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Breast cancer is the most prevalent cancer among women worldwide, and early detection is crucial for reducing its mortality rate and improving quality of life. Dedicated breast computed tomography (CT) scanners offer better image quality than mammography and tomosynthesis in general but at higher radiation dose. To enable breast CT for cancer screening, the challenge is to minimize the radiation dose without compromising image quality, according to the ALARA principle (as low as reasonably achievable). Over the past years, deep learning has shown remarkable successes in various tasks, including low-dose CT especially few-view CT. Currently, the diffusion model presents the state of the art for CT reconstruction. To develop the first diffusion model-based breast CT reconstruction method, here we report innovations to address the large memory requirement for breast cone-beam CT reconstruction and high computational cost of the diffusion model. Specifically, in this study we transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction in projection and image domains. This novel approach involves the concurrent training of two distinct DDPM models dedicated to processing projection and image data synergistically in the dual domains. Our experimental findings reveal that this method delivers competitive reconstruction performance at half to one-third of the standard radiation doses. This advancement demonstrates an exciting potential of diffusion-type models for volumetric breast reconstruction at high-resolution with much-reduced radiation dose and as such hopefully redefines breast cancer screening and diagnosis.

Two photons can annihilate into a neutral pion-like resonance via the anomaly coupling, just like $\pi^0\gamma\gamma$ in QCD. In some strongly interacting electroweak symmetry breaking models, e.g., technicolor type models, there often exist neutral pion-like resonances. TeV photon colliders have a strong capability to discover such particles, because the standard model background in photon scattering goes through box diagrams and is therefore highly suppressed. In this study, we perform a signal-background comparison. We show that $e^+ e^-$ linear colliders running in $\gamma\gamma$ mode can discover such neutral-pion-like resonances with a decent sensitivity., Comment: 10 pages, better figures, added discussion and references