Parallel reconstructions of MRI: evaluation using detection and perceptual difference studies

Yuhao Jiang; Donglai Huo; David L. Wilson

doi:10.1117/12.655663

17 March 2006 Parallel reconstructions of MRI: evaluation using detection and perceptual difference studies

Yuhao Jiang, Donglai Huo, David L. Wilson

Proceedings Volume 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment; 61460K (2006) https://doi.org/10.1117/12.655663
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

Parallel imaging using multiple coils and sub-sampled k-space data is a promising fast MR image acquisition technique. We used detection studies and perceptual difference models on image data with ¼ sampling to evaluate three different reconstruction methods: a regularization method developed by Ying and Liang of UIUC, a simplified regularization method, and an iterative method. We also included images obtained from a full complement of k-space data as "gold standard" images. Detection studies were performed using a simulated dark tumor added on MR images of bovine liver. We found that human detection depended strongly on the reconstruction methods used, with the simplified regularization and UIUC methods achieving better performance than the iterative method. We also evaluated images using detection with a Channlized Hotelling Observer (CHO) model and with a Perceptual Difference Model (PDM). Both predicted the same trends as observed in the human detection studies. We are encouraged that PDM gives trends similar to that for detection studies. Its ease of use and applicability to a variety of MR imaging situations make it attractive for evaluating image quality in a variety of MR studies.

Citation Download Citation

Yuhao Jiang, Donglai Huo, and David L. Wilson "Parallel reconstructions of MRI: evaluation using detection and perceptual difference studies", Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 61460K (17 March 2006); https://doi.org/10.1117/12.655663

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KEYWORDS

Magnetic resonance imaging

Image quality

Data modeling

Contrast sensitivity

Liver

Visual process modeling

Image processing

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