Publications by authors named "Lemley M"
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.
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- 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.
Competition between life science companies is critical to ensure innovative therapies are efficiently developed. Anticompetitive behavior may harm scientific progress and, ultimately, patients. One well-established category of anticompetitive behavior is the 'interlocking directorate'.
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- Chest CT scans are widely used in the U.S., with 15 million performed yearly, primarily for diagnosing various conditions, including cardiac risks.
- A new automated AI system can quickly and accurately assess coronary calcium and various heart chamber volumes from these scans, processing data in about 18 seconds and only missing 0.1% of cases.
- The AI-generated measurements of coronary calcium and heart volumes are effective in predicting overall and cardiovascular mortality, offering a better risk assessment method than traditional evaluations by radiologists.
Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and imaging parameters could predict hospitalization for acute HF exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging. The HF risk prediction model was developed using data from 4,766 patients who underwent SPECT/CT at a single center (internal cohort).
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- AI can enhance the analysis of cardiac anatomy from CT-based myocardial imaging, improving the identification of risks related to cardiovascular events.
- A study of over 7,600 patients showed that higher left ventricular mass and volume increased the likelihood of major adverse cardiovascular events (MACEs) by up to 3.31 times.
- Integrating AI-derived cardiac measurements improved risk prediction significantly, as evidenced by a 23.1% better classification in assessing cardiovascular risks.
Article Synopsis
- The study examines the effectiveness of using automated deep learning techniques to analyze vessel-specific coronary artery calcification (CAC) for better prognostic assessments of heart health in patients.
- A large dataset comprised of electrocardiogram-gated and attenuation-corrected CT scans was used to evaluate the accuracy of this analysis, showing strong agreement with expert assessments across various artery segments.
- The findings indicate that significant CAC levels, especially in the left main/left anterior descending artery, correlate with a higher risk of major adverse cardiovascular events, suggesting that vessel-specific assessment can enhance risk stratification in cardiovascular health.
Article Synopsis
- Epicardial adipose tissue (EAT) volume and attenuation can indicate cardiovascular risk, but measuring them manually is time-consuming; the study explored using deep learning to automate this process using CT scans.
- Researchers trained a deep learning model on data from 500 patients to accurately identify EAT, achieving results in under 2 seconds compared to 15 minutes for manual analysis.
- After analyzing 8781 patients, results showed that higher EAT measurements were linked to an increased risk of death or myocardial infarction over a median follow-up of 2.7 years, indicating that automated EAT assessments could enhance cardiovascular risk prediction.
Article Synopsis
- Myocardial creep refers to the movement of the heart from its original position during stress PET imaging, potentially affecting blood flow measurements, highlighting the need for motion correction for accurate results.
- In a study involving over 4,000 patients, downward myocardial creep was found to significantly correlate with lower all-cause mortality rates and improved prediction of outcomes compared to standard imaging metrics.
- The research indicates that integrating downward creep measurements into PET-MPI models enhances risk assessment for patients, underlining its clinical importance despite limited effects from movement in other directions.
Motion correction (MC) affects myocardial blood flow (MBF) measurements in Rb PET myocardial perfusion imaging (MPI); however, frame-by-frame manual MC of dynamic frames is time-consuming. This study aims to develop an automated MC algorithm for time-activity curves used in compartmental modeling and compare the predictive value of MBF with and without automated MC for significant coronary artery disease (CAD). In total, 565 patients who underwent PET-MPI were considered.
View Article and Find Full Text PDFBackground: On July 1st, 2021, the University of Colorado Hospital (UCH) implemented new sedation protocols in the luminal gastrointestinal (GI) suite. GI proceduralist supervised, Nurse Administered Sedation with fentanyl, midazolam, and diphenhydramine (NAS) sedation was transitioned to Monitored Anesthesia Care with propofol under physician anesthesiologist supervision (MAC).
Objective: To determine if there are statistically significant reductions in Sedation-Start to Scope-In time (SSSI) when using Monitored Anesthesia Care with propofol (MAC) versus Nurse Administered Sedation with fentanyl, midazolam, and diphenhydramine (NAS).
CT attenuation correction improves quantitative risk prediction by cardiac SPECT in obese patients.
Eur J Nucl Med Mol Imaging
February 2024
Purpose: This study aimed to compare the predictive value of CT attenuation-corrected stress total perfusion deficit (AC-sTPD) and non-corrected stress TPD (NC-sTPD) for major adverse cardiac events (MACE) in obese patients undergoing cadmium zinc telluride (CZT) SPECT myocardial perfusion imaging (MPI).
Methods: The study included 4,585 patients who underwent CZT SPECT/CT MPI for clinical indications (chest pain: 56%, shortness of breath: 13%, other: 32%) at Yale New Haven Hospital (age: 64 ± 12 years, 45% female, body mass index [BMI]: 30.0 ± 6.
We investigated the prognostic utility of visually estimated coronary artery calcification (VECAC) from low dose computed tomography attenuation correction (CTAC) scans obtained during SPECT/CT myocardial perfusion imaging (MPI), and assessed how it compares to coronary artery calcifications (CAC) quantified by calcium score on CTACs (QCAC). From the REFINE SPECT Registry 4,236 patients without prior coronary stenting with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 47% female). VECAC in each coronary artery (left main, left anterior descending, circumflex, and right) were scored separately as 0 (absent), 1 (mild), 2 (moderate), or 3 (severe), yielding a possible score of 0-12 for each patient (overall VECAC grade zero:0, mild:1-2, moderate: 3-5, severe: >5).
View Article and Find Full Text PDFBackground: Assessment of coronary artery calcium (CAC) by computed tomographic (CT) imaging provides an accurate measure of atherosclerotic burden. CAC is also visible in computed tomographic attenuation correction (CTAC) scans, always acquired with cardiac positron emission tomographic (PET) imaging.
Objectives: The aim of this study was to develop a deep-learning (DL) model capable of fully automated CAC definition from PET CTAC scans.
Calcium scoring in low-dose ungated chest CT scans using convolutional long-short term memory networks.
Proc SPIE Int Soc Opt Eng
April 2022
Article Synopsis
- The study focused on creating a new deep-learning method, called conv-LSTM, to automatically quantify coronary artery calcium (CAC) in low-dose CT scans, which are used for CT attenuation correction (CTAC).
- A total of 9,543 scans were used to train the conv-LSTM model, and its performance was compared against a U-Net model using metrics like Cohen's kappa coefficients to see how well they agreed with expert evaluations of CAC scores.
- Results showed that conv-LSTM not only yielded similar accuracy to the U-Net model but also processed data faster (6.18 seconds vs. 10.1 seconds) and required less memory (13.11 GB vs.
Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (CAC) from these scans in an automatic manner. We evaluated CAC quantification derived with a DL model, including correlation with expert annotations and associations with major adverse cardiovascular events (MACE).
View Article and Find Full Text PDFBackground: Machine learning (ML) has been previously applied for prognostication in patients undergoing SPECT myocardial perfusion imaging (MPI). We evaluated whether including attenuation CT coronary artery calcification (CAC) scoring improves ML prediction of major adverse cardiovascular events (MACE) in patients undergoing SPECT/CT MPI.
Methods: From the REFINE SPECT Registry 4770 patients with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 45% female).
To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted by misregistration. We developed and externally validated a deep-learning model to generate simulated AC images directly from non-AC (NC) SPECT, without the need for CT.
View Article and Find Full Text PDFPurpose: We sought to evaluate inter-scan and inter-reader agreement of coronary calcium (CAC) scores obtained from dedicated, ECG-gated CAC scans (standard CAC scan) and ultra-low-dose, ungated computed tomography attenuation correction (CTAC) scans obtained routinely during cardiac PET/CT imaging.
Methods: From 2928 consecutive patients who underwent same-day Rb cardiac PET/CT and gated CAC scan in the same hybrid PET/CT scanning session, we have randomly selected 200 cases with no history of revascularization. Standard CAC scans and ungated CTAC scans were scored by two readers using quantitative clinical software.
Background: We hypothesized early post-stress left ventricular ejection fraction reserve (EFR) on solid-state-SPECT is associated with major cardiac adverse events (MACE).
Methods: 151 patients (70 ± 12 years, male 50%) undergoing same-day rest/regadenoson stress Tc-sestamibi solid-state SPECT were followed for MACE. Rest imaging was performed in the upright and supine positions.