M Timothy Hauser
Overview
Explore the profile of M Timothy Hauser including associated specialties, affiliations and a list of published articles.
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6
Citations
16
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0
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Recent Articles
1.
Miller R, Bednarski B, Cui Y, Calsavara V, Patel K, Rozanski A, et al.
JACC Adv
. 2025 Jan;
4(1):101440.
PMID: 39759439
Background: Observational data have suggested that patients with moderate to severe ischemia benefit from revascularization. However, this was not confirmed in a large, randomized trial. Objectives: Using a contemporary, multicenter...
2.
Miller R, Lemley M, Shanbhag A, Ramirez G, Liang J, Builoff V, et al.
J Nucl Med
. 2024 Oct;
65(11):1795-1801.
PMID: 39362762
The 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...
3.
Miller R, Bednarski B, Pieszko K, Kwiecinski J, Williams M, Shanbhag A, et al.
EBioMedicine
. 2024 Jan;
99:104930.
PMID: 38168587
Background: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large...
4.
Pieszko K, Shanbhag A, Singh A, Hauser M, Miller R, Liang J, et al.
NPJ Digit Med
. 2023 May;
6(1):78.
PMID: 37127660
Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval...
5.
Williams M, Bednarski B, Pieszko K, Miller R, Kwiecinski J, Shanbhag A, et al.
Eur J Nucl Med Mol Imaging
. 2023 Apr;
50(9):2656-2668.
PMID: 37067586
Purpose: Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion....
6.
Miller R, Hauser M, Sharir T, Einstein A, Fish M, Ruddy T, et al.
J Nucl Cardiol
. 2022 Jun;
29(5):2393-2403.
PMID: 35672567
Background: Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning...