コンピュータ断層撮影（ＣＴ）における機械学習画像再構成のための１つ以上の信頼度表示を決定するための方法及びシステムが提供される。この方法は、（Ｓ１）エネルギー分解Ｘ線データを取得することと、（Ｓ２）少なくとも１つの機械学習システムに基づいてエネルギー分解Ｘ線データを処理して、少なくとも１つの再構成基底画像又はその画像特徴の事後確率分布の表現を生成することとを備える。本方法は更に、事後確率分布の表現に基づいて、前記少なくとも１つの再構成基底画像、又は前記少なくとも１つの再構成基底画像に由来する少なくとも１つの派生画像、又は前記少なくとも１つの再構成基底画像又は前記少なくとも１つの派生画像の画像特徴に対する１つ以上の信頼度表示を生成する（Ｓ３）ことを含む。【選択図】図６Ａ

There is provided a method and corresponding system for image reconstruction based on energy-resolved x-ray data. The method comprises collecting (S1) at least one representation of energy-resolved x-ray data, and performing (S2) at least two basis material decompositions based on said at least one representation of energy-resolved x-ray data to generate at least two original basis image representation sets. The method further comprises obtaining or selecting (S3) at least two basis image representations from at least two of said original basis image representation sets, and processing (S4) said obtained or selected basis image representations by data processing based on machine learning to generate at least one representation of output image data.

Photon-counting detectors are greatly improving the resolution and image quality in computed tomography (CT) technology. The drawback is, however, that the reconstruction becomes more challenging. This is because there is a considerable increment of the processing data due to the multiple energy bins and materials in the reconstruction analysis, as well as improved resolution. Yet efficient material decomposition and reconstruction methods tend to generate noisy images that do not completely satisfy the expected image quality. Therefore, there is a need for efficient denoising of the resulting material images. We present a new and fast denoiser that is based on a linear minimum mean square error (LMMSE) estimator. The LMMSE is very fast to compute, but not commonly used for CT image denoising, probably due to its inability to adapt the amount of denoising to different parts of the image and the difficulty to derive accurate statistical properties from the CT data. To overcome these problems we propose a model-based deep learning strategy, that is, a deep neural network that preserves an LMMSE structure (model-based), providing more robustness unseen data, as well as good interpretability to the result. In this way, the solution adapts to the anatomy in every point of the image and noise properties at that particular location. In order to asses the performance of the new method, we compare it to both to a conventional LMMSE estimator and to a "black-box"CNN in a simulation study with anthropomorphic phantoms.

Photon-counting spectral CT is a novel technology with a lot of promise. However, one common issue is detector inhomogeneity which results in streak artifacts in the sinogram domain and ring artifacts in the image domain. These rings are very conspicuous and limit the clinical usefulness of the images. We propose a deep learning based image processing technique for ring artifact correction in the sinogram domain. In particular, we train a UNet using a perceptual loss function with VGG16 as feature extractor to remove streak artifacts in the basis sinograms. Our results show that this method can successfully produce ring-corrected virtual monoenergetic images at a range of energy levels. 

Photon-counting detectors (PCD) are the most recent advancement in computed tomography (CT). PCDs allow, among other things, for material decomposition, which decomposes the imaged object into a set of basis materials. Another field that is gaining attention, is the use of deep learning to improve the image reconstruction process in CT. In this work, we study the use of deep learning, specifically convolutional neural networks trained on the KiTS19 Challenge kidney data set, to improve the image quality of basis images resulting from three-material decomposition, a problem that is difficult due to its high sensitivity to noise. Our objective is to compare different network architectures and investigate whether these are best implemented in the projection domain or in the image domain. We study three different network architectures: U-Net, Dilated U-Net and ResNet, each applied in either the image domain or in the projection domain. The resulting image quality is evaluated in terms of contrast-to-noise ratio, task transfer function and noise power spectrum. Results show that for the type of phantoms the networks were trained on, the most effective option is to implement the network in the image domain and to use either the U-Net or Dilated U-Net architectures. However, when applying the networks to other phantoms, it seems that the networks in the sinogram generalize better, and produce better results. We also discuss why this might be the case, compare it with previous research, and consider what further improvements can be made.

Photon-Counting CT is an emerging imaging technology that promises higher spatial resolution and the possibility for material decomposition in the reconstruction. A major difficulty in Photon-Counting CT is to efficiently model cross-talk between detectors. In this work, we accelerate image reconstruction tasks for Photon-Counting CT by modelling the cross-talk with an appropriately trained deep convolutional neural network. The main result relates to proving convergence when using such a learned cross-talk model in the context of second-order optimisation methods for spectral CT. Another is to evaluate the method through numerical experiments on small-scale CT acquisitions generated using a realistic physics model. Using the reconstruction with a full cross-talk model as ground truth, the learned cross-talk model results in a 20 dB increase in peak-signal-to noise ratio compared to ignoring crass-talk altogether. At the same time, it effectively cuts the computation time of the full cross-talk model in half. Furthermore, the learned cross-talk model generalises well to both unseen data and unseen detector settings. Our results indicate that such a partially learned forward operator is a suitable way of modelling data generation in Photon-Counting CT with a computational benefit that becomes more noticeable for realistic problem sizes.

Photon counting detectors in x-ray computed tomography (CT) improve the decomposition of the CT scans into different materials. This decomposition is however not straightforward to solve, both in terms of computation expense and Photon counting detectors in x-ray computed tomography (CT) are a major technological advancement that provides additional energy information, and improve the decomposition of the CT image into material images. This material decomposition problem is however a non-linear inverse problem that is difficult to solve, both in terms of computation expense and accuracy. The most accepted solution consists in defining an optimization problem based on a maximum likelihood (ML) estimate with Poisson statistics, which is a model-based approach very dependent on the considered forward model and the chosen optimization solver. This may make the material decomposition result noisy and slow to be computed. To incorporate data-driven enhancement to the ML estimate, we propose a deep learning post-processing technique. Our approach is based on convolutional residual blocks that mimic the updates of an iterative optimization process and consider the ML estimate as an input. Therefore, our architecture implicitly considers the physical models of the problem, and in consequence needs less training data and fewer parameters than other standard convolutional networks typically used in medical imaging. We have studied a simulation case of our deep learning post-processing, first on a set of 350 Shepp-Logan -based phantoms, and then in a 600 human numerical phantoms. Our approach has shown denoising enhancement over two different ray-wise decomposition methods: one based on a Newton’s method to solve the ML estimation, and one based on a linear least-squares approximation of the ML expression. We believe this new deep learning post-processing approach is a promising technique to denoise material-decomposed sinograms in photon-counting CT.