Iterative stripe artifact correction framework for TOF-MRA

The purpose of this study is to develop a practical stripe artifacts correction framework on three-dimensional (3-D) time-of-flight magnetic resonance angiography (TOF-MRA) obtained by multiple overlapping thin slab acquisitions (MOTSA) technology. In this work, the stripe artifacts in TOF-MRA were considered as a part of image texture. To separate the image structure and the texture, the relative total variation (RTV) was firstly employed to smooth the TOF-MRA for generating the template image with fewer image textures. Then a residual image was generated, which was the difference between the template image and the raw TOF-MRA. The residual image was served as the image texture, which contained the image details and stripe artifacts. Then, we obtained the artifact image from the residual image via a filter in a specific direction since the image artifacts appeared as stripes. The image details were then produced from the difference between the artifact image and the image texture. To produce the corrected images, we finally compensated the image details to the RTV smoothing image. The proposed method was continued until the stripe artifacts during the iteration vary as little as possible. The digital phantom and the real patients' TOF-MRA were used to test the approach. The spatial uniformity was increased from 74% to 82% and the structural similarity was improved from 86% to 98% in the digital phantom test by using the proposed algorithm. Our approach proved to be highly successful in eliminating stripe artifacts in real patient data tests while retaining image details. The proposed iterative framework on TOF-MRA stripe artifact correction is effective and appealing for enhancing the imaging performance of multi-slab 3-D acquisitions.

[1]  D L Parker,et al.  MR angiography by multiple thin slab 3D acquisition , 1991, Magnetic resonance in medicine.

[2]  Na Li,et al.  Statistical modeling and knowledge-based segmentation of cerebral artery based on TOF-MRA and MR-T1 , 2019, Comput. Methods Programs Biomed..

[3]  S. Lerakis,et al.  The role of magnetic resonance angiography in peripheral artery disease , 2018, Current opinion in pharmacology.

[4]  H Handels,et al.  Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Segmentation , 2010, Methods of Information in Medicine.

[5]  V. Rayz,et al.  Advanced vascular imaging techniques. , 2021, Handbook of clinical neurology.

[6]  Carlos Dias Maciel,et al.  Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders , 2007, Comput. Biol. Medicine.

[7]  Na Li,et al.  Cerebrovascular segmentation from TOF-MRA using model- and data-driven method via sparse labels , 2020, Neurocomputing.

[8]  T. Niu,et al.  Scatter correction for a clinical cone-beam CT system using an optimized stationary beam blocker in a single scan. , 2019, Medical physics.

[9]  Li Xu,et al.  Structure extraction from texture via relative total variation , 2012, ACM Trans. Graph..

[10]  Sung-Hong Park,et al.  Simultaneous Variable-Slab Dual-Echo TOF MR Angiography and Susceptibility-Weighted Imaging , 2017, IEEE Transactions on Medical Imaging.

[11]  Marcello Cadioli,et al.  Comparison of 3D TOF-MRA and 3D CE-MRA at 3T for imaging of intracranial aneurysms. , 2013, European journal of radiology.

[12]  Alejandro F. Frangi,et al.  Automated segmentation of cerebral vasculature with aneurysms in 3DRA and TOF-MRA using geodesic active regions: an evaluation study. , 2010, Medical physics.

[13]  Yaoqin Xie,et al.  Iterative image-domain ring artifact removal in cone-beam CT , 2017, Physics in medicine and biology.

[14]  Ivana Galinovic,et al.  Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks , 2021, Comput. Biol. Medicine.

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  Wufan Chen,et al.  Segmentation of brain magnetic resonance angiography images based on MAP-MRF with multi-pattern neighborhood system , 2013, 2013 IEEE International Conference on Medical Imaging Physics and Engineering.

[17]  Rasim Boyacioğlu,et al.  Multiband multislab 3D time‐of‐flight magnetic resonance angiography for reduced acquisition time and improved sensitivity , 2016, Magnetic resonance in medicine.