Functional MRI (fMRI) is an important tool for non-invasive studies of brain
function. Over the past decade, multi-echo fMRI methods that sample multiple
echo times has become popular with potential to improve quantification. While
these acquisitions are typically performed with Cartesian trajectories,
non-Cartesian trajectories, in particular spiral acquisitions, hold promise for
denser sampling of echo times. However, such acquisitions require very high
acceleration rates for sufficient spatiotemporal resolutions. In this work, we
propose to use a physics-driven deep learning (PD-DL) reconstruction to
accelerate multi-echo spiral fMRI by 10-fold. We modify a self-supervised
learning algorithm for optimized training with non-Cartesian trajectories and
use it to train the PD-DL network. Results show that the proposed
self-supervised PD-DL reconstruction achieves high spatio-temporal resolution
with meaningful BOLD analysis.

本文提出一种基于物理驱动的深度学习重建技术来加速多回波螺旋 fMRI，从而实现高时空分辨率和有意义的 BOLD 分析。

非笛卡尔自监督物理驱动深度学习重建高加速多回波螺旋磁共振成像

Non-Cartesian Self-Supervised Physics-Driven Deep Learning  Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI

Over the past few years, monocular depth estimation and completion have been
paid more and more attention from the computer vision community because of
their widespread applications. In this paper, we introduce novel physics
(geometry)-driven deep learning frameworks for these two tasks by assuming that
3D scenes are constituted with piece-wise planes. Instead of directly
estimating the depth map or completing the sparse depth map, we propose to
estimate the surface normal and plane-to-origin distance maps or complete the
sparse surface normal and distance maps as intermediate outputs. To this end,
we develop a normal-distance head that outputs pixel-level surface normal and
distance. Meanwhile, the surface normal and distance maps are regularized by a
developed plane-aware consistency constraint, which are then transformed into
depth maps. Furthermore, we integrate an additional depth head to strengthen
the robustness of the proposed frameworks. Extensive experiments on the
NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds
in performance prior state-of-the-art monocular depth estimation and completion
competitors. The source code will be available at
this https URL

通过假设 3D 场景由分段平面组成，本文提出了针对单目深度估计和补全任务的新型物理（几何）推动深度学习框架。我们通过估计表面法线和平面到原点的距离图或补全稀疏表面法线和距离图作为中间输出，提出了一个法线 - 距离模块。同时，通过开发出平面感知一致性约束来规范化表面法线和距离图，并将其转换为深度图。此外，还集成了一个额外的深度模块来增强所提出框架的鲁棒性。在 NYU-Depth-v2、KITTI 和 SUN RGB-D 数据集上的大量实验表明，我们的方法在单目深度估计和补全任务方面胜过了先前的最先进竞争方法。