Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networks (1802.01221v1)

Abstract: Acquiring images of the same anatomy with multiple different contrasts increases the diversity of diagnostic information available in an MR exam. Yet, scan time limitations may prohibit acquisition of certain contrasts, and images for some contrast may be corrupted by noise and artifacts. In such cases, the ability to synthesize unacquired or corrupted contrasts from remaining contrasts can improve diagnostic utility. For multi-contrast synthesis, current methods learn a nonlinear intensity transformation between the source and target images, either via nonlinear regression or deterministic neural networks. These methods can in turn suffer from loss of high-spatial-frequency information in synthesized images. Here we propose a new approach for multi-contrast MRI synthesis based on conditional generative adversarial networks. The proposed approach preserves high-frequency details via an adversarial loss; and it offers enhanced synthesis performance via a pixel-wise loss for registered multi-contrast images and a cycle-consistency loss for unregistered images. Information from neighboring cross-sections are utilized to further improved synthesis quality. Demonstrations on T1- and T2-weighted images from healthy subjects and patients clearly indicate the superior performance of the proposed approach compared to previous state-of-the-art methods. Our synthesis approach can help improve quality and versatility of multi-contrast MRI exams without the need for prolonged examinations.

• The paper presents a novel use of conditional GANs employing adversarial, pixel-wise, and cycle-consistency losses to enhance multi-contrast MRI synthesis.
• It demonstrates that using both registered (pGAN) and unregistered (cGAN) strategies improves image quality, achieving up to a 5.22 dB PSNR gain over existing methods.
• Rigorous testing on MIDAS, IXI, and BRATS datasets confirms the method’s potential for robust, scalable applications in clinical imaging.

An In-depth Analysis of Conditional Generative Adversarial Networks for Multi-Contrast MRI Synthesis

The paper presented introduces a sophisticated methodology for addressing the challenges in synthesizing multi-contrast MRI images utilizing conditional Generative Adversarial Networks (GANs). This approach focuses on overcoming the limitations of traditional intensity-based transformation methods by leveraging the architectural strengths of GANs, specifically for the synthesis of T1 and T2 weighted MRI images. Previous methodologies have been hampered by the degradation of high-frequency content in synthesized images and issues related to unaligned multi-contrast images. The novel application of conditional GANs seeks to rectify these constraints by incorporating adversarial, pixel-wise, and cycle-consistency loss functions to preserve spatial details and accommodate unregistered datasets.

Methodological Advancements

Key to this paper is the deployment of conditional GANs under two scenarios: with registered (pGAN) and unregistered (cGAN) multi-contrast images. The distinction allows the method to be versatile in practical clinical settings where perfect image alignment is often unattainable. Notably, while pGAN utilizes a pixel-wise loss suitable for registered datasets, cGAN introduces a cycle-consistency loss to handle unregistered images, obviating the need for preliminary alignment processes.

The integration of adversarial and cycle-consistency losses aligns closely with the emerging trends in computer vision, where high-frequency textures are maintained through the adversarial paradigm. Moreover, the cGAN's ability to operate on unpaired images significantly broadens the applicability of this model, potentially facilitating the inclusion of larger and variably sourced datasets.

Numerical Validation and Comparison

A critical appraisal of the proposed method involves rigorous testing across three publicly available datasets: MIDAS, IXI, and BRATS. The datasets encompass a variety of subjects, including those with glioma, providing a robust testing ground for the synthesis methodologies. Performance metrics, specifically PSNR and SSIM, were employed to quantitatively assess the output against competing approaches, including Replica and Multimodal frameworks.

The results highlight that pGAN consistently outstrips these established methods, indicating improvements of up to 5.22 dB in PSNR for certain tasks. These enhancements are particularly significant in high-frequency regions, an area where prior methods such as random forest regressions in Replica or squared error losses in Multimodal falter. Additionally, visual inspections substantiate these findings, with pGAN maintaining superior structural integrity and detailing.

Implications and Future Directions

The implications of this research stretch across both theoretical and practical domains of AI in medical imaging. The end-to-end learning capability of GANs features as a notable leap over segmented, traditional workflows, promising enhanced accuracy and clinical applicability. Though MRI synthesis forms the current application focus, extending these models to multimodal integration across MRI, CT, and PET imaging might capture further diagnostic value.

Speculatively, the generalizability to include multiple source contrasts as inputs, not just multiple slices, can ameliorate cross-modality image synthesis. Additionally, the use of cGANs on unpaired data accentuates a potential shift toward more data-agile and scalable AI-driven diagnostic tools, leveraging deep networks without exhaustive labeling efforts or inter-modality alignment.

In conclusion, this robust framework for MRI synthesis using conditional GANs sets a precedent for both medical imaging and the broader AI community. The methodology's adeptness at synthesizing high-quality images from disparate sources showcases its potential beyond current paradigms, positioning it as a linchpin in future clinical imaging innovations.