MRI is a widely used ionization-free soft-tissue imaging modality, often
employed repeatedly over a patient's lifetime. However, prolonged scanning
durations, among other issues, can limit availability and accessibility. In
this work, we aim to substantially reduce scan times by leveraging prior scans
of the same patient. These prior scans typically contain considerable shared
information with the current scan, thereby enabling higher acceleration rates
when appropriately utilized. We propose a prior informed reconstruction method
with a trained diffusion model in conjunction with data-consistency steps. Our
method can be trained with unlabeled image data, eliminating the need for a
dataset of either k-space measurements or paired longitudinal scans as is
required of other learning-based methods. We demonstrate superiority of our
method over previously suggested approaches in effectively utilizing prior
information without over-biasing prior consistency, which we validate on both
an open-source dataset of healthy patients as well as several longitudinal
cases of clinical interest.

基于已有扫描结果的 MRI 先验信息重建方法以及扩散模型的训练方法，能够有效缩短扫描时间并提高利用先验信息的效果。

利用先验信息的潜在扩散加速纵向磁共振成像

Accelerating Longitudinal MRI using Prior Informed Latent Diffusion

Magnetic Resonance (MR) imaging, despite its proven diagnostic utility,
remains an inaccessible imaging modality for disease surveillance at the
population level. A major factor rendering MR inaccessible is lengthy scan
times. An MR scanner collects measurements associated with the underlying
anatomy in the Fourier space, also known as the k-space. Creating a
high-fidelity image requires collecting large quantities of such measurements,
increasing the scan time. Traditionally to accelerate an MR scan, image
reconstruction from under-sampled k-space data is the method of choice.
However, recent works show the feasibility of bypassing image reconstruction
and directly learning to detect disease directly from a sparser learned subset
of the k-space measurements. In this work, we propose Adaptive Sampling for MR
(ASMR), a sampling method that learns an adaptive policy to sequentially select
k-space samples to optimize for target disease detection. On 6 out of 8
pathology classification tasks spanning the Knee, Brain, and Prostate MR scans,
ASMR reaches within 2% of the performance of a fully sampled classifier while
using only 8% of the k-space, as well as outperforming prior state-of-the-art
work in k-space sampling such as EMRT, LOUPE, and DPS.

MR 成像通过 Adaptive Sampling for MR 原理允许在只使用 8% 的 k 空间的情况下，优化目标疾病检测并达到仅差 2% 于完全采样分类器的性能，同时优于 EMRT、LOUPE 和 DPS 等以往 k 空间采样的最新研究成果。