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Objective. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer’s disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep learning (DL) networks using neuroimaging for AD diagnosis. However, no particular model has emerged as optimal. Due to a lack of direct comparisons and evaluations on independent data, there is no consensus on which modality is best for diagnostic models or whether longitudinal information enhances performance. The purpose of this work was (1) to develop a generalizable DL model to distinguish neuroimaging scans of AD patients from controls and (2) to evaluate the influence of imaging modality and longitudinal data on performance. Approach. We trained a 2-class convolutional neural network (CNN) with and without a cascaded recurrent neural network (RNN). We used datasets of 772 (N AD = 364, N control = 408) 3D 18F-FDG PET scans and 780 (N AD = 280, N control = 500) T1-weighted volumetric-3D MR images (containing 131 and 144 patients with multiple timepoints) from the Alzheimer’s Disease Neuroimaging Initiative, plus an independent set of 104 (N AD = 63, N NC = 41) 18F-FDG PET scans (one per patient) for validation. Main Results. ROC analysis showed that PET-trained models outperformed MRI-trained, achieving maximum AUC with the CNN + RNN model of 0.93 ± 0.08, with accuracy 82.5 ± 8.9%. Adding longitudinal information offered significant improvement to performance on 18F-FDG PET, but not on T1-MRI. CNN model validation with an independent 18F-FDG PET dataset achieved AUC of 0.99. Layer-wise relevance propagation heatmaps added CNN interpretability. Significance. The development of a high-performing tool for AD diagnosis, with the direct evaluation of key influences, reveals the advantage of using 18F-FDG PET and longitudinal data over MRI and single timepoint analysis. This has significant implications for the potential of neuroimaging for future research on AD diagnosis and clinical management of suspected AD patients.

O advento da radiômica tem gerado oportunidades em várias aplicações diagnósticas, incluindo a tomografia computadorizada (TC) de tórax para diagnóstico e estadiamento da COVID-19. Este trabalho demonstra a aplicação de técnicas de seleção e harmonização de atributos radiômicos em um estudo multicêntrico de COVID-19. 37 indivíduos com COVID-19 e 36 indivíduos com outras pneumonias não associadas a COVID-19 foram selecionados de três centros diagnósticos. Noventa e quatro atributos radiômicos da TC de tórax foram quantificados e selecionados usando dois métodos: 1) supervisionado, utilizando análise univariada pela curva ROC, e 2) não supervisionado, utilizando concordância de observadores intraclasse (ICC > 0,9) a partir de réplicas das TC com variações na qualidade de imagem e na segmentação. Adicionalmente, foram analisadas características populacionais que pudessem enviesar a harmonização dos atributos. O método de seleção não supervisionado demonstrou ser eficaz na triagem de atributos harmonizados, não sendo necessário uma harmonização com pós processamento. Os atributos selecionados pelo método supervisionado possuem maior dispersão das distribuições das quantificações entre os equipamentos, fazendo-se necessária a aplicação da harmonização ComBat após avaliação da sensibilidade do viés ecológico.

La cuantificación en la tomografía por emisión de positrones (PET) está sujeta a sesgos debido a limitaciones físicas y técnicas. El objetivo de la armonización cuantitativa es lograr mediciones comparables entre diferentes tomógrafos PET, permitiendo así ensayos clínicos multicéntricos. Las guías clínicas, como las de la Asociación Europea de Medicina Nuclear (EANM), recomiendan armonizar las reconstrucciones de PET para llevar los coeficientes de recuperación de contraste (CRC) dentro de las especificaciones. Sin embargo, estas reconstrucciones armonizadas pueden mostrar sesgos cuantitativos. En este trabajo mejoramos la armonización mediante el uso de un novedoso esquema de filtrado adaptativo. Nuestro objetivo fue obtener un sesgo de cuantificación bajo y valores de relación señal / ruido (PSNR) altos al mismo tiempo. Para ello se diseñó e implementó un nuevo filtro de eliminación de ruido adaptativo de tres etapas. Los parámetros del filtro se optimizaron para lograr un PSNR alto en un fantoma cerebral digital y un sesgo cuantitativo bajo de los valores máximos de recuperación de contraste (CRCmax) obtenidos de un fantoma de calidad de imagen de PET de la Asociación Nacional de Fabricantes Eléctricos (NEMA). El fantoma NEMA se escaneó en varios escáneres PET / CT y se reconstruyó sin filtros. La configuración de filtro óptima encontrada para un conjunto de datos de entrenamiento se aplicó luego a las reconstrucciones de prueba de otros escáneres. Los límites de armonización se definieron utilizando los intervalos de confianza del 95% entre reconstrucciones. Como resultado, se lograron valores de CRCmax promedio cercanos a la unidad (± 5%) para esferas con un diámetro igual o superior a 13 mm para el conjunto de datos de entrenamiento. Los valores de PSNR fueron comparables a otros resultados de filtros de última generación. Usando la misma configuración de filtro óptima para los conjuntos de datos de prueba, se encontraron resultados cuantitativos similares. La visibilidad de la lesión mejoró en imágenes clínicas en comparación con las reconstrucciones EANM, sin artefactos visibles. De esta forma, nuestro filtro adaptativo de tres etapas logró un rendimiento cuantitativo de vanguardia para la obtención de imágenes PET. Se lograron tolerancias de armonización con un sesgo y varianza más bajos que las directrices de EANM para una variedad de modelos de escáner. Los valores de CRCmax estaban cerca de la unidad y la variabilidad de cuantificación se redujo en comparación con las reconstrucciones estándar.

Patients with metastatic melanoma often receive 18F-FDG PET/CT scans on different scanners throughout their monitoring period. In this study, we quantified the impact of scanner harmonization on longitudinal changes in PET standardized uptake values using various harmonization and normalization methods, including an anthropomorphic PET phantom. Twenty metastatic melanoma patients received at least two FDG PET/CT scans, each on two different scanners with an average of 4 months (range: 2–8) between. Scans from a General Electric (GE) Discovery 710 PET CT−1 were harmonized to the GE Discovery VCT using image reconstruction settings matching recovery coefficients in an anthropomorphic phantom with bone equivalent inserts and wall-less synthetic lesions. In patient images, SUVmax was measured for each melanoma lesion and time-point. Lesions were classified as progressing, stable, or responding based on pre-defined threshold of ±30% change in SUVmax. For comparison, harmonization was also performed using simpler methods, including harmonization using a NEMA phantom, post-reconstruction filtering, reference region normalization of SUVmax, and use of SUVpeak instead of SUVmax. In the 20 patients, 90 lesions across two time-points were available for treatment response assessment. Treatment response classification changed in 47% (42/90) of cases after harmonization with anthropomorphic phantom. Before harmonization, 37% (33/90) of the lesions were classified as stable (changing less than 30% between two time-points), while the fraction of stable lesions increased to 58% (52/90) after harmonization. Harmonization with the NEMA phantom agreed with harmonization with the anthropomorphic phantom in 91% (82/90) of cases. Post-reconstruction filtering agreed with anthropomorphic phantom-based harmonization in 83% (75/90) cases. The utilization of reference regions for normalization or SUVpeak was unable to correct for changes as identified by the anthropomorphic phantom-based harmonization. Overall, PET scanner harmonization has a major impact on individual lesion treatment response classification in metastatic melanoma patients. Harmonization using the NEMA phantom yielded similar results to harmonization using anthropomorphic phantom, while the only acceptable post-reconstruction technique was post-reconstruction filtering. Phantom-based harmonization is therefore strongly recommended when comparing lesion uptake across time-points when the images have been acquired on different PET scanners.

Purpose: Oncological 18F-FDG PET/CT acquisition and reconstruction protocols need to be optimized for both quantitative and detection tasks. To date, most studies have focused on either quantification or noise, leading to quantitative harmonization guidelines or appropriate noise levels. We developed and evaluated protocols that provide harmonized quantitation with optimal amount of noise as a function of acquisition parameters and body mass. Methods: Multiple image acquisitions (N = 17) of the IEC/NEMA PET image quality phantom were performed with variable counting statistics. Phantom images were reconstructed with OSEM3D and PSF reconstructions for harmonized CRCmax quantification. The lowest counting statistics that resulted in compliance with EANM recommendations for CRCmax and CRCmax variability were used as optimization metrics. Image noise in the liver of 48 typical oncological 18F-FDG PET/CT studies was analysed with OSEM3D and PSF harmonized reconstructions. 164 additional 18F-FDG PET/CT reconstructed list mode images were also evaluated to derive analytical expressions that predict image quality and noise variability. Phantom to subject translational analysis was used to derive optimized acquisition and reconstruction protocols. Results: For harmonized quantitation levels, PSF reconstructions yielded decreased noise and lower CRCmax variability compared with regular OSEM3D reconstructions, suggesting they could enable a decreased activity regimen for matched performance. Conclusion: A PSF reconstruction with 7mm post-filter can provide harmonized quantification performance and acceptable image noise levels with injected activity, duration, and mass settings of 260 MBq.s/kg acquisition parameter at scan time. Similarly, the OSEM3D with 5mm post filter can provide similar performance with 401 MBq.s/kg.

PURPOSE. To investigate image intensity histograms as a potential source of useful imaging biomarkers in both a clinical example of detecting immune-related colitis (irColitis) in (18)F-FDG PET/CT images of immunotherapy patients and an idealized case of classifying digital reference objects (DRO). METHODS. Retrospective analysis of bowel (18)F-FDG uptake in N = 40 patients receiving immune checkpoint inhibitors was conducted. A CNN trained to segment the bowel was used to generate the histogram of bowel (18)F-FDG uptake, and percentiles of the histogram were considered as potential metrics for detecting inflammation associated with irColitis. A model of the colon was also considered using cylindrical DRO. Classification of DRO with different intensity distributions was undertaken under varying geometry and noise settings. RESULTS. The most predictive biomarker of irColitis was the 95th percentile of the bowel SUV histogram (SUV(95%)). Patients later diagnosed with irColitis had a significantly higher increase in SUV(95%) from baseline to first on-treatment PET than patients who did not experience irColitis (p = 0.02). An increase in SUV(95%)> + 40% separated pre-irColitis change from normal variability with a sensitivity of 75% and specificity of 88%. Furthermore, histogram percentiles were ideal metrics for classifying ‘hot center’ and ‘cold center’ DRO, and were robust to varying DRO geometry and noise, and to the presence of spoiler volumes unrelated to the detection task. CONCLUSIONS. The 95th percentile of the bowel SUV histogram was the optimal metric for detecting irColitis on (18)F-FDG PET/CT. Image intensity histograms are a promising source of imaging biomarkers for clinical tasks.

INTRODUCTION: Digital mammography units usually record the entrance surface exposure (ESE) and the average glandular dose (AGD) received by the patient for each exposure. This information is usually available in the DICOM headers and can be used to perform dose surveys. The main objective of this paper was to develop a software tool to automatically analyze digital mammography image headers in order to assess the dose received by each patient. MATERIALS AND METHODS: Images were acquired on a Senographe Essential (GE Healthcare) digital mammography system, using automatic exposure control. Patients with breast implants were excluded. Images were queried and downloaded from the institution's PACS using the DCM4CHE 3.0 open source toolkit. MATLAB code was developed to extract the breast thickness and the AGD from the DICOM headers. This data was exported to a spreadsheet for further analysis. RESULTS: Demographic data, breast thickness and dose per view for 59 patients were analyzed. A AGD of 1.56 mGy and 1.53 mGy was obtained for the craniocaudal (CC) and mediolateral oblique (MLO) views respectively. CONCLUSIONS: the developed tool proved to be useful for the dosimetric survey of digital mammographic techniques, allowing us to implement a dose management program.

Implementation of efficient computed tomography (CT) detectors, automatic exposure control and iterative reconstruction algorithms allows to obtain high quality images while minimizing absorbed dose to the patients, in consonance with the ALARA criterion. In this work, we present the results of the acceptance testing of a Philips iCT Elite scanner with the IMR reconstruction algorithm. A Catphan 500 phantom was used for this purpose. RESULTS: The laser positioning system was misaligned, while the gantry and table were aligned. The high-contrast spatial resolution was 21 lp/cm. Regarding low contrast resolution, a 3 mm pin with 0.3% contrast was observed using IMR for CTDIvol = 10.4 mGy, while a 5mm pin was observed using filtered back projection (FBP). Average uniformity was ± 3 Hounsfield Units (HU) and noise was

Purpose: This paper describes a method to achieve consistent clinical image quality in 18F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques. Methods: Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects. Results: The adoption of different schemes for three body mass ranges ( 90 kg) allows improved image quality with both point spread function and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice. Conclusions: Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations.

PURPOSE Quantification in positron emission tomography (PET) is subject to bias due to physical and technical limitations. The goal of quantitative harmonization is to achieve comparable measurements between different scanners, thus enabling multicenter clinical trials. Clinical guidelines, such as those from the European Association of Nuclear Medicine (EANM), recommend harmonizing PET reconstructions to bring contrast recovery coefficients (CRCs) within specifications. However, these harmonized reconstructions can show quantitative biases. In this work we improve harmonization by using a novel adaptive filtering scheme. Our goal was to obtain low quantification bias and high peak signal to noise ratio (PSNR) values at the same time. METHODS a novel three-stage adaptive denoising filter was implemented. Filter parameters were optimized to achieve both high PSNR in a digital brain phantom and low quantitative bias of maximum CRC values (CRCmax) obtained from a National Electrical Manufacturers Association (NEMA) PET image quality phantom. The NEMA phantom was scanned on several PET/CT scanners and reconstructed without postfilters. The optimal filter settings found for a training dataset were then applied to testing reconstructions from other scanners. Harmonization limits were defined using the 95% confidence intervals across reconstructions. RESULTS Average CRCmax values close to unity (± 5%) were achieved for spheres with diameter equal or greater than 13 mm for the training dataset. PSNR values were comparable to other state-of-the-art filter results. Using the same optimal filter settings for the testing datasets, similar quantitative results were found. Lesion conspicuity was improved on clinical scans when compared with EANM reconstructions, with no visible artifacts. CONCLUSIONS Our three-stage adaptive filter achieved state-of-the-art quantitative performance for PET imaging. Harmonization tolerances with lower bias and variance than EANM guidelines were achieved for a variety of scanner models. CRCmax values were close to unity and the quantification variability was reduced when compared with standard reconstructions.

Introduccion La relacion entre la viabilidad, el flujo miocardico y el grado de estenosis epicardica en pacientes con enfermedad coronaria y disfuncion ventricular izquierda esta poco investigada. Objetivo Determinar si los patrones de viabilidad por tomografia por emision de positrones (PET) y el flujo miocardico en reposo se relacionan con el grado de estenosis epicardica. Material y metodos Se evaluo la viabilidad en 27 pacientes mediante el analisis combinado de la perfusion con 13N-amonio (13NH3) y el metabolismo con 18F-fluoro-2-desoxiglucosa (FDG) para identificar cuatro patrones PET: match (hipocaptacion concordante de ambos radiotrazadores), mismatch (hipoperfusion con captacion preservada de FDG), mismatch inverso (perfusion preservada e hipocaptacion de FDG) y perfusion/metabolismo conservados. El flujo absoluto se calculo mediante un modelo bicompartimental. Las estenosis se clasificaron en leves (< 50%), moderadas (> 50%), graves (> 70%) y criticas (. 90%). Resultados De 459 segmentos resultaron match el 33%, mismatch el 12%, mismatch inverso el 11% y conservado el 44%. El flujo para mismatch, mismatch inverso y conservado fue mayor que para los segmentos con match (p < 0,01). Quince lesiones fueron leves, 7 moderadas, 20 graves y 39 criticas. No hubo correlacion entre el grado de estenosis y los patrones de viabilidad (R < 0,2; p = ns) ni con los valores de flujo (R = 0,12). El analisis por territorio vascular no mostro correlacion con el grado de estenosis (p = ns). Conclusiones No hubo correlacion entre los patrones PET, el grado de estenosis epicardica y el flujo miocardico, lo que sugiere que la anatomia coronaria no puede discriminar miocardio viable del necrotico ni predecir el estado del flujo miocardico en pacientes con disfuncion ventricular izquierda.

Elastofibroma dorsi has been described in the literature as an unusual tumor or pseudotumor. However, autopsies and imaging studies have revealed that it is a non-negligible finding. Purpose The aim of this study has been to illustrate and become familiar with this type of lesion in order to prevent misdiagnosis. Materials and methods From 3 December 2008 to 5 January 2010, 1,751 patients were evaluated with 18 FDG-PET/CT. Of these, 29 cases of elastofibroma dorsi were recorded as an incidental finding. A retrospective and descriptive analysis was performed on this study series. Results The study showed a prevalence of 1.66%. Out of the 29 findings, 22 (75.86%) were females and 7 (24.14%) males. Seventeen (58.62%) cases were bilateral, 12 (41.38%) unilateral and the SUVmax ranged from 1.4 to 3.2. These lesions were reported as soft tissue density images with mild or moderate diffuse metabolic activity. Conclusion The elastofibroma dorsi is a relatively common finding in PET/CT that should be known in order to avoid making wrong diagnoses.

Positron emission tomography (PET) imaging allows for measurement of activity concentrations of a given radiotracer in vivo. The quantitative capabilities of PET imaging are particularly important in the context of monitoring response to treatment, where quantitative changes in tracer uptake could be used as a biomarker of treatment response. Reconstruction algorithms and settings have a significant impact on PET quantification. In this work we introduce a novel harmonization methodology requiring only a simple cylindrical phantom and show that it can match the performance of more complex harmonization approaches based on phantoms with spherical inserts. Resolution and noise measurements from cylindrical phantoms are used to simulate the spherical inserts from NEMA image quality phantoms. An optimization algorithm was used to find the optimal smoothing filters for the simulated NEMA phantom images to identify those that best harmonized the PET scanners. Our methodology was tested on seven different PET models from two manufacturers installed at five institutions. Our methodology is able to predict contrast recovery coefficients (CRCs) from NEMA phantoms with errors within ±5.2% for CRCmax and ±3.7% for CRCmean (limits of agreement = 95%). After applying the proposed harmonization protocol, all the CRC values were within the tolerances from EANM. Quantitative harmonization in compliance with the EARL FDG-PET/CT accreditation program is achieved in a simpler way, without the need of NEMA phantoms. This may lead to simplified scanner harmonization workflows more accessible to smaller institutions.