Positron Emission Tomography (PET) is an important clinical imaging tool but
inevitably introduces radiation hazards to patients and healthcare providers.
Reducing the tracer injection dose and eliminating the CT acquisition for
attenuation correction can reduce the overall radiation dose, but often results
in PET with high noise and bias. Thus, it is desirable to develop 3D methods to
translate the non-attenuation-corrected low-dose PET (NAC-LDPET) into
attenuation-corrected standard-dose PET (AC-SDPET). Recently, diffusion models
have emerged as a new state-of-the-art deep learning method for image-to-image
translation, better than traditional CNN-based methods. However, due to the
high computation cost and memory burden, it is largely limited to 2D
applications. To address these challenges, we developed a novel 2.5D Multi-view
Averaging Diffusion Model (MADM) for 3D image-to-image translation with
application on NAC-LDPET to AC-SDPET translation. Specifically, MADM employs
separate diffusion models for axial, coronal, and sagittal views, whose outputs
are averaged in each sampling step to ensure the 3D generation quality from
multiple views. To accelerate the 3D sampling process, we also proposed a
strategy to use the CNN-based 3D generation as a prior for the diffusion model.
Our experimental results on human patient studies suggested that MADM can
generate high-quality 3D translation images, outperforming previous CNN-based
and Diffusion-based baseline methods.

使用新型的 2.5D 多视角平均扩散模型 (MADM) 进行 3D 图像转换，从非衰减校正低剂量 PET (NAC-LDPET) 到校正衰减标准剂量 PET (AC-SDPET)，实现高质量的 3D 图像翻译。

2.5D 多视角平均扩散模型用于 3D 医学图像翻译：应用于无 CT 衰减校正的低计数 PET 重建

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image  Translation: Application to Low-count PET Reconstruction with CT-less  Attenuation Correction

A large portion of volumetric medical data, especially magnetic resonance
imaging (MRI) data, is anisotropic, as the through-plane resolution is
typically much lower than the in-plane resolution. Both 3D and purely 2D deep
learning-based segmentation methods are deficient in dealing with such
volumetric data since the performance of 3D methods suffers when confronting
anisotropic data, and 2D methods disregard crucial volumetric information.
Insufficient work has been done on 2.5D methods, in which 2D convolution is
mainly used in concert with volumetric information. These models focus on
learning the relationship across slices, but typically have many parameters to
train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable
parameters, which captures information across all the slices in the volume by
applying semantic, positional, and slice attention on deep feature maps at
different scales. Our extensive experiments using different network
architectures and tasks demonstrate the usefulness and generalizability of
CSAM. Associated code is available at this https URL

通过跨切片注意力模块（CSAM）对体积数据中不同切片的深层特征图进行语义、位置和切片的关注，解决了处理异态体积数据时 3D 方法性能下降和 2D 方法忽略关键体积信息的问题。