A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study.

Background: The spatiotemporal progression and patterns of tissue deformation in ventilator-induced lung injury (VILI) remain understudied. Our aim was to identify lung clusters based on their regional mechanical behavior over space and time in lungs subjected to VILI using machine-learning techniques.

Results: Ten anesthetized pigs (27 ± 2 kg) were studied.

A strategy for estimating radiation dose to the blood in outpatient settings in differentiated thyroid cancer therapy with I-NaI.

Background: Although standard operational procedures for pre-therapeutic dosimetry already exist for the determination of the maximum safe activity to treat differentiated thyroid cancer patients, empiric activity administration of I is still the most frequent way of treatment. In this way, the absorbed dose to the blood/bone marrow remains unknown.

Purpose: In this work, we present a strategy to estimate radiation dose to the blood in an outpatient setting.

Synthetic Attenuation Correction Maps for SPECT Imaging Using Deep Learning: A Study on Myocardial Perfusion Imaging.

(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.

Longitudinal changes in metabolic network activity in early Alzheimer's disease.

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-[ F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (n  = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance.

Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.

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 ( = 73), and another for model validation ( = 55).

Development of a deep learning network for Alzheimer's disease classification with evaluation of imaging modality and longitudinal data.

. 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.

Image intensity histograms as imaging biomarkers: application to immune-related colitis.

To investigate image intensity histograms as a potential source of useful imaging biomarkers in both a clinical example of detecting immune-related colitis (irColitis) inF-FDG PET/CT images of immunotherapy patients and an idealized case of classifying digital reference objects (DRO).Retrospective analysis of bowelF-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 bowelF-FDG uptake, and percentiles of the histogram were considered as potential metrics for detecting inflammation associated with irColitis.

Improved PET quantification and harmonization by adaptive denoising.

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.

Protocols for Harmonized Quantification and Noise Reduction in Low-Dose Oncologic F-FDG PET/CT Imaging.

Oncologic F-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 amounts of noise as a function of acquisition parameters and body mass.

Optimization of oncological ¹⁸F-FDG PET/CT imaging based on a multiparameter analysis.

Purpose: This paper describes a method to achieve consistent clinical image quality in (18)F-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.