Denoising Multiphase Functional Cardiac CT Angiography Using Deep Learning and Synthetic Data.

Coronary CT angiography is increasingly used for cardiac diagnosis. Dose modulation techniques can reduce radiation dose, but resulting functional images are noisy and challenging for functional analysis. This retrospective study describes and evaluates a deep learning method for denoising functional cardiac imaging, taking advantage of multiphase information in a three-dimensional convolutional neural network.

Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks.

Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement.

Coronary calcium density in relation to coronary heart disease and cardiovascular disease in adults with diabetes or metabolic syndrome: the Multi-ethnic Study of Atherosclerosis (MESA).

Background: Coronary artery calcium (CAC) density is inversely associated with coronary heart disease (CHD) and cardiovascular disease (CVD) risk. We examined this relation in those with diabetes mellitus (DM) or metabolic syndrome (MetS).

Methods: We studied 3,818 participants with non-zero CAC scores from the Multiethnic Study of Atherosclerosis and classified them as DM, MetS (without DM) or neither DM/MetS.

Inflammation, coronary plaque progression, and statin use: A secondary analysis of the Risk Stratification with Image Guidance of HMG CoA Reductase Inhibitor Therapy (RIGHT) study.

Background: Statin treatment is a potent lipid-lowering therapy associated with decreased cardiovascular risk and mortality. Recent studies including the PARADIGM trial have demonstrated the impact of statins on promoting calcified coronary plaque.

Hypothesis: The degree of systemic inflammation impacts the amount of increase in coronary plaque calcification over 2 years of statin treatment.

Hepatic Steatosis: CT-Based Prevalence in Adults in China and the United States and Associations With Age, Sex, and Body Mass Index.

Calibrated CT fat fraction (FF) measurements derived from un-enhanced abdominal CT reliably reflect liver fat content, allowing large-scale population-level investigations of steatosis prevalence and associations. The purpose of this study was to compare the prevalence of hepatic steatosis, as assessed by calibrated CT measurements, between population-based Chinese and U.S.

Erratum: Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CT.

[This corrects the article DOI: 10.1148/ryai.2021200218.

Long-term expression changes of immune-related genes in prostate cancer after radiotherapy.

The expression of immune-related genes in cancer cells can alter the anti-tumor immune response and thereby impact patient outcomes. Radiotherapy has been shown to modulate immune-related genes dependent on the fractionation regimen. To identify long-term changes in gene expression after irradiation, PC3 (p53 deleted) and LNCaP (p53 wildtype) prostate cancer cells were irradiated with either a single dose (SD, 10 Gy) or a fractionated regimen (MF) of 10 fractions (1 Gy per fraction).

Use of Variational Autoencoders with Unsupervised Learning to Detect Incorrect Organ Segmentations at CT.

Purpose: To develop a deep learning model to detect incorrect organ segmentations at CT.

Materials And Methods: In this retrospective study, a deep learning method was developed using variational autoencoders (VAEs) to identify problematic organ segmentations. First, three different three-dimensional (3D) U-Nets were trained on segmented CT images of the liver ( = 141), spleen ( = 51), and kidney ( = 66).

The lncRNAs and are upregulated in long-term prostate cancer adaptation after radiotherapy.

Long non-coding RNAs (lncRNAs) have been shown to impact important biological functions such as proliferation, survival, and genomic stability. To analyze radiation-induced lncRNA expression in human tumors, we irradiated prostate cancer cells with a single dose of 10 Gy or a multifractionated radiotherapeutic regimen of 10 fractions with a dose of 1 Gy (10 × 1 Gy) during 5 days. We found a stable upregulation of the lncRNA and at 2 months after radiotherapy that was more pronounced after single-dose irradiation.

Automated assessment of longitudinal biomarker changes at abdominal CT: correlation with subsequent cardiovascular events in an asymptomatic adult screening cohort.

Background: Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes.

Methods: Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search.

Spectral photon-counting CT in cardiovascular imaging.

Photon-counting computed tomography (PCCT) is an emerging technology promising to substantially improve cardiovascular imaging. Recent engineering and manufacturing advances by several vendors are expected to imminently launch this new technology into clinical reality. Photon-counting detectors (PCDs) have multiple potential advantages over conventional energy integrating detectors (EIDs) such as the absence of electronic noise, multi-energy capability, and increased spatial resolution.

Reliable segmentation of 2D cardiac magnetic resonance perfusion image sequences using time as the 3rd dimension.

Objectives: Cardiac magnetic resonance (CMR) first-pass perfusion is an established noninvasive diagnostic imaging modality for detecting myocardial ischemia. A CMR perfusion sequence provides a time series of 2D images for dynamic contrast enhancement of the heart. Accurate myocardial segmentation of the perfusion images is essential for quantitative analysis and it can facilitate automated pixel-wise myocardial perfusion quantification.

Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans.

Background: Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical.

Purpose: To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT.

Materials And Methods: The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans.

Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast.

Purpose: Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures.

Materials And Methods: Initial study cohort consisted of 1211 healthy adults (mean age, 45.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard.

Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard.

Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study.

Background: Body CT scans are frequently performed for a wide variety of clinical indications, but potentially valuable biometric information typically goes unused. We investigated the prognostic ability of automated CT-based body composition biomarkers derived from previously-developed deep-learning and feature-based algorithms for predicting major cardiovascular events and overall survival in an adult screening cohort, compared with clinical parameters.

Methods: Mature and fully-automated CT-based algorithms with pre-defined metrics for quantifying aortic calcification, muscle density, visceral/subcutaneous fat, liver fat, and bone mineral density (BMD) were applied to a generally-healthy asymptomatic outpatient cohort of 9223 adults (mean age, 57.

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults.

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016.

Coronary CT Angiography and Carotid MRI Improve Phenotyping of Disease Extent Compared with ACC/AHA Risk Score Alone.

Purpose: To determine the relationship between the American College of Cardiology/American Heart Association (ACC/AHA) risk score and plaque phenotype of the coronary and carotid arteries assessed directly using CT angiography and MRI.

Materials And Methods: Asymptomatic subjects eligible for statin therapy by risk score were enrolled in a prospective study of disease burden using coronary artery calcium (CAC) scoring, coronary CT angiography, and MRI of the carotid arteries. Quartiles were calculated for noncalcified plaque, CAC, and average carotid wall volume and were compared with ACC/AHA risk quartiles.

Utilizing Fully Automated Abdominal CT-Based Biomarkers for Opportunistic Screening for Metabolic Syndrome in Adults Without Symptoms.

Objective: Metabolic syndrome describes a constellation of reversible cardiometabolic abnormalities associated with cardiovascular risk and diabetes. The present study investigates the use of fully automated abdominal CT-based biometric measures for opportunistic identification of metabolic syndrome in adults without symptoms.

Materials And Methods: International Diabetes Federation criteria were applied to a cohort of 9223 adults without symptoms who underwent unenhanced abdominal CT.

Cardiac cine CT approaching 1 mSv: implementation and assessment of a 58-ms temporal resolution protocol.

Clinical use of cardiac cine CT imaging is limited by high radiation dose and low temporal resolution. To evaluate a low radiation dose, high temporal resolution cardiac cine CT protocol in human cardiac CT and phantom scans. CT scans of a circulating iodine target were reconstructed using the conventional single heartbeat half-scan (HS, approx.