PURPOSE: Deep learning (DL) has shown promise in enabling attenuation correction (AC) for SPECT myocardial perfusion imaging (MPI) without relying on anatomical information or CT-derived attenuation maps (ATMs). We introduce a novel reconstruction-informed and multidomain (RIMD) DL framework utilizing both multi-input reconstruction and dual-domain supervision for AC in SPECT MPI. Our method incorporates multi-input non-AC (NAC) images as input to the DL model and employs a combined loss function that optimizes performance in both the ATM and AC domains. METHODS: Tc-Sestamibi from two centers was used for training (934 cases) and an external test set (124 cases). SPECT projections were reconstructed into AC and NAC images using three reconstruction settings of the Ordered Subset - Expectation Maximization (OSEM) algorithm. SwinUnetR model was trained in a consistent 5-fold cross-validation framework with normalized NAC images as input. Our proposed method, as an indirect strategy, uses multi-input NAC images (incorporating all three images with different reconstruction settings) trained using a combination of ATM loss (between predicted and true ATMs) and AC loss (between AC images reconstructed from predicted ATMs and reference AC images). Evaluation included voxel-wise and region-wise metrics for both ATM and AC domains, 17-segment polar map analysis, and an organ-specific analysis. Clinical validation was also performed on part of the external dataset. RESULTS: Our proposed approach significantly outperformed direct and indirect methods in ablation comparison. This model yielded mean relative absolute error percentage (MRAE%) values of 25.02 ± 23 (internal) and 26.31 ± 14 (external) for ATMs, and 11.72 ± 6.3 (internal) and 19.31 ± 4.9 (external) for AC SPECT images. Organ-wise analysis showed region-wise MRAE% of 9.29 ± 6.5 (internal) and 17.28 ± 17 (external) in the ATM domain, and 4.51 ± 4.3 (internal) and 9.53 ± 6.6 (external) in the AC domain. Polar map analysis across 17 segments showed MRAE% of 5.04 ± 4.6 (internal) and 10.88 ± 7.3 (external). Clinical validation demonstrated high agreement between DLAC and CTAC images (ICC = 0.98), with physicians unable to distinguish between them (F1 score = 0.40), and no significant difference in diagnostic accuracy (DLAC: 0.63, CTAC: 0.70; p = 0.37). CONCLUSION: This study demonstrated that our proposed RIMD method utilizing multiple OSEM reconstruction inputs and jointly optimizing ATM and AC losses substantially improved model performance in the indirect strategy. The indirect method consistently outperformed the direct approach, and our model generalized well on external data, showing strong agreement with SPECT CTAC images in both quantitative and qualitative assessments. Preliminary clinical evaluation suggested comparable interpretability between DLAC and CTAC under controlled validation conditions.

OBJECTIVE: The primary objective of our study is to address the challenge of confidentially sharing medical images across different centers. This is often a critical necessity in both clinical and research environments, yet restrictions typically exist due to privacy concerns. Our aim is to design a privacy-preserving data-sharing mechanism that allows medical images to be stored as encoded and obfuscated representations in the public domain without revealing any useful or recoverable content from the images. In tandem, we aim to provide authorized users with compact private keys that could be used to reconstruct the corresponding images. METHOD: Our approach involves utilizing a neural auto-encoder. The convolutional filter outputs are passed through sparsifying transformations to produce multiple compact codes. Each code is responsible for reconstructing different attributes of the image. The key privacy-preserving element in this process is obfuscation through the use of specific pseudo-random noise. When applied to the codes, it becomes computationally infeasible for an attacker to guess the correct representation for all the codes, thereby preserving the privacy of the images. RESULTS: The proposed framework was implemented and evaluated using chest X-ray images for different medical image analysis tasks, including classification, segmentation, and texture analysis. Additionally, we thoroughly assessed the robustness of our method against various attacks using both supervised and unsupervised algorithms. CONCLUSION: This study provides a novel, optimized, and privacy-assured data-sharing mechanism for medical images, enabling multi-party sharing in a secure manner. While we have demonstrated its effectiveness with chest X-ray images, the mechanism can be utilized in other medical images modalities as well.

It has recently been shown that the attenuation map can be estimated from time-of-flight (TOF) PET emission data using joint maximum likelihood reconstruction of attenuation and activity (MLAA). In this work, we propose a novel MRI-guided MLAA algorithm for emission-based attenuation correction in whole-body PET/MR imaging. The algorithm imposes MR spatial and CT statistical constraints on the MLAA estimation of attenuation maps using a constrained Gaussian mixture model (GMM) and a Markov random field smoothness prior. Dixon water and fat MR images were segmented into outside air, lung, fat and soft-tissue classes and an MR low-intensity (unknown) class corresponding to air cavities, cortical bone and susceptibility artifacts. The attenuation coefficients over the unknown class were estimated using a mixture of four Gaussians, and those over the known tissue classes using unimodal Gaussians, parameterized over a patient population. To eliminate misclassification of spongy bones with surrounding tissues, and thus include them in the unknown class, we heuristically suppressed fat in water images and also used a co-registered bone probability map. The proposed MLAA-GMM algorithm was compared with the MLAA algorithms proposed by Rezaei and Salomon using simulation and clinical studies with two different tracer distributions. The results showed that our proposed algorithm outperforms its counterparts in suppressing the cross-talk and scaling problems of activity and attenuation and thus produces PET images of improved quantitative accuracy. It can be concluded that the proposed algorithm effectively exploits the MR information and can pave the way toward accurate emission-based attenuation correction in TOF PET/MRI.

X-ray computed tomography (CT) imaging of patients with metallic implants usually suffers from streaking metal artifacts. In this paper, we propose a new projection completion metal artifact reduction (MAR) algorithm by formulating the completion of missing projections as a regularized inverse problem in the wavelet domain. The Douglas-Rachford splitting (DRS) algorithm was used to iteratively solve the problem. Two types of prior information were exploited in the algorithm: 1) the sparsity of the wavelet coefficients of CT sinograms in a dictionary of translation-invariant wavelets and 2) the detail wavelet coefficients of a prior sinogram obtained from the forward projection of a segmented CT image. A pseudo- L0 synthesis prior was utilized to exploit and promote the sparsity of wavelet coefficients. The proposed L0-DRS MAR algorithm was compared with standard linear interpolation and the normalized metal artifact reduction (NMAR) approach proposed by Meyer using both simulated and clinical studies including hip prostheses, dental fillings, spine fixation and electroencephalogram electrodes in brain imaging. The qualitative and quantitative evaluations showed that our algorithm substantially suppresses streaking artifacts and can outperform both linear interpolation and NMAR algorithms.

The impact of PET on radiation therapy is held back by poor methods of defining functional volumes of interest. Many new software tools are being proposed for contouring target volumes but the different approaches are not adequately compared and their accuracy is poorly evaluated due to the illdefinition of ground truth. This paper compares the largest cohort to date of established, emerging and proposed PET contouring methods, in terms of accuracy and variability. We emphasise spatial accuracy and present a new metric that addresses the lack of unique ground truth. 30 methods are used at 13 different institutions to contour functional VOIs in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring methods are grouped according to algorithmic type, level of interactivity and how they exploit structural information in hybrid images. Experiments reveal benefits of high levels of user interaction, as well as simultaneous visualisation of CT images and PET gradients to guide interactive procedures. Method-wise evaluation identifies the danger of over-automation and the value of prior knowledge built into an algorithm.

Conventional tomographies (e.g. X-ray scanner, Positron Emission Tomography), functioning on primary radiation are modeled by the Radon transform (RT) which is an integral transform defined on straight lines. In this paper, we consider a new RT defined on a pair of half-lines forming a letter V, which arises from modeling two-dimensional emission imaging process by Compton scattered gamma rays. We give its definition and establish its analytic inverse to support the feasibility of the reconstruction of a two-dimensional (2D) image from scattered radiation measured on a one-dimensional collimated camera. Moreover, a filtered back-projection inversion method is also constructed. Its main advantages are algorithmic efficiency and computational rapidity. We present numerical simulations to illustrate the working. To sum up, the V-line RT leads not only to a new imaging principle, but also to a new concept of detector with high energetic resolution capable to collect the scattered radiation.

The increasing numbers of patient scans and the prevailing application of positron emission tomography (PET) in clinical oncology have led to a need for efficient PET volume handling and the development of new volume analysis approaches to aid clinicians in the diagnosis of disease and planning of treatment. A novel automated system for oncological PET volume segmentation is proposed in this paper. The proposed intelligent system is using competitive neural network (CNN) and learning vector quantisation neural network (LVQNN) for clustering and quantifying phantom and real PET volumes. Bayesian information criterion (BIC) has been used in this system to assess the optimal number of clusters for each PET data set. The experimental study using phantom PET volume was conducted for quantitative evaluation of the performance of the proposed segmentation algorithm. The analysis of the resulting segmentation of clinical oncological PET data seems to confirm that this approach shows promise and can successfully segment patient lesion.

Tumour classification and quantification in positron emission tomography (PET) imaging at early stage of illness are important for radiotherapy planning, tumour diagnosis, and fast recovery. Analysing large medical volumes using traditional techniques requires a decent amount of time, and in some approaches poor accuracy is achieved. Artificial intelligence (AI) technologies can provide better accuracy and save decent amount of time. Artificial neural network (ANN), as one of the best AI technologies, has the capability to classify, measure precisely the region of interest, and model the clinical evaluation for a specific problem. This paper presents a novel application of the ANN in the wavelet domain for PET volume segmentation. ANN performance evaluation using different number of hidden neurons is also considered. The proposed intelligent system outputs are compared with the outputs of thresholding, and clustering based approaches. Two PET phantom data sets and real PET volumes have been utilised to validate the proposed system which has shown promising results.

The widespread availability of high-performance computing and accurate and realistic computer simulation techniques has stimulated the development of computational anthropomorphic models of both the anatomy and physiological functions of humans and laboratory animals. These simulation tools have been applied to different medical imaging modalities including ultrasound, single photon emission computed tomography, positron emission tomography, X-ray computed tomography, magnetic resonance imaging, optical imaging, and multimodality imaging with various combinations of the above. This paper reviews the fundamental and technical challenges and future directions of developing computational models of normal and abnormal human anatomy and physiological functions, with a particular focus on their applications to biomedical imaging and radiation dosimetry calculations. The combination of accurate and realistic computer generated models of human and laboratory animals, radiation sources and distributions, transport of radiation through biological tissues, characteristics of the imaging system, and physics of the image formation process allows accurate and realistic simulation of biomedical imaging data and radiation dose distributions that are ever closer to those obtained from clinical and experimental laboratory animal studies. These simulation tools and techniques will provide an increasingly important contribution and impact in the future of biomedical imaging and dosimetry calculations.