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Original Article | Open Access

Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion

Lei Zhang1,2,3( ), Fang-Fang Yin1,2,3, Ke Lu1,2, Brittany Moore1,2, Silu Han1,2, Jing Cai4( )
Medical Physics Graduate Program, Duke University, Durham, North Carolina, USA
Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA
Medical Physics Graduate Program, Duke Kunshan University, Kunshan, Jiangsu, China
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
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Abstract

Objective

Multiparametric magnetic resonance imaging (MRI) renders rich and complementary anatomical and functional information, which is often utilized separately. This study aimed to propose an adaptive multiparametric MRI (mpMRI) fusion method, and examine its capability in improving tumor contrast and synthesizing novel tissue contrasts among liver cancer patients.

Methods

An adaptive mpMRI fusion method was developed with five components: image pre-processing, fusion algorithm, database, adaptation rules, and fused MRI. The linear-weighted summation algorithm was used for fusion. Weight-driven and feature-driven adaptations were designed for different applications. A clinical-friendly graphic user interface (G was developed in Matlab and used for mpMRI fusion. Twelve liver cancer patients and a digital human phantom were included in the study. Synthesis of novel image contrast, and enhancement of image signal and contrast were examined in patient cases. Tumor contrast-to-noise ratio (CNR) and liver signal-to-noise ratio (SNR) were evaluated and compared before and after mpMRI fusion.

Results

The fusion platform was applicable in both XCAT phantom and patient cases. Novel image contrasts, including enhancement of soft-tissue boundary, vertebral body, tumor, and composition of multiple image features in one image, were achieved. Tumor CNR improved from –1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for T1-weighted (T1-w), from 3.39 ± 1.89 to 7.87 ± 3.47 (p < 0.01) for T2-w, and from 1.42 ± 1.66 to 7.69 ± 3.54 (p < 0.001) for T2/T1-w MRI. Liver SNR improved from 2.92 ± 2.39 to 9.96 ± 8.60 (p < 0.05) for diffusion-weighted MRI. The coefficient of variation of tumor CNR lowered from 1.57, 0.56, and 1.17 to 0.47, 0.44, and 0.46 for T1-w, T2-w, and T2/T1-w MRI, respectively.

Conclusion

A multiparametric MRI fusion method was proposed and a prototype was developed. The method showed potential in improving clinically relevant features, such as tumor contrast and liver signal. Synthesis of novel image contrasts, including the composition of multiple image features into a single image set, was achieved.

Keywords

liver cancerradiation therapyimage fusionmultiparametric magnetic resonance imagingtumor contrast

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Precision Radiation Oncology
Volume 6 Issue 3,
September 2022
Pages 190-198
DOI: 10.1002/pro6.1167
Cite this article:
Zhang L, Yin F-F, Lu K, et al. Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion. Precision Radiation Oncology, 2022, 6(3): 190-198. https://doi.org/10.1002/pro6.1167

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Received: 31 March 2022
Revised: 10 June 2022
Accepted: 23 June 2022
Published: 16 July 2022
© 2022 The Authors. Precision Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Shandong Cancer Hospital & Institute.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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