Imputing Missing Slices in Diffusion MRI Scans with Incomplete Field-of-View using Deep Generative Models

Core Concepts

A deep generative model framework that imputes the missing brain regions of diffusion-weighted images (DWI) outside of the field-of-view (FOV) by leveraging information from the corresponding T1-weighted image.

Abstract

The content presents a method for imputing missing slices in diffusion MRI (dMRI) scans with incomplete field-of-view (FOV). The key highlights are: Incomplete FOV in dMRI scans is a common issue that can severely impact whole-brain tissue microstructure and connectivity analyses. This is often caused by patient misalignment, suboptimal scan plan selection, or necessity in protocol design. The proposed framework uses a deep generative model that learns to impute the missing brain regions in DWI by leveraging information from the corresponding T1-weighted image. This helps maintain the anatomical consistency and diffusion characteristics. The method was evaluated on two datasets - WRAP and NACC. It achieved good imputation performance in terms of PSNR and SSIM metrics for both b0 and b1300 DWI volumes. The imputed DWI with complete FOV was shown to improve the accuracy and completeness of whole-brain tractography, especially for tracts commonly associated with Alzheimer's disease. This highlights the potential of the approach to repair corrupted dMRI data and reduce uncertainty in clinical applications. The framework exhibited slightly better performance on b0 images compared to b1300 images, likely due to the closer similarity between b0 images and T1-weighted images. Imputation errors were more prominent at the brain boundaries, further from the acquired regions. Overall, the proposed method provides a desirable solution to impute missing slices in dMRI scans with incomplete FOV, enabling subsequent whole-brain analyses that would otherwise be challenging or unattainable.

Stats

The total cutoff distance from the reduced FOV to the top of the brain ranged from 1 mm to 32 mm across 103 real cases of dMRI scans with incomplete FOV. On the WRAP dataset, the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, SSIMb1300=0.893. On the NACC dataset, the proposed framework achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, SSIMb1300=0.877.

Quotes

"Results suggest that the proposed framework achieved sufficient imputation performance in dMRI data with incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data." "Our approach notably enhances the accuracy and completeness of tracts that are significantly compromised with an incomplete FOV."

Key Insights Distilled From

Field-of-View Extension for Diffusion MRI via Deep Generative Models

by Chenyu Gao,S... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03652.pdf

Deeper Inquiries

How can the proposed framework be extended to handle more complex diffusion models beyond the diffusion tensor model?

The proposed framework can be extended to handle more complex diffusion models by incorporating advanced deep learning architectures that can learn the intricate relationships within the data. One approach could involve integrating deep generative models that are specifically designed to capture the nuances of more complex diffusion models, such as the diffusion kurtosis model. By enhancing the network architecture to accommodate the additional parameters and features of these models, the framework can learn to impute missing slices based on the more detailed information provided by these advanced diffusion models. Furthermore, incorporating multi-modal data fusion techniques can also enhance the framework's ability to handle complex diffusion models. By integrating additional imaging modalities, such as functional MRI or spectroscopy data, the framework can leverage complementary information to improve the accuracy of the imputation process. This multi-modal approach can provide a more comprehensive understanding of the brain's microstructure and connectivity, enabling the framework to handle a wider range of diffusion models beyond the traditional diffusion tensor model.

What are the potential limitations of the current approach in handling large variations in brain anatomy and diffusion characteristics across different patient populations?

While the current approach shows promising results in imputing missing slices in diffusion MRI scans with incomplete FOV, there are potential limitations when dealing with large variations in brain anatomy and diffusion characteristics across different patient populations. One limitation is the generalization of the model to diverse datasets with varying anatomical features and diffusion properties. The model may struggle to adapt to the unique characteristics of each population, leading to suboptimal imputation performance in datasets with significant variations. Another limitation is the reliance on T1-weighted images as anatomical references. While T1-weighted images provide valuable structural information, they may not capture all the intricacies of brain anatomy that can vary across different populations. This limitation could impact the accuracy of the imputation process, especially in cases where the T1-weighted images do not fully represent the anatomical variations present in the diffusion MRI scans. Additionally, the current approach may face challenges in preserving high-frequency details and sharp intensity contrasts at tissue boundaries, especially in datasets with diverse diffusion characteristics. This limitation could affect the overall quality of the imputed slices, particularly in regions where the diffusion properties vary significantly across different patient populations.

Can the framework be further improved to better preserve high-frequency details and sharp intensity contrasts at tissue boundaries during the imputation process?

To enhance the preservation of high-frequency details and sharp intensity contrasts at tissue boundaries during the imputation process, several improvements can be implemented in the framework. One approach is to incorporate advanced image enhancement techniques, such as edge-preserving filters and high-pass filtering, to enhance the fine details and edges in the imputed slices. By applying these techniques selectively in regions with high-frequency information, the framework can better preserve the intricate structures present in the brain tissue. Furthermore, integrating attention mechanisms into the deep generative model can help the framework focus on relevant features and details during the imputation process. By directing the model's attention to critical regions with sharp intensity contrasts and fine details, the framework can prioritize the preservation of these features in the imputed slices. This attention-based approach can improve the overall quality of the imputed images, especially at tissue boundaries where detailed information is crucial. Moreover, leveraging adversarial training techniques can enhance the realism and fidelity of the imputed slices, ensuring that the generated images closely resemble the ground truth data. By training the model with adversarial loss functions, the framework can learn to produce more realistic and detailed imputations, including high-frequency information and sharp intensity contrasts at tissue boundaries. This adversarial training strategy can further improve the visual quality and accuracy of the imputed slices, addressing the challenge of preserving fine details during the imputation process.