Cine Magnetic Resonance Imaging (MRI) allows for understanding of the heart's
function and condition in a non-invasive manner. Undersampling of the $k$-space
is employed to reduce the scan duration, thus increasing patient comfort and
reducing the risk of motion artefacts, at the cost of reduced image quality. In
this challenge paper, we investigate the use of a convolutional recurrent
neural network (CRNN) architecture to exploit temporal correlations in
supervised cine cardiac MRI reconstruction. This is combined with a
single-image super-resolution refinement module to improve single coil
reconstruction by 4.4\% in structural similarity and 3.9\% in normalised mean
square error compared to a plain CRNN implementation. We deploy a high-pass
filter to our $\ell_1$ loss to allow greater emphasis on high-frequency details
which are missing in the original data. The proposed model demonstrates
considerable enhancements compared to the baseline case and holds promising
potential for further improving cardiac MRI reconstruction.

研究提出了一种卷积递归神经网络（CRNN）架构，用于利用时间相关性在监督影像的心脏磁共振成像重建中。该模型结合了单图像超分辨率细化模块，相比于普通 CRNN 的实现，其在结构相似性和标准化均方误差方面，使单线圈重建提高了 4.4％和 3.9％。通过在 l1 损失中使用高通滤波器，可以更好地强调原始数据中缺失的高频细节，从而证明了该模型相较于基准案例的显著改进，并具有进一步提高心脏磁共振成像重建的潜力。

使用卷积循环网络和优化技术的心脏 MRI 重建

Cine cardiac MRI reconstruction using a convolutional recurrent network  with refinement

Accurate 3D cardiac reconstruction from cine magnetic resonance imaging
(cMRI) is crucial for improved cardiovascular disease diagnosis and
understanding of the heart's motion. However, current cardiac MRI-based
reconstruction technology used in clinical settings is 2D with limited
through-plane resolution, resulting in low-quality reconstructed cardiac
volumes. To better reconstruct 3D cardiac volumes from sparse 2D image stacks,
we propose a morphology-guided diffusion model for 3D cardiac volume
reconstruction, DMCVR, that synthesizes high-resolution 2D images and
corresponding 3D reconstructed volumes. Our method outperforms previous
approaches by conditioning the cardiac morphology on the generative model,
eliminating the time-consuming iterative optimization process of the latent
code, and improving generation quality. The learned latent spaces provide
global semantics, local cardiac morphology and details of each 2D cMRI slice
with highly interpretable value to reconstruct 3D cardiac shape. Our
experiments show that DMCVR is highly effective in several aspects, such as 2D
generation and 3D reconstruction performance. With DMCVR, we can produce
high-resolution 3D cardiac MRI reconstructions, surpassing current techniques.
Our proposed framework has great potential for improving the accuracy of
cardiac disease diagnosis and treatment planning. Code can be accessed at
this https URL

通过形态导向扩散模型，我们提出了 DMCVR，用于从稀疏的 2D 图像堆叠中更好地重建 3D 心脏容积，这种方法在生成模型中将心脏形态作为条件，消除了潜在代码的耗时迭代优化过程，提高了生成质量。我们的实验表明，DMCVR 在 2D 生成和 3D 重建性能等几个方面非常有效，能够产生高分辨率的 3D 心脏 MRI 重建，超过了当前的技术水平。我们提出的框架有着改善心脏疾病诊断和治疗规划准确性的巨大潜力。

DMCVR: 基于形态学引导的扩散模型用于 3D 心脏容积重构

DMCVR: Morphology-Guided Diffusion Model for 3D Cardiac Volume  Reconstruction

Cine magnetic resonance imaging (MRI) is the current gold standard for the
assessment of cardiac anatomy and function. However, it typically only acquires
a set of two-dimensional (2D) slices of the underlying three-dimensional (3D)
anatomy of the heart, thus limiting the understanding and analysis of both
healthy and pathological cardiac morphology and physiology. In this paper, we
propose a novel fully automatic surface reconstruction pipeline capable of
reconstructing multi-class 3D cardiac anatomy meshes from raw cine MRI
acquisitions. Its key component is a multi-class point cloud completion network
(PCCN) capable of correcting both the sparsity and misalignment issues of the
3D reconstruction task in a unified model. We first evaluate the PCCN on a
large synthetic dataset of biventricular anatomies and observe Chamfer
distances between reconstructed and gold standard anatomies below or similar to
the underlying image resolution for multiple levels of slice misalignment.
Furthermore, we find a reduction in reconstruction error compared to a
benchmark 3D U-Net by 32% and 24% in terms of Hausdorff distance and mean
surface distance, respectively. We then apply the PCCN as part of our automated
reconstruction pipeline to 1000 subjects from the UK Biobank study in a
cross-domain transfer setting and demonstrate its ability to reconstruct
accurate and topologically plausible biventricular heart meshes with clinical
metrics comparable to the previous literature. Finally, we investigate the
robustness of our proposed approach and observe its capacity to successfully
handle multiple common outlier conditions.

该研究提出了一种全新的完全自动化表面重建管道，能够从原始的 Cine 磁共振成像获取多类三维心脏解剖网格，其中关键组件是一种多类点云完成网络（PCCN）；研究人员证明了该算法在合成数据集和实际数据集中的高精度和鲁棒性。