Paper
2 May 2003 Sub-band denoising and spline curve fitting method for hemodynamic measurement in perfusion MRI
Hong-Dun Lin, Hsiao-Ling Huang, Yuan-Yu Hsu, Chi-Chen Chen, Ing-Yi Chen, Liang-Chi Wu, Ren-Shyan Liu, Kang-Ping Lin
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Abstract
In clinical research, non-invasive MR perfusion imaging is capable of investigating brain perfusion phenomenon via various hemodynamic measurements, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean trasnit time (MTT). These hemodynamic parameters are useful in diagnosing brain disorders such as stroke, infarction and periinfarct ischemia by further semi-quantitative analysis. However, the accuracy of quantitative analysis is usually affected by poor signal-to-noise ratio image quality. In this paper, we propose a hemodynamic measurement method based upon sub-band denoising and spline curve fitting processes to improve image quality for better hemodynamic quantitative analysis results. Ten sets of perfusion MRI data and corresponding PET images were used to validate the performance. For quantitative comparison, we evaluate gray/white matter CBF ratio. As a result, the hemodynamic semi-quantitative analysis result of mean gray to white matter CBF ratio is 2.10 ± 0.34. The evaluated ratio of brain tissues in perfusion MRI is comparable to PET technique is less than 1-% difference in average. Furthermore, the method features excellent noise reduction and boundary preserving in image processing, and short hemodynamic measurement time.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong-Dun Lin, Hsiao-Ling Huang, Yuan-Yu Hsu, Chi-Chen Chen, Ing-Yi Chen, Liang-Chi Wu, Ren-Shyan Liu, and Kang-Ping Lin "Sub-band denoising and spline curve fitting method for hemodynamic measurement in perfusion MRI", Proc. SPIE 5031, Medical Imaging 2003: Physiology and Function: Methods, Systems, and Applications, (2 May 2003); https://doi.org/10.1117/12.480285
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KEYWORDS
Hemodynamics

Image processing

Magnetic resonance imaging

Tissues

Denoising

Brain

Image quality

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