Authors:
Adnan Khalid
1
;
Husnain Shahid
2
;
Hatem Rashwan
1
and
Domenec Puig
1
Affiliations:
1
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
;
2
Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain
Keyword(s):
Image Reconstruction, Compressed Sensing, Under-Sampled Measurements, Deep Learning.
Abstract:
Magnetic Resonance Imaging (MRI) reconstruction, particularly restoration and denoising, remains challenging due to its ill-posed nature and high computational demands. In response to this, Compressed Sensing (CS) has recently gained prominence for enabling image reconstruction from limited measurements and consequently reducing computational costs. However, CS often struggles to maintain diagnostic image quality and strictly relies on sparsity and incoherence conditions that are somewhat challenging to meet with experimental data or particularly real-world medical data. To address these limitations, this paper proposes a novel framework that integrates CS with a convolutional neural network (CNN), effectively relaxing the CS constraints and enhancing the diagnostic quality of MRI reconstructions. In essence, this method applies CS to generate a measurement vector during initial step and then refined the output by CNN to improve image quality. Extensive evaluations on the MRI knee da
taset demonstrate the efficacy of this dual step approach, achieving significant quality improvements with measurements (SSIM = 0.876, PSNR = 27.56 dB). A deep comparative analysis also perform to identify the superior performance over multiple existing CNN architectures
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