Abstract #2262

A Method to Improve the Reliability of Pulmonary Perfusion-weighted Maps Obtained Using 3D Ultrashort Echo-time MRI

Hyeonha Kim¹, Seokwon Lee², Jinil Park³, You Jin Oh⁴, Jonghoon Kim⁴, Jooae Choe⁵, Ho Yun Lee^4,6, and Jang-Yeon Park^1,2

¹Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, Republic of, ²Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of, ³Department of Radiology, University of California, Davis, Sacramento, CA, United States, ⁴Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea, Republic of, ⁵Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, Republic of, ⁶Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Lung, Lung, Perfusion, Image processing

Motivation: MRI-based non-enhanced 3D perfusion-weighted (QW) maps have challenges due to unwanted signals from small vessels and registration errors.

Goal(s): In this study, we propose developing a method that reliably excludes these unwanted signals to improve the accuracy of quantitative pulmonary perfusion analysis.

Approach: We rescaled the difference map into 30 equal-level bins and applied a bin-based clustering technique to remove non-clustered, artifact-like signals from the QW map after image registration.

Results: The proposed method effectively removes unwanted signals caused by small vessels or boundary signals during image processing, including registration and demonstrated performance in patients with COPD and ILD.

Impact: Improves the reliability of the 3D non-contrast enhanced perfusion-weighted map by effectively removing unwanted signals due to registration errors combined with low SNR and undersampling during image processing.

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