arXiv:2608. 01839v1 Announce Type: new Abstract: Transcranial focused ultrasound (tFUS) requires accurate estimation of the intracranial acoustic field, which is distorted by skull-induced aberrations.
By Minjee Seo, Haris Ghafoor, Minju Seol, Seonaeng Cho, Kyungho Yoon
arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.
By Masashi Sode, Gianmarco Pinton
Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly constrain null-space information. Existing learnin...
arXiv:2609.13969v1 Announce Type: new
Abstract: Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in sub...
By Wen-Chun Lin, Yu-Chee Tseng, Jen-Jee Chen, Nan-You Chen
arXiv:2609.13688v1 Announce Type: cross
Abstract: Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but remains severely ill-posed because incomplete projections poorly...
By Song Ni, Haijun Yu, Haodong Li, Changsheng Fang, Shuyi Fan, Yixing Huang, Hengyong Yu
MIGA is a scan‑specific framework for accelerated 3D multi‑echo MRI that uses shared anisotropic Gaussian geometry, a coordinate‑conditioned multi‑output amplitude network, and explicit echo‑specific phase variables. The method jointly optimizes all components from undersampled multi‑coil k‑space data without requiring fully sampled training data. Experiments demonstrate that MIGA outperforms existing methods across various imaging tasks and acceleration factors, especially under stronger undersampling, and offers a favorable quality‑cost balance among full‑volume multi‑echo reconstruction techniques.
By Jingran Xu, Yuanyuan Liu, Yanjie Zhu