Multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI): Development and initial evaluation in liver tumor patients

Zhang, Lei; Yin, Fang‐Fang; Li, Tian; Teng, Xinzhi; Xiao, Haonan; Harris, Wendy; Ren, Lei; Kong, Feng-Ming Spring; Ge, Hong; Mao, Rui; Cai, Jing

doi:10.1002/mp.15314

Cited by 6 publications

(8 citation statements)

References 53 publications

(96 reference statements)

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“…Apart from this, the MAMF method is not limited to the four studied MR sequences. Other types of MR images, such as T2/T1-weighted MRI using MR steady-state free precession sequences (22,37), can also be used as input for MAMF and provide unique contributions to the contrast spectrum of the resulting fused MR images. The clinical efficacy of different MRI sequences combinations for MAMF is yet to be investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, four-dimensional MRI (4D-MRI) has been an emerging technique for studying the impact of respiratory motion (23,48,49). Initial incorporation of 4D-MRI with the MAMF fusion method has been reported (22), suggesting that the MAMF method could be combined with 4D-MRI for enhanced tumor contrast and inter-observer target delineation consistency. One limitation of the study is a relatively small cohort size.…”

Section: Discussionmentioning

confidence: 99%

“…Instead, they were independently optimized to achieve optimal tumor CNR with a positive liver SNR for each patient. More details of the MAMF method could be found in previous publications (21,22).…”

Section: Generation Of Fused Mri Using Multisource Adaptive Mri Fusionmentioning

confidence: 99%

“…The contours of the target are firstly created in MR images and then transferred to planning CT images via MRI-CT registration. However, MRI might still be prone to inter-sequence and inter-patient variations in image quality and tumor contrast, and potentially inter-observer variations in tumor identification or delineation (14)(15)(16)(17)(18)(19)(20)(21)(22)(23).…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these challenges, we have previously developed a Multisource Adaptive MRI Fusion (MAMF) method that is capable of producing a large number of fused MR images with multifaceted image contrasts for RT applications using a limited number of standard MR images as input (21). This method has shown promise in enhancing the image quality of MRI in radiotherapy treatment planning featuring application-specific adaptation and optimization of image contrast (22). In this study, we evaluated the potential clinical efficacy of MAMF in gross tumor volume (GTV) delineation of HCC patients in terms of both tumor contrast optimization and inter-observer variability improvement.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma

et al. 2022

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PurposeTumor delineation plays a critical role in radiotherapy for hepatocellular carcinoma (HCC) patients. The incorporation of MRI might improve the ability to correctly identify tumor boundaries and delineation consistency. In this study, we evaluated a novel Multisource Adaptive MRI Fusion (MAMF) method in HCC patients for tumor delineation.MethodsTen patients with HCC were included in this study retrospectively. Contrast-enhanced T1-weighted MRI at portal-venous phase (T1WPP), contrast-enhanced T1-weighted MRI at 19-min delayed phase (T1WDP), T2-weighted (T2W), and diffusion-weighted MRI (DWI) were acquired on a 3T MRI scanner and imported to in-house-developed MAMF software to generate synthetic MR fusion images. The original multi-contrast MR image sets were registered to planning CT by deformable image registration (DIR) using MIM. Four observers independently delineated gross tumor volumes (GTVs) on the planning CT, four original MR image sets, and the fused MRI for all patients. Tumor contrast-to-noise ratio (CNR) and Dice similarity coefficient (DSC) of the GTVs between each observer and a reference observer were measured on the six image sets. Inter-observer and inter-patient mean, SD, and coefficient of variation (CV) of the DSC were evaluated.ResultsFused MRI showed the highest tumor CNR compared to planning CT and original MR sets in the ten patients. The mean ± SD tumor CNR was 0.72 ± 0.73, 3.66 ± 2.96, 4.13 ± 3.98, 4.10 ± 3.17, 5.25 ± 2.44, and 9.82 ± 4.19 for CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. Fused MRI has the minimum inter-observer and inter-patient variations as compared to original MR sets and planning CT sets. GTV delineation inter-observer mean DSC across the ten patients was 0.81 ± 0.09, 0.85 ± 0.08, 0.88 ± 0.04, 0.89 ± 0.08, 0.90 ± 0.04, and 0.95 ± 0.02 for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively. The patient mean inter-observer CV of DSC was 3.3%, 3.2%, 1.7%, 2.6%, 1.5%, and 0.9% for planning CT, T1WPP, T2W, DWI, T1WDP, and fused MRI, respectively.ConclusionThe results demonstrated that the fused MRI generated using the MAMF method can enhance tumor CNR and improve inter-observer consistency of GTV delineation in HCC as compared to planning CT and four commonly used MR image sets (T1WPP, T1WDP, T2W, and DWI). The MAMF method holds great promise in MRI applications in HCC radiotherapy treatment planning.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Generation Of Fused Mri Using Multisource Adaptive Mri Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma

et al. 2022

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Multi‐parametric MRI for radiotherapy simulation

Wang

Yang

et al. 2023

Medical Physics

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Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image‐guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi‐parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi‐contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.

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

Zhang

Yin

et al. 2022

Precision Radiation Oncology

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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 featuredriven 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, suchThis 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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Multi‐contrast four‐dimensional magnetic resonance imaging (MC‐4D‐MRI): Development and initial evaluation in liver tumor patients

Cited by 6 publications

References 53 publications

Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma

Evaluation of Multisource Adaptive MRI Fusion for Gross Tumor Volume Delineation of Hepatocellular Carcinoma

Multi‐parametric MRI for radiotherapy simulation

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

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