-off among resolution, speed, and noise which comes from the limited number of photons that can be collected. In this paper, we propose a recursive light propagation network (RLP-Net) that infers the 3-D volu

FAZE

0302-9743 ational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*.The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are org

DAMP

InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reductionnly consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in ..

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