TY - JOUR
T1 - Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion
AU - Zhang, Lei
AU - Yin, Fang Fang
AU - Lu, Ke
AU - Moore, Brittany
AU - Han, Silu
AU - Cai, Jing
N1 - Funding Information:
The work was supported by research grants of National Institutes of Health (R01 CA226899), General Research Fund (GRF 15102219, 15102118), and Health and Medical Research Fund (HMRF 06173276).
Publisher Copyright:
© 2022 The Authors. Precision Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Shandong Cancer Hospital & Institute.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - image fusion
KW - liver cancer
KW - multiparametric magnetic resonance imaging
KW - radiation therapy
KW - tumor contrast
UR - http://www.scopus.com/inward/record.url?scp=85134246720&partnerID=8YFLogxK
U2 - 10.1002/pro6.1167
DO - 10.1002/pro6.1167
M3 - Journal article
AN - SCOPUS:85134246720
SN - 2398-7324
VL - 6
SP - 190
EP - 198
JO - Precision Radiation Oncology
JF - Precision Radiation Oncology
IS - 3
ER -