Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion
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 tum...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-09-01
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| Series: | Precision Radiation Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/pro6.1167 |
| _version_ | 1811181267349143552 |
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| author | Lei Zhang Fang‐Fang Yin Ke Lu Brittany Moore Silu Han Jing Cai |
| author_facet | Lei Zhang Fang‐Fang Yin Ke Lu Brittany Moore Silu Han Jing Cai |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T09:14:38Z |
| format | Article |
| id | doaj.art-1d24155fac66416984cde8e28c3f7a8d |
| institution | Directory Open Access Journal |
| issn | 2398-7324 |
| language | English |
| last_indexed | 2024-04-11T09:14:38Z |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Precision Radiation Oncology |
| spelling | doaj.art-1d24155fac66416984cde8e28c3f7a8d2022-12-22T04:32:23ZengWileyPrecision Radiation Oncology2398-73242022-09-016319019810.1002/pro6.1167Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusionLei Zhang0Fang‐Fang Yin1Ke Lu2Brittany Moore3Silu Han4Jing Cai5Medical Physics Graduate Program Duke University Durham North Carolina USAMedical Physics Graduate Program Duke University Durham North Carolina USAMedical Physics Graduate Program Duke University Durham North Carolina USAMedical Physics Graduate Program Duke University Durham North Carolina USAMedical Physics Graduate Program Duke University Durham North Carolina USADepartment of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom, Kowloon Hong KongAbstract 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.https://doi.org/10.1002/pro6.1167image fusionliver cancermultiparametric magnetic resonance imagingradiation therapytumor contrast |
| spellingShingle | Lei Zhang Fang‐Fang Yin Ke Lu Brittany Moore Silu Han Jing Cai Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion Precision Radiation Oncology image fusion liver cancer multiparametric magnetic resonance imaging radiation therapy tumor contrast |
| title | Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion |
| title_full | Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion |
| title_fullStr | Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion |
| title_full_unstemmed | Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion |
| title_short | Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion |
| title_sort | improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric magnetic resonance imaging fusion |
| topic | image fusion liver cancer multiparametric magnetic resonance imaging radiation therapy tumor contrast |
| url | https://doi.org/10.1002/pro6.1167 |
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