Volumetric optical microscopy using non-diffracting beams enables rapid
imaging of 3D volumes by projecting them axially to 2D images but lacks crucial
depth information. Addressing this, we introduce MicroDiffusion, a pioneering
tool facilitating high-quality, depth-resolved 3D volume reconstruction from
limited 2D projections. While existing Implicit Neural Representation (INR)
models often yield incomplete outputs and Denoising Diffusion Probabilistic
Models (DDPM) excel at capturing details, our method integrates INR's
structural coherence with DDPM's fine-detail enhancement capabilities. We
pretrain an INR model to transform 2D axially-projected images into a
preliminary 3D volume. This pretrained INR acts as a global prior guiding
DDPM's generative process through a linear interpolation between INR outputs
and noise inputs. This strategy enriches the diffusion process with structured
3D information, enhancing detail and reducing noise in localized 2D images. By
conditioning the diffusion model on the closest 2D projection, MicroDiffusion
substantially enhances fidelity in resulting 3D reconstructions, surpassing INR
and standard DDPM outputs with unparalleled image quality and structural
fidelity. Our code and dataset are available at
this https URL

通过使用非衍射光束进行体积光学显微镜法，我们提出了 MicroDiffusion 工具，用于从有限的 2D 投影中进行高质量的、深度解析的 3D 体积重建。该方法融合了 INR 模型的结构连贯性和 DDPM 模型的细节增强能力，通过将 INR 和噪声输入之间的线性插值来指导 DDPM 的生成过程，从而提高了扩散过程的结构化 3D 信息，增强了本地化 2D 图像的细节，并在生成的 3D 重建中显著提高了保真度。

微扩散：由有限 2D 显微投影重建的隐式表示引导的 3D 重建

MicroDiffusion: Implicit Representation-Guided Diffusion for 3D  Reconstruction from Limited 2D Microscopy Projections

Myocardial motion tracking stands as an essential clinical tool in the
prevention and detection of Cardiovascular Diseases (CVDs), the foremost cause
of death globally. However, current techniques suffer incomplete and inaccurate
motion estimation of the myocardium both in spatial and temporal dimensions,
hindering the early identification of myocardial dysfunction. In addressing
these challenges, this paper introduces the Neural Cardiac Motion Field
(NeuralCMF). NeuralCMF leverages the implicit neural representation (INR) to
model the 3D structure and the comprehensive 6D forward/backward motion of the
heart. This approach offers memory-efficient storage and continuous capability
to query the precise shape and motion of the myocardium throughout the cardiac
cycle at any specific point. Notably, NeuralCMF operates without the need for
paired datasets, and its optimization is self-supervised through the physics
knowledge priors both in space and time dimensions, ensuring compatibility with
both 2D and 3D echocardiogram video inputs. Experimental validations across
three representative datasets support the robustness and innovative nature of
the NeuralCMF, marking significant advantages over existing state-of-the-arts
in cardiac imaging and motion tracking.

该研究提出了一种名为神经心脏运动场 (NeuralCMF) 的新方法，它通过内隐神经表示 (INR) 模拟心脏的三维结构和全面的六维前后运动，有效解决了目前心肌运动追踪方法在空间和时间维度上不准确的问题，具有在 2D 和 3D 超声心动图视频输入中应用的优势。