Publications by authors named "M G Lemley"
Background: Motion correction (MC) is critical for accurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from F-flurpiridaz positron emission tomography (PET) myocardial perfusion imaging (MPI). However, manual correction is time consuming and introduces inter-observer variability. We aimed to validate an automatic MC algorithm for F-flurpiridaz PET-MPI in terms of diagnostic performance for predicting coronary artery disease (CAD).
View Article and Find Full Text PDFThe Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT) has been expanded to include more patients and CT attenuation correction imaging. We present the design and initial results from the updated registry. The updated REFINE SPECT is a multicenter, international registry with clinical data and image files.
View Article and Find Full Text PDFBackground Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites.
View Article and Find Full Text PDFArticle Synopsis
- Researchers developed a new AI method to analyze routine CTAC scans from cardiac imaging to create volumetric measurements of various tissues, including fat and muscle, in the chest area.
- The study examined data from nearly 10,000 patients, finding that higher volumes of certain types of body fat (VAT, EAT, IMAT) were linked to an increased risk of all-cause mortality, whereas higher bone and skeletal muscle volumes were associated with lower mortality risk.
- This suggests that CTAC scans hold significant potential for identifying body composition markers that may help predict patient mortality risk beyond their current use.
Article Synopsis
- Low-dose computed tomography (CT) scans, used in hybrid myocardial perfusion imaging, provide valuable anatomical and pathological insights beyond just attenuation correction, which may be enhanced through AI-driven frameworks.
- This study analyzed data from over 10,000 patients, segmenting various structures and utilizing deep learning to assess coronary artery health, leading to improved all-cause mortality predictions.
- The comprehensive model integrating data from CT attenuation correction, myocardial perfusion imaging, and clinical factors outperformed other AI models in predicting mortality risk, particularly among patients with normal perfusion.