About

Bradley D Allen is the Chief of Cardiovascular and Thoracic Imaging in the Department of Radiology at Northwestern University Feinberg School of Medicine. He holds the position of Associate Professor in Radiology with a specialization in Cardiovascular and Thoracic Imaging. His professional role involves leading efforts in cardiovascular and thoracic imaging within the department, contributing to clinical practice, research, and education in this specialized area of radiology.

Research topics

• Medicine
• Internal medicine
• Radiology
• Computer Science
• Emergency medicine
• Surgery
• Political Science
• Cardiology
• Artificial Intelligence
• Statistics
• Law
• Mathematics
• Algorithm
• Intensive care medicine
• Medical physics
• Pathology
• Medical emergency

Selected publications

• Automated Extraction of False Lumen Flow Dynamics in Aortic Dissection Using 4D Flow MRI Correlates with False Lumen Ejection Fraction

  Journal of Cardiovascular Magnetic Resonance · 2026-01-01

  articleOpen accessSenior author
  PublisherDOI

• Machine-Learned Hemodynamic Estimates from Contrast-Enhanced MRA and Direct Velocity Measurements from 4D flow MRI Are Equally Predictive of Adverse BAV Outcomes

  Journal of Cardiovascular Magnetic Resonance · 2026-01-01

  articleOpen accessSenior author
  PublisherDOI

• Impact of Clinical Outcomes and Socioeconomic Status on CT versus MR Utilization in Thoracic Aortic Surveillance Imaging

  Journal of Cardiovascular Magnetic Resonance · 2026-01-01

  articleOpen accessSenior author
  PublisherDOI

• 26-CCC-19812-ACC A SLEEPING GIANT: AN INCIDENTAL DIAGNOSIS OF A MASSIVE RIGHT ATRIAL DIVERTICULUM

  Journal of the American College of Cardiology · 2026-03-27

  article
  PublisherDOI

• Atlas-based Aorta Deformation Heatmaps for Improved Characterization of Thoracic Aortic Disease.

  Journal of Cardiovascular Magnetic Resonance · 2026-01-01

  articleOpen access
  PublisherDOI

• Fully Automated AI-Driven Quantification of Thoracic Aorta Dimensions from Contrast-Enhanced MRA

  Journal of Cardiovascular Magnetic Resonance · 2026-01-01

  articleOpen access
  PublisherDOI

• Myocardial ablation related ‘persistent’ microvascular obstruction like lesions

  The International Journal of Cardiovascular Imaging · 2025-08-04

  article
  PublisherDOI

• Abstract 4365172: Aortic Valve Type and Stenosis Can Be Identified from Wearable-Recorded Seismocardiograms Using Machine-Learned Classifiers

  Circulation · 2025-11-03

  article

  Introduction: Patients with aortic valve disease, such as bicuspid aortic valve (BAV), require regular echocardiography or cardiovascular (CV) MRI to monitor for complications such as valve stenosis (AS) and aortic dilation. However, repeated imaging can be burdensome and incur substantial cost. Seismocardiogram (SCG) chest acceleration measurements recorded by inexpensive wearable devices can give indicators of valve-mediated hemodynamic changes, and as such may have supplemental value for such patients. This study investigated using SCG recordings coupled with a novel machine-learned (ML) classifier for SCG signals to identify patient valve type and presence/absence of aortic valve stenosis (AS). Hypothesis: We hypothesize that accurate classification of aortic valve type and AS can be made from SCG recordings with ML analysis compared to those from standard-of-care imaging (ground truth: cardiac MRI or echo). Methods: Healthy controls (no known CV disease) and aortic valve disease patients with tricuspid (TAV), BAV, or post-repair mechanical valve who received echo or MRI (clinical CV protocol) were enrolled for same-day 2-minute wearable SCG measurement (fig. A). Standard clinical assessment of valve/flow function was used (fig. B). Informed consent was given with IRB oversight. Clinical imaging used 4D flow MRI (1.5T,1-3mm3/30-40ms) or 2D Doppler echo (1.7-3.3MHz,12-40FPS). From clinical read of valve type/function, subjects were grouped in four classes: AS (any degree), BAV no-AS, TAV no-AS, mechanical. A hybrid network with convolutional neural network and multi-layer perceptron was trained (80/20 train/test) to classify patient valve status from SCG wavelet coefficients and demographics (age/sex/height/weight). Performance was evaluated by 20-fold cross-validation. Results: Enrolment was 129 subjects (97 MRI/32 echo): 46 controls (45.9±17.4y/20F) and 83 patients (22.4±15.8y/20F; 67 BAV/6 TAV/10 mech.). Classification area-under-curve (AUC) was high for all classes (AUC≥0.79). Across all ML validations, correct classification was achieved for ≥75% of subjects. Conclusion: This evaluation of a machine-learned classifier for SCG indicate potential utility in screening for valve-mediated hemodynamic changes, which reverberate through the chest and cause altered vibrations. The low cost and ease of acquisition for SCG would make it an appealing complement to imaging as the current standard for aortic valve abnormality screening and management.

  PublisherDOI

• Radiology:Cardiothoracic Imaging Highlights 2024

  Utrecht University Repository (Utrecht University) · 2025-06-01

  articleOpen access

  Radiology: Cardiothoracic Imaging publishes research, technical developments, and reviews related to cardiac, vascular, and thoracic imaging. The current review article, led by the Radiology: Cardiothoracic Imaging trainee editorial board, highlights the most impactful articles published in the journal between November 2023 and October 2024. The review encompasses various aspects of cardiac, vascular, and thoracic imaging related to coronary artery disease, cardiac MRI, valvular imaging, congenital and inherited heart diseases, thoracic imaging, lung cancer, artificial intelligence, and health services research. Key highlights include the role of CT fractional flow reserve analysis to guide patient management, the role of MRI elastography in identifying age-related myocardial stiffness associated with increased risk of heart failure, review of MRI in patients with cardiovascular implantable electronic devices and fractured or abandoned leads, imaging of mitral annular disjunction, specificity of the Lung Imaging Reporting and Data System version 2022 for detecting malignant airway nodules, and a radiomics-based reinforcement learning model to analyze serial low-dose CT scans in lung cancer screening. Ongoing research and future directions include artificial intelligence tools for applications such as plaque quantification using coronary CT angiography and growing understanding of the interconnectedness of environmental sustainability and cardiovascular imaging.

  Publisher

• Optimal Location, Regurgitant Volume and Regurgitant Fraction by CMR Derived 4D Flow Analysis for Discriminating Severe Aortic Regurgitation

  Heart Lung and Circulation · 2025-08-01

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Michael Markl

  109 shared

• James Carr

  Environmental Molecular Sciences Laboratory

  68 shared

• Jeremy D. Collins

  Mayo Clinic in Arizona

  35 shared

• Alex J. Barker

  30 shared

• Anthony Maroun

  26 shared

• Kelly Jarvis

  25 shared

• Ryan Avery

  Northwestern University

  23 shared

• Justin Baraboo

  21 shared