Dr. Boah Kim Profile

Dr. Boah Kim

at National Institutes of Health

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Proceedings Article | 3 April 2024 Presentation + Paper

Unsupervised multi-parametric MRI registration using neural optimal transport

Boah Kim, Tejas Sudharshan Mathai, Ronald Summers

Proceedings Volume 12927, 129270U (2024) https://doi.org/10.1117/12.3006289

KEYWORDS: Image registration, Deformation, Magnetic resonance imaging, Medical imaging, Machine learning

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Precise deformable image registration of multi-parametric MRI sequences is necessary for radiologists in order to identify abnormalities and diagnose diseases, such as prostate cancer and lymphoma. Despite recent advances in unsupervised learning-based registration, volumetric medical image registration that requires considering the variety of data distributions is still challenging. To address the problem of multi-parametric MRI sequence data registration, we propose an unsupervised domain-transported registration method, called OTMorph by employing neural optimal transport that learns an optimal transport plan to map different data distributions. We have designed a novel framework composed of a transport module and a registration module: the former transports data distribution from the moving source domain to the fixed target domain, and the latter takes the transported data and provides the deformed moving volume that is aligned with the fixed volume. Through endto-end learning, our proposed method can effectively learn deformable registration for the volumes in different distributions. Experimental results with abdominal multi-parametric MRI sequence data show that our method has superior performance over around 67-85% in deforming the MRI volumes compared to the existing learningbased methods. Our method is generic in nature and can be used to register inter-/intra-modality images by mapping the different data distributions in network training.

Proceedings Article | 3 April 2024 Presentation + Paper

Automated classification of body MRI sequence type using convolutional neural networks

Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee, Jianfei Liu, Ronald Summers

Proceedings Volume 12927, 129270K (2024) https://doi.org/10.1117/12.3008780

KEYWORDS: Brain, Abdomen, Image classification, Deep learning, Convolutional neural networks, 3D magnetic resonance imaging

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Multi-parametric MRI of the body is routinely acquired for the identification of abnormalities and diagnosis of diseases. However, a standard naming convention for the MRI protocols and associated sequences does not exist due to wide variations in imaging practice at institutions and myriad MRI scanners from various manufacturers being used for imaging. The intensity distributions of MRI sequences differ widely as a result, and there also exists information conflicts related to the sequence type in the DICOM headers. At present, clinician oversight is necessary to ensure that the correct sequence is being read and used for diagnosis. This poses a challenge when specific series need to be considered for building a cohort for a large clinical study or for developing AI algorithms. In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis. In our pilot work, our 3D DenseNet-121 model achieved an F1 score of 99.5% at differentiating 5 common MRI sequences obtained by three Siemens scanners (Aera, Verio, Biograph mMR). To the best of our knowledge, we are the first to develop an automated method for the 3D classification of MRI sequences in the chest, abdomen, and pelvis, and our work has outperformed the previous state-of-the-art MRI series classifiers.

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