Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20×\times Speedup

Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equation (ODE) solver for a much-improved sampling efficiency. The experiments show that the accelerated DDPM can achieve 20x speedup without compromising image quality

Bilevel optimization, deep learning and fractional Laplacian regularization with applications in tomography

The article of record as published may be located at https://doi.org/10.1088/1361-6420/ab80d7Funded by Naval Postgraduate SchoolIn this work we consider a generalized bilevel optimization framework for solv- ing inverse problems. We introduce fractional Laplacian as a regularizer to improve the reconstruction quality, and compare it with the total variation regularization. We emphasize that the key advantage of using fractional Laplacian as a regularizer is that it leads to a linear operator, as opposed to the total varia- tion regularization which results in a nonlinear degenerate operator. Inspired by residual neural networks, to learn the optimal strength of regularization and the exponent of fractional Laplacian, we develop a dedicated bilevel opti- mization neural network with a variable depth for a general regularized inverse problem. We illustrate how to incorporate various regularizer choices into our proposed network. As an example, we consider tomographic reconstruction as a model problem and show an improvement in reconstruction quality, especially for limited data, via fractional Laplacian regularization. We successfully learn the regularization strength and the fractional exponent via our proposed bilevel optimization neural network. We observe that the fractional Laplacian regular- ization outperforms total variation regularization. This is specially encouraging, and important, in the case of limited and noisy data.The first and third authors are partially supported by NSF grants DMS-1818772, DMS-1913004, the Air Force Office of Scientific Research under Award No.: FA9550-19-1-0036, and the Department of Navy, Naval PostGraduate School under Award No.: N00244-20-1-0005. The third author is also partially supported by a Provost award at George Mason University under the Industrial Immersion Program. The second author is partially supported by DOE Office of Science under Contract No. DE-AC02-06CH11357.The first and third authors are partially supported by NSF grants DMS-1818772, DMS-1913004, the Air Force Office of Scientific Research under Award No.: FA9550-19-1-0036, and the Department of Navy, Naval PostGraduate School under Award No.: N00244-20-1-0005. The third author is also partially supported by a Provost award at George Mason University under the Industrial Immersion Program. The second author is partially supported by DOE Office of Science under Contract No. DE-AC02-06CH11357

Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks

Noise and motion artifacts in Positron emission tomography (PET) scans can interfere in diagnosis and result in inaccurate interpretations. PET gating techniques effectively reduce motion blurring, but at the cost of increasing noise, as only a subset of the data is used to reconstruct the image. Deep convolutional neural networks (DCNNs) could complement gating techniques by correcting such noise. However, there is little research on the specific application of DCNNs to gated datasets, which present additional challenges that are not considered in these studies yet, such as the varying level of noise depending on the gate, and performance pitfalls due to changes in the noise properties between non-gated and gated scans. To extend the current status of artificial intelligence (AI) in gated-PET imaging, we present a post-reconstruction denoising approach based on U-Net architectures on cardiac dual-gated PET images obtained from 40 patients. To this end, we first evaluate the denoising performance of four different variants of the U-Net architecture (2D, semi-3D, 3D, Hybrid) on non-gated data to better understand the advantages of each type of model, and to shed more light on the factors to take in consideration when selecting a denoising architecture. Then, we tackle the denoising of gated-PET reconstructions, revising challenges and limitations, and propose two training approaches, which overcome the need for gated targets. Quantification results show that the proposed deep learning (DL) frameworks can successfully reduce noise levels while correctly preserving the original motionless resolution of the gates.</p