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A Supervised Image Registration Approach for Late Gadolinium Enhanced MRI and Cine Cardiac MRI Using Convolutional

Roshan Reddy Upendra1, Richard Simon2, Cristian A Linte1,2

  • 1Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.

Medical Image Understanding and Analysis. Medical Image Understanding and Analysis (Conference)
|July 19, 2021
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Summary
This summary is machine-generated.

This study introduces a novel deep learning method for aligning cardiac MRI scans, improving the accuracy of myocardial viability assessment after heart attack or other conditions. The new technique significantly reduces alignment errors, enhancing diagnostic precision.

Keywords:
Cine cardiac MRIDeep learningImage registrationLate gadolinium enhanced MRI

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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Deep Learning in Radiology

Background:

  • Late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) is crucial for assessing myocardial viability in conditions like myocardial infarction, myocarditis, and cardiomyopathy.
  • Accurate delineation of hyper-enhanced myocardial tissue is essential but challenging due to low contrast between myocardium and left ventricle (LV) blood-pool in LGE CMR.
  • Balanced-Steady State Free Precession (bSSFP) cine CMR offers superior contrast, making registration with LGE CMR vital for precise localization and quantification of myocardial damage.

Purpose of the Study:

  • To develop and evaluate a novel Spatial Transformer Network (STN) inspired convolutional neural network (CNN) architecture for supervised registration of bSSFP cine CMR and LGE CMR images.
  • To improve the accuracy of localizing and quantifying compromised myocardial tissue by enhancing the alignment between LGE and bSSFP CMR datasets.

Main Methods:

  • Proposed a CNN architecture incorporating a Spatial Transformer Network (STN) for supervised image registration.
  • Evaluated the registration method on the 2019 Multi-Sequence Cardiac Magnetic Resonance Segmentation Challenge (MS-CMRSeg) dataset.
  • Utilized quantitative metrics including center-to-center LV/RV blood-pool distance and contour-to-contour blood-pool/myocardium distance for performance assessment.

Main Results:

  • The proposed registration method significantly reduced the LV blood-pool center distance from 3.28mm to 2.27mm and RV blood-pool center distance from 4.35mm to 2.52mm.
  • Average Surface Distance (ASD) between bSSFP and LGE images was reduced for LV blood-pool (2.53mm to 2.09mm), LV myocardium (1.78mm to 1.40mm), and RV blood-pool (2.42mm to 1.73mm).
  • Demonstrated improved alignment accuracy between LGE and bSSFP CMR images, crucial for accurate viability assessment.

Conclusions:

  • The developed STN-inspired CNN effectively performs supervised registration of bSSFP cine CMR and LGE CMR images.
  • The method enhances the accuracy of aligning cardiac MRI sequences, leading to more precise quantification of myocardial scar and viability.
  • This approach holds significant potential for improving diagnostic accuracy in patients with various cardiac conditions requiring myocardial viability assessment.