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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17192 |
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| _version_ | 1866917379764649984 |
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| author | Thakur, Sukirt Roper, Marcus Zhou, Yang Isaev, Dmitry Yu. Bafghi, Reza Akbarian Nallamothu, Brahmajee K. Figueroa, C. Alberto Paruchuri, Srinivas Burger, Scott Collet, Carlos Raissi, Maziar |
| author_facet | Thakur, Sukirt Roper, Marcus Zhou, Yang Isaev, Dmitry Yu. Bafghi, Reza Akbarian Nallamothu, Brahmajee K. Figueroa, C. Alberto Paruchuri, Srinivas Burger, Scott Collet, Carlos Raissi, Maziar |
| contents | More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions are identified in 70% of patients evaluated for ischemic heart disease. Up to half of these patients have undiagnosed, life-limiting coronary microvascular dysfunction (CMD), which remains under-detected due to the limited availability of invasive tools required to measure coronary flow reserve (CFR). Here, we introduce PUNCH, a non-invasive, uncertainty-aware framework for estimating CFR directly from standard coronary angiography. PUNCH integrates physics-informed neural networks with variational inference to infer coronary blood flow from first-principles models of contrast transport, without requiring ground-truth flow measurements or population-level training. The pipeline runs in approximately three minutes per patient on a single GPU. Validated on synthetic angiograms with controlled noise and imaging artifacts, as well as on clinical bolus thermodilution data from 20 patients, PUNCH demonstrates accurate and uncertainty-calibrated CFR estimation. This approach establishes a new paradigm for CMD diagnosis and illustrates how physics-informed inference can substantially expand the diagnostic utility of available clinical imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17192 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics Thakur, Sukirt Roper, Marcus Zhou, Yang Isaev, Dmitry Yu. Bafghi, Reza Akbarian Nallamothu, Brahmajee K. Figueroa, C. Alberto Paruchuri, Srinivas Burger, Scott Collet, Carlos Raissi, Maziar Machine Learning More than 10 million coronary angiograms are performed globally each year, providing a gold standard for detecting obstructive coronary artery disease. Yet, no obstructive lesions are identified in 70% of patients evaluated for ischemic heart disease. Up to half of these patients have undiagnosed, life-limiting coronary microvascular dysfunction (CMD), which remains under-detected due to the limited availability of invasive tools required to measure coronary flow reserve (CFR). Here, we introduce PUNCH, a non-invasive, uncertainty-aware framework for estimating CFR directly from standard coronary angiography. PUNCH integrates physics-informed neural networks with variational inference to infer coronary blood flow from first-principles models of contrast transport, without requiring ground-truth flow measurements or population-level training. The pipeline runs in approximately three minutes per patient on a single GPU. Validated on synthetic angiograms with controlled noise and imaging artifacts, as well as on clinical bolus thermodilution data from 20 patients, PUNCH demonstrates accurate and uncertainty-calibrated CFR estimation. This approach establishes a new paradigm for CMD diagnosis and illustrates how physics-informed inference can substantially expand the diagnostic utility of available clinical imaging. |
| title | PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.17192 |