Mauro Namías

Background. Established methods are available for measuring the noise power spectrum (NPS) in computed tomography (CT) by using uniform phantoms. However, in clinical practice, direct estimation of this metric from patient images is desirable for continuous image quality monitoring and protocol optimization. Methodology. An automated workflow was developed through the integration of liver segmentation with TotalSegmentator, optimized selection of square patches in potentially homogeneous regions using a greedy algorithm, and NPS computation employing the PyLinac library. CT images from five patients and one homogeneous phantom were analyzed using two reconstruction kernels (lung and standard). Validation was performed using Kullback–Leibler divergence ( D KL ) to compare the NPS distributions obtained from patients and phantoms under equivalent acquisition conditions. Results. NPS estimation from abdominal images was feasible. In nearly all paired comparisons (phantom versus patient under identical acquisition and reconstruction conditions), D KL values between normalized NPS distributions were below 0.05 , indicating strong agreement in the shape of the spatial noise distribution between the homogeneous phantom and clinical studies. Conclusion. An automated workflow for NPS estimation in the hepatic parenchyma is presented. The similarity between the characteristic NPS profile obtained from the patient images and that derived from a homogeneous phantom acquired under equivalent conditions was subsequently validated. These preliminary results support the potential integration of this metric as a complementary quantitative tool for CT quality control programs, directly on patient images.

The diagnostic image quality of positron emission tomography (PET) acquisitions strongly depends on the administered radiotracer activity and acquisition time. However, keeping these variables as low as possible is desired to reduce patient radiation exposure and the radiopharmaceutical related costs of PET examinations. PETfectior is an artificial intelligence-based software that processes PET scans and increases the signal-to-noise ratio, obtaining high-quality images from low-count-rate images. In this study, we perform an initial clinical validation of PETfectior on images acquired with half of the counting statistics required to meet the most recent European Association of Nuclear Medicine quantitative standards for ¹⁸ F-FDG PET, evaluating lesion detectability, quantitative accuracy and image quality. 258 patients referred for ¹⁸ F-FDG PET/computed tomography (CT) were prospectively included. The standard-of-care scans (100% scans) were acquired and reconstructed according to EARL standards 2. Besides, half-counting-statistics versions of these images were obtained from list-mode data and processed with PETfectior (50% + PETfectior scans). All the oncologic lesions were segmented on both versions of the PET/CT images either manually or automatically and lesion detectability was evaluated. The SUV _max of the lesions was measured and the quantitative concordance of 50% + PETfectior and 100% images was evaluated. Subjective image quality was visually assessed by two experienced physicians. 1649 lesions were detected in a total of 198 studies. The 50%+ PETfectior images showed high sensitivity for lesion detection (99.9%) and only one false positive was detected. Quantitative concordance of SUV _max measured on 100% and 50% + PETfectior images was within ±12.5% (95% limits of agreement), with a bias of −1.01%. No difference in image quality between 50% + PETfectior and 100% images was found. We conclude that PETfectior can safely be used in the clinical practice with ¹⁸ F-FDG at half counting statistics, with high sensitivity and specificity, low quantitative bias and high subjective image quality, at least for the scanners evaluated in this study.

Psilocybin is a classic psychedelic drug known to alter subjective experience and elicit long-term psychological changes, enhancing cognitive flexibility and reducing rigid self-related beliefs. Combined with compassion motivational primes that involve generating mental representations of compassion, it may increase the potential for activating the care-affiliative motivational systems, linked to several important biopsychosocial processes underpinning social safeness, social connection and mental wellbeing. We investigated the synergetic effects of psilocybin and compassion imagery with self-reported questionnaires and functional resonance imaging data (fMRI) in a sample of 105 participants. Participants were primed with either attention to breathing or a short compassion focused imagery prime. We found a long-term synergetic effect of compassion imagery and psilocybin on cognitive absorption, as well as changes relative to baseline self-compassion and decentering. Based on functional interactions between attentional, executive and default mode networks, fMRI-based classifiers detected participant engagement in compassion focused imagery before psilocybin intake and distinguished compassion imagery vs. attention to breathing priming only the high dose of psilocybin. Our results support the potential for synergistic effects from combinations of psilocybin and compassion-based interventions to induce long-term psychological changes, reshaping the functional organization of large-scale brain networks. Future confirmatory studies of our exploratory analyses should be conducted to determine whether the combination of psilocybin and compassion-based practices promotes increases in caring and contemplative abilities, enhanced psychological flexibility and well-being.

Background: Diagnostic PET image quality depends on the administered activity and acquisition time. However, minimizing these variables is desirable to reduce patient radiation exposure and radiopharmaceutical costs. PETfectior is an artificial intelligence-based software that processes PET scans and increases signal-to-noise ratio, obtaining high-quality images from low-count-rate images. We perform an initial clinical validation of PETfectior on images acquired with half of the counting statistics required to meet the most recent EANM quantitative standards for 18F-FDG PET, evaluating lesions detectability, quantitative performance and image quality. Materials and methods: 258 patients referred for 18F-FDG PET/CT were prospectively included. The standard-of-care scans (100% scans) were acquired and reconstructed according to EARL standards 2. Half-counting-statistics versions were generated from list-mode data and processed with PETfecftior (50%+PETfectior scans). All oncologic lesions were segmented on both PET/CT versions, manually or automatically, and lesions detectability was evaluated. The SUVmax of the lesions was measured and the quantitative concordance of 50%+PETfectior and 100% images was evaluated. Subjective image quality was visually assessed by two experienced physicians. Results: 1649 lesions were detected in a total of 198 studies. The 50%+PETfectior images showed high sensitivity for lesion detection (99.9%) and only 1 false positive was detected. The SUVmax measured in 100% and 50%+PETfectior images agreed within 12.5% (95% limits of agreement), with a bias of -1.01%. Image quality of the 50%+PETfectior images was rated equal to or better than the standard-of-care images. Conclusion: PETfectior can safely be used in clinical practice at half counting statistics, with high sensitivity and specificity, low quantitative bias and high subjective image quality.

Objective. To determine whether pre-treatment brain metabolic network patterns measured with ¹⁸F-FDG PET are associated with treatment response and survival in cancer patients. Approach. Exploratory retrospective study of two independent cohorts: stage III breast cancer patients treated with neoadjuvant chemotherapy and stage IV melanoma patients treated with anti-PD-1 immunotherapy. Metabolic brain network scores were derived from pre-treatment ¹⁸F-FDG PET scans and evaluated for their ability to stratify good versus poor responders using ROC analysis (AUC). Longitudinal changes in network scores were assessed across follow-up, and progression-free survival (PFS) and overall survival (OS) analyses were performed in the melanoma cohort. Main results. Specific brain networks were associated with treatment outcome; the cognition/language network was the strongest predictor (AUC > 0.84 for distinguishing good vs. poor responders in both cohorts). Good responders showed lower cognition/language scores than poor responders and healthy controls. Longitudinally, cognition/language scores remained stable in good responders, while poor responders exhibited a gradual convergence toward the scores observed in good responders. In the melanoma cohort, lower cognition/language scores were significantly associated with longer PFS and OS. Significance. These findings indicate that metabolic brain network patterns, particularly the cognition/language network, may serve as noninvasive biomarkers linked to treatment efficacy and survival in oncology. The results support a possible complex interaction between brain metabolism, immune response, and clinical outcomes. Key limitations include the retrospective design and lack of direct immune-function and psychometric measures; prospective, multimodal studies are needed to validate these observations and elucidate underlying mechanisms.

Background: Cardiac reorientation is a necessary step in processing myocardial perfusion images. This task usually requires manual intervention and thus introduces intra‐ and inter‐operator variability in the processing workflow that may lead to reduced reproducibility of the results.Methods: A deep learning model was trained to perform segmentation of cardiac structures from SPECT images simulated from a real PET/CT dataset. Labels used for training were automatically generated in a semi‐supervised fashion by using TotalSegmentator on CT images. Segmentation results from the trained model were used to calculate cardiac landmarks from which the cardiac axes were defined, and reorientation was performed. Automatic reorientation was compared against the manual reorientation defined by three expert nuclear cardiologists.Results: The average rotation difference between cardiac axes calculated from predicted segmentations and ground‐truth segmentations was on the simulated SPECT test dataset. In real SPECT images, the standard deviation of the angle difference between the automatic method and human experts was lower in all axes and operators compared to the maximum inter‐operator standard deviation.Conclusions: The proposed deep learning‐based algorithm provides an automatic method to perform cardiac reorientation in myocardial perfusion SPECT images with an error range like the variability between operators and with the advantage of using objective anatomical landmarks for the definition of cardiac axes.

(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome planning logistics and larger radiation doses for patients, making it a suboptimal solution. Deep learning (DL) offers a revolutionary way to generate complementary images for individual patients at a large scale. Hence, we aimed to generate linear attenuation coefficient maps from SPECT emission images reconstructed without attenuation correction using deep learning. (2) Methods: A total of 384 SPECT myocardial perfusion studies that used ^99mTc-sestamibi were included. A DL model based on a 2D U-Net architecture was trained using information from 312 patients. The quality of the generated synthetic attenuation correction maps (ACMs) and reconstructed emission values were evaluated using three metrics and compared to standard-of-care data using Bland–Altman plots. Finally, a quantitative evaluation of myocardial uptake was performed, followed by a semi-quantitative evaluation of myocardial perfusion. (3) Results: In a test set of 66 test patients, the ACM quality metrics were MSSIM = 0.97 ± 0.001 and NMAE = 3.08 ± 1.26 (%), and the reconstructed emission quality metrics were MSSIM = 0.99 ± 0.003 and NMAE = 0.23 ± 0.13 (%). The 95% limits of agreement (LoAs) at the voxel level for reconstructed SPECT images were: [−9.04; 9.00]%, and for the segment level, they were [−11; 10]%. The 95% LoAs for the Summed Stress Score values between the images reconstructed were [−2.8, 3.0]. When global perfusion scores were assessed, only 2 out of 66 patients showed changes in perfusion categories. (4) Conclusion: Deep learning can generate accurate attenuation correction maps from non-attenuation-corrected cardiac SPECT images. These high-quality attenuation maps are suitable for attenuation correction in myocardial perfusion SPECT imaging and could obviate the need for additional imaging in standalone SPECT scanners.

INTRODUCTION: The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD‐related pattern (ADRP) and the default mode network (DMN).METHODS: Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2‐[¹⁸F]fluoro‐2‐deoxy‐D‐glucose (FDG) positron emission tomography (PET) three or more times over 6 years (n_scans = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated.RESULTS: Longitudinal increases in ADRP expression were observed in converters, while age‐related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels.DISCUSSION: The results point to the potential utility of ADRP as an imaging biomarker of AD progression.

Purpose To evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans. Methods Sixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen’s Kappa agreement coefficient. Results Physicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial. Conclusion Integrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.