  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.
