Abstract
Cross-modal medical imaging techniques are predominantly being used in the clinical suite. The ensemble learning methods using cross-modal medical imaging adds reliability to several medical image analysis tasks. Motivated by the performance of deep learning in several medical imaging tasks, a deep learning-based denoising method Cross-Modality Guided Denoising Network CMGDNet for removing Rician noise in T1-weighted (T1-w) Magnetic Resonance Images (MRI) is proposed in this paper. CMGDNet uses a guidance image, which is a cross-modal (T2-w) image of better perceptual quality to guide the model in denoising its noisy T1-w counterpart. This cross-modal combination allows the network to exploit complementary information existing in both images and therefore improve the learning capability of the model. The proposed framework consists of two components: Paired Hierarchical Learning (PHL) module and Cross-Modal Assisted Reconstruction (CMAR) module. PHL module uses Siamese network to extract hierarchical features from dual images, which are then combined in a densely connected manner in the CMAR module to finally reconstruct the image. The impact of using registered guidance data is investigated in removing noise as well as retaining structural similarity with the original image. Several experiments were conducted on two publicly available brain imaging datasets available on the IXI database. The quantitative assessment using Peak Signal to noise ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) demonstrates that the proposed method exhibits 4.7% and 2.3% gain (average), respectively, in SSIM and FSIM values compared to other state-of-the-art denoising methods that do not integrate cross-modal image information in removing various levels of noise.
Highlights
Magnetic Resonance Imaging (MRI) is preferred for the structural and functional analysis of several organs in the clinical setting thanks to its non-ionizing nature and ability to highlight structures with high contrast
‘substantia nigra’, a brain area affected due to Parkinson’s disease can be visualized clearly on T2-w images compared to T1-w [2], whereas, T1-w images are preferred in the quantification of atrophy, an irreversible loss of neurons associated with multiple sclerosis [3]
The performance of the proposed method was validated by comparing it with five state-of-the-art methods including Non-local means filter (NLM) [10], Stein’s unbiased risk estimate (SURE) [58], Block-matching and 3D filtering (BM3D) [59], Multi-channel Denoising convolutional neural network (MCDnCNN), referred as MCDN in the paper [24] and FFD-Net [53]
Summary
Magnetic Resonance Imaging (MRI) is preferred for the structural and functional analysis of several organs in the clinical setting thanks to its non-ionizing nature and ability to highlight structures with high contrast. MR neuroimaging is widely employed in the screening and diagnosis of brain cancers and neurodegenerative dysfunctions such as Alzheimer’s disease and multiple sclerosis [1]. MRI can highlight tissue with various contrasts using different sequences of Radio-Frequency (RF) pulses. Specific pathologies are accurately analyzed and interpreted when captured using a particular RF pulse sequence. ‘substantia nigra’, a brain area affected due to Parkinson’s disease can be visualized clearly on T2-w images compared to T1-w [2], whereas, T1-w images are preferred in the quantification of atrophy, an irreversible loss of neurons associated with multiple sclerosis [3]. A cohort study comprising 200 surgically treated Craniopharyngiomas (CPs), an infiltrative brain tumor concluded that several key radiological variables recognized on both T1-w and T2-w MR images correctly predicted the CP topography in 86% of cases [4]
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