Feasibility of fast non-local means noise reduction algorithm in magnetic resonance imaging using 1.5 and 3.0 T with diffusion-weighted image technique
Choi, Won Ho
(Department of Radiology, Asan Medical Center)
,
Choi, Hye Ran
(Department of Radiological Science, Eulji University)
,
Seo, Eunsoo
(Department of Radiological Science, Gachon University)
,
Hwang, Jeewoo
(Department of Radiological Science, Gachon University)
,
Oh, Heekyung
(Department of Radiological Science, Gachon University)
,
Kim, Myeong Rae
(Department of Radiological Science, Eulji University)
,
Han, Su Rin
(Department of Radiological Science, Eulji University)
,
Kim, Min Seok
(Department of Radiology, Seoul National University Boramae Medical Center)
,
Kang, Seong-Hyeon
(Department of Radiological Science, Gachon University)
,
Lee, Youngjin
(Department of Radiological Science, Gachon University)
Abstract Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weight...
Abstract Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weighted gradient b-value. However, a setting with inappropriate b-value causes loss of image signal that increases the influence of noise. Therefore, in this study, we quantitatively evaluated image quality after applying a variety of algorithms to the image acquired by changing the b-value and the main magnetic field in the MRI device. To acquire the image, the phantom was self-produced with an acrylic panel and chicken breast. Wiener filter, total variation (TV), and our proposed fast non-local means (FNLM) noise reduction algorithms were applied to the image. Consequently, the signal intensity at a 3.0 T magnetic field increased by a factor 4.8 compared to a 1.5 T magnetic field. Moreover, the signal-to-noise ratio and contrast-to-noise ratio were highest with the FNLM algorithm, and the values increased by factors of 9.5 and 9.9 with a 1.5 T magnetic field and by factors of 9.9 and 5.0 with a 3.0 T magnetic field compared to the noise image, respectively. The result of time resolution, the Wiener filter appeared the finest value, but had no significant difference compared to FNLM algorithm. In conclusion, our results confirmed that the proposed FNLM noise reduction algorithm can acquire both improved image quality and high processing time in MRI imaging with the DWI technique.
Abstract Magnetic resonance imaging (MRI) has many advantages and has developed various pulse sequences. In particular, the diffusion weighted image (DWI) technique is widely used because it can acquire images quickly during examination of stroke, through a proper adjustment of the diffusion-weighted gradient b-value. However, a setting with inappropriate b-value causes loss of image signal that increases the influence of noise. Therefore, in this study, we quantitatively evaluated image quality after applying a variety of algorithms to the image acquired by changing the b-value and the main magnetic field in the MRI device. To acquire the image, the phantom was self-produced with an acrylic panel and chicken breast. Wiener filter, total variation (TV), and our proposed fast non-local means (FNLM) noise reduction algorithms were applied to the image. Consequently, the signal intensity at a 3.0 T magnetic field increased by a factor 4.8 compared to a 1.5 T magnetic field. Moreover, the signal-to-noise ratio and contrast-to-noise ratio were highest with the FNLM algorithm, and the values increased by factors of 9.5 and 9.9 with a 1.5 T magnetic field and by factors of 9.9 and 5.0 with a 3.0 T magnetic field compared to the noise image, respectively. The result of time resolution, the Wiener filter appeared the finest value, but had no significant difference compared to FNLM algorithm. In conclusion, our results confirmed that the proposed FNLM noise reduction algorithm can acquire both improved image quality and high processing time in MRI imaging with the DWI technique.
Clin. Neurol. Neurosurg. Reiche 112 218 2010 10.1016/j.clineuro.2009.11.016 Differential diagnosis of intracranial ring enhancing cystic mass lesions-role of diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI)
NeuroImage Weiskopf 33 493 2006 10.1016/j.neuroimage.2006.07.029 Optimal EPI parameters for reduction of susceptibility-induced BOLD sensitivity losses: a whole-brain analysis at 3 T and 1.5 T
Magn. Reson. Med. Dietrich 45 448 2001 10.1002/1522-2594(200103)45:3<448::AID-MRM1059>3.0.CO;2-W Noise correction for the exact determination of apparent diffusion coefficients at low SNR
Comput. Biol. Med. Lemaitre 60 8 2015 10.1016/j.compbiomed.2015.02.009 Computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review
Optik Lee 172 456 2018 10.1016/j.ijleo.2018.07.051 X-ray image denoising with fast non-local means (FNLM) approach using acceleration function in CdTe semiconductor photon counting detector (PCD): monte Carlo simulation study
※ AI-Helper는 부적절한 답변을 할 수 있습니다.