Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement.

PURPOSE Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images. METHODS Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC). RESULTS The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging. CONCLUSION The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets

Comparable assessment of adolescent repeated physical or psychological stress effects on adult cardiac performance in female rats

Abstract Extensive evidence highlights a robust connection between various forms of chronic stress and cardiovascular disease (CVD). In today's fast-paced world, with chronic stressors abound, CVD has emerged as a leading global cause of mortality. The intricate interplay of physical and psychological stressors triggers distinct neural networks within the brain, culminating in diverse health challenges. This study aims to discern the unique impacts of chronic physical and psychological stress on the cardiovascular system, unveiling their varying potencies in precipitating CVD. Twenty-one adolescent female rats were methodically assigned to three groups: (1) control (n = 7), (2) physical stress (n = 7), and (3) psychological stress (n = 7). Employing a two-compartment enclosure, stressors were administered to the experimental rats over five consecutive days, each session lasting 10 min. After a 1.5-month recovery period post-stress exposure, a trio of complementary techniques characterized by high specificity or high sensitivity were employed to meticulously evaluate CVD. Echocardiography and single-photon emission computed tomography (SPECT) were harnessed to scrutinize left ventricular architecture and myocardial viability, respectively. Subsequently, the rats were ethically sacrificed to facilitate heart removal, followed by immunohistochemistry staining targeting glial fibrillary acidic protein (GFAP). Rats subjected to psychological stress showed a wider range of significant cardiac issues compared to control rats. This included left ventricular hypertrophy [IVSd: 0.1968 ± 0.0163 vs. 0.1520 ± 0.0076, P < 0.05; LVPWd: 0.2877 ± 0.0333 vs. 0.1689 ± 0.0057, P < 0.01; LVPWs: 0.3180 ± 0.0382 vs. 0.2226 ± 0.0121, P < 0.05; LV-mass: 1.283 ± 0.0836 vs. 1.000 ± 0.0241, P < 0.01], myocardial ischemia [21.30% vs. 32.97%, P < 0.001], and neuroinflammation. This outcome underscores the imperative of prioritizing psychological well-being during adolescence, presenting a compelling avenue to curtail the prevalence of CVD in adulthood. Furthermore, extending such considerations to individuals grappling with CVD might prospectively enhance their overall quality of life

Evaluating the Application of Tissue-Specific Dose Kernels Instead of Water Dose Kernels in Internal Dosimetry: A Monte Carlo Study

The aim of this work is to evaluate the application of tissue-specific dose kernels instead of water dose kernels to improve the accuracy of patient-specific dosimetry by taking tissue heterogeneities into consideration

Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.Medicine, Faculty ofScience, Faculty ofNon UBCPhysics and Astronomy, Department ofRadiology, Department ofReviewedFacultyResearche

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body

NEMA NU-4 2008 Performance Evaluation of Xtrim-PET: A prototype SiPM-based preclinical scanner

Xtrim-PET is a newly designed Silicon Photomultipliers (SiPMs)-based prototype PET scanner dedicated for small laboratory animal imaging. We present the performance evaluation of the Xtrim-PET scanner following NEMA NU-4 2008 standards to help optimizing scanning protocols which can be achieved through standard and reliable system performance characterization

Radiosynthesis, Biological Evaluation, and Preclinical Study of a 68Ga-Labeled Cyclic RGD Peptide as an Early Diagnostic Agent for Overexpressed αvβ3 Integrin Receptors in Non-Small-Cell Lung Cancer

The αvβ3 integrin receptors have high expression on proliferating growing tumor cells of different origins including non-small-cell lung cancer. RGD-containing peptides target the extracellular domain of integrin receptors. This specific targeting makes these short sequences a suitable nominee for theranostic application. DOTA-E(cRGDfK)2 was radiolabeled with 68Ga efficiently. The in vivo and in vitro stability was examined in different buffer systems. Metabolic stability was assessed in mice urine. In vitro specific binding, cellular uptake, and internalization were determined. The tumor-targeting potential of [68Ga]Ga-DOTA-E(cRGDfK)2 in a lung cancer mouse model was studied. Besides, the very early diagnostic potential of the 68Ga-labeled RGD peptide was evaluated. The acquisition and reconstruction of the PET-CT image data were also carried out. Radiochemical and radionuclide purity for [68Ga]Ga-DOTA-E(cRGDfK)2 was >%98 and >%99, respectively. Radiotracer showed high in vivo, in vitro, and metabolic stability which was determined by ITLC. The dissociation constant (Kd) of [68Ga]Ga-DOTA-E(cRGDfK)2 was 15.28 nM. On average, more than 95% of the radioactivity was specific binding (internalized + surface-bound) to A549 cells. Biodistribution data showed that radiolabeled peptides were accumulated significantly in A549 tumor and excreted rapidly by the renal system. Tumor uptake peaks were at 1-hour postinjection for [68Ga]Ga-DOTA-E(cRGDfK)2. The tumor was clearly visualized in all images. [68Ga]Ga-DOTA-E(cRGDfK)2 can be used as a peptide-based imaging agent allowing very early detection of different cancers overexpressing αvβ3 integrin receptors and can be a potential candidate in clinical peptide-based imaging for lung cancer