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

Pixelwise Gradient Model for Image Fusion (PGMIF): a multi-sequence magnetic resonance imaging (MRI) fusion model for tumor contrast enhancement of nasopharyngeal carcinoma

Background

Different image modalities capture different aspects of a patient. It is desirable to produce images that capture all such features in a single image. This research investigates the potential of multi-modal image fusion method to enhance magnetic resonance imaging (MRI) tumor contrast and its consistency across different patients, which can capture both the anatomical structures and tumor contrast clearly in one image, making MRI-based target delineation more accurate and efficient.

Methods

T1-weighted (T1-w) and T2-weighted (T2-w) magnetic resonance (MR) images from 80 nasopharyngeal carcinoma (NPC) patients were used. A novel image fusion method, Pixelwise Gradient Model for Image Fusion (PGMIF), which is based on the pixelwise gradient to capture the shape and a generative adversarial network (GAN) term to capture the image contrast, was introduced. PGMIF is compared with several popular fusion methods. The performance of fusion methods was quantified using two metrics: the tumor contrast-to-noise ratio (CNR), which aims to measure the contrast of the edges, and a Generalized Sobel Operator Analysis, which aims to measure the sharpness of edge.

Results

The PGMIF method yielded the highest CNR [median (mdn) =1.208, interquartile range (IQR) =1.175-1.381]. It was a statistically significant enhancement compared to both T1-w (mdn =1.044, IQR =0.957-1.042, P<5.60×10-4) and T2-w MR images (mdn =1.111, IQR =1.023-1.182, P<2.40×10-3), and outperformed other fusion models: Gradient Model with Maximum Comparison among Images (GMMCI) (mdn =0.967, IQR =0.795-0.982, P<5.60×10-4), Deep Learning Model with Weighted Loss (DLMWL) (mdn =0.883, IQR =0.832-0.943, P<5.60×10-4), Pixelwise Weighted Average (PWA) (mdn =0.875, IQR =0.806-0.972, P<5.60×10-4) and Maximum of Images (MoI) (mdn =0.863, IQR =0.823-0.991, P<5.60×10-4). In terms of the Generalized Sobel Operator Analysis, a measure based on Sobel operator to measure contrast enhancement, PGMIF again exhibited the highest Generalized Sobel Operator (mdn =0.594, IQR =0.579-0.607; mdn =0.692, IQR =0.651-0.718 for comparison with T1-w and T2-w images), compared to: GMMCI (mdn =0.491, IQR =0.458-0.507, P<5.60×10-4; mdn =0.495, IQR =0.487-0.533, P<5.60×10-4), DLMWL (mdn =0.292, IQR =0.248-0.317, P<5.60×10-4; mdn =0.191, IQR =0.179-0.243, P<5.60×10-4), PWA (mdn =0.423, IQR =0.383-0.455, P<5.60×10-4; mdn =0.448, IQR =0.414-0.463, P<5.60×10-4) and MoI (mdn =0.437, IQR =0.406-0.479, P<5.60×10-4; mdn =0.540, IQR =0.521-0.636, P<5.60×10-4), demonstrating superior contrast enhancement and sharpness compared to other methods.

Conclusions

Based on the tumor CNR and Generalized Sobel Operator Analysis, the proposed PGMIF method demonstrated its capability of enhancing MRI tumor contrast while keeping the anatomical structures of the input images. It holds promises for NPC tumor delineation in radiotherapy.

Advances in gross tumor target volume determination in radiotherapy for patients with hepatocellular carcinoma

Hepatocellular Carcinoma (HCC) is one of the most common malignant neoplasms. With the advancement of technology, the precision of radiotherapy (RT) for HCC has considerably increased, and it is an indispensable modality in the comprehensive management of HCC. Some RT techniques increase the radiation dose to HCC, which decreases the radiation dose delivered to the surrounding normal liver tissue. This approach significantly improves the efficacy of HCC treatment and reduces the incidence of Radiation-induced Liver Disease (RILD). Clear imaging and precise determination of the Gross Target Volume (GTV) are prerequisites of precise RT of HCC. The main hindrances in determining the HCC GTV include indistinct tumor boundaries on imaging and the impact on respiratory motion. The integration of multimodal imaging, four-dimensional imaging, and artificial intelligence (AI) techniques can help overcome challenges for HCC GTV. In this article, the advancements in medical imaging and precise determination for HCC GTV have been reviewed, providing a framework for the precise RT of HCC.

Improving liver tumor image contrast and synthesizing novel tissue contrasts by adaptive multiparametric MRI fusion

Purpose

Multiparametric MRI contains rich and complementary anatomical and functional information, which is often utilized separately. This study aims 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. 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 (GUI) 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 a single image were achieved. Tumor CNR improved from -1.70 ± 2.57 to 4.88 ± 2.28 (p < 0.0001) for 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 DWI. The coefficient of variation (CV) 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 single image set was achieved.