- •Image restoration algorithm to incorporate denoising and deblurring methods.
- •Our proposed algorithm can improve diagnostic accuracy for Alzheimer’s disease.
- •Improving the image quality of Alzheimer's disease using the PET image with ADNI.
- •Numerous evaluations in the field are taking advantage of our algorithm.
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- Feasibility of total variation noise reduction algorithm according to various MR-based PET images in a simultaneous PET/MR system: A phantom study.Diagnostics. 2021; 11https://doi.org/10.3390/diagnostics11020319
- Imaging in anatomy: a comparison of imaging techniques in embalmed human cadavers.BMC Med Educ. 2013; 13https://doi.org/10.1186/1472-6920-13-143
- PET/SPECT: functional imaging beyond flow.Vision Res. 2001; 41: 1277-1281
- Multi-atlas cardiac PET segmentation.Physica Med. 2019; 58: 32-39
- Development and initial results of a brain PET insert for simultaneous 7-Tesla PET/MRI using an FPGA-only signal digitization method.IEEE Trans Med Imaging. 2021; 40: 1579-5790
- Low-dose CT for the spatial normalization of PET images: a validation procedure for amyloid-PET semi-quantification.Clinl NeuroImage. 2018; 20: 153-160
- Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR.Physica Med. 2022; 97: 36-43
- The use of PET in Alzheimer disease.Nature Rev Neurol. 2020; 6: 78-87
- Brain PET in the diagnosis of Alzheimer’s disease.Clin Nucl Med. 2014; 39: e413-e426
- Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias.Lancet. 2020; 19: 951-962
- A future for PET imaging in Alzheimer’s disease.Eur J Nucl Med Mol Imaging. 2020; 47: 231-234
- Centiloid scale analysis for 18F-THK5351 PET imaging in Alzheimer’s disease.Physica Med. 2021; 82: 249-254
- The role of PET scan in diagnosis, staging, and management of non-small cell lung cancer.Oncologist. 2004; 9: 633-643
- Metabolic PET imaging in cancer detection and therapy response.Semin Oncol. 2011; 38: 55-69
- Clinical PET imaging in prostate cancer.RadioGraphics. 2017; 37: 1512-1536
- 18F-FDG PET-based imaging of myocardial inflammation following acute myocardial infarction in a mouse model.Internat J Mol Sci. 2020; 21https://doi.org/10.3390/ijms21093340
- 11C-Methionine PET of acute myocardial infarction.J Nucl Med. 2009; 50: 1283-1287
- Alzheimer’s disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein.Biochem Biophys Res Commun. 1984; 120: 885-890
- Amyloid beta: structure, biology and structure-based therapeutic development.Acta Pharmacol Sin. 2017; 38: 1205-1235
- Reproducibility and repeatability of magnetic resonance imaging in dementia.Physica Med. 2022; 101: 8-17
- Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias.J Nucl Med. 2008; 49: 390-398
- Amyloid-b imaging with pittsburgh compound B and florbetapir: comparing radiotracers and quantification methods.J Nucl Med. 2013; 54: 70-77
- Comparison of 11C-PiB and 18F-florbetaben for Aβ imaging in ageing and Alzheimer’s disease.Eur J Nucl Med Mol Imaging. 2012; 39: 983-989
- Dynamic PET image denoising using deep convolutional neural networks without prior training datasets.IEEE Access. 2019; 7: 96594-96603
- PET image denoising using unsupervised deep learning.Eur J Nucl Med Mol Imaging. 2019; 46: 2780-2789
- Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system.Nucl Eng Technol. 2021; 53: 2341-2347
- PET image deblurring and super-resolution with an MR-based joint entropy prior.IEEE Trans Comput Imaging. 2019; 5: 530-539
Cadena L, Zotin A, Cadena F, Espinosa N. Espinosa, Noise removal of the x-ray medical image using fast spatial filters and GPU. Proceeding of SPIE 10752, Applications of Digital Image Processing XLI 2019;35:176-186.
- Nonlinear total variation based noise removal algorithms.Physica D. 1992; 60: 259-268
- An iterative regularization method for total variation-based image restoration.Multiscale Model Simul. 2005; 4: 460-489
- Evaluation of non-local means based denoising filters for diffusion kurtosis imaging using a new phantom.PLoS ONE. 2015; 10: e0116986
Pal C, Chakrabarti A, Ghosh R. A brief survey of recent edge-preserving smoothing algorithms on digital images 2015: arXiv:1503.072976.
Wang L, Lu J, Li Y, Yahagi T, Okamoto T. Noise reduction using wavelet with application to medical x-ray image. 2015 IEEE International Conference on Industrial Technology, Hong Kong, China 2005: doi:10.1109/ICIT.2005.1600606.
- The nonsubsampled contourlet transform: theory, design, and applications.IEEE Trans Image Process. 2006; 15: 3089-3101
- A new wavelet threshold function and denoising application.Math Probl Eng. 2016; https://doi.org/10.1155/2016/3195492
- Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising.IEEE Trans Image Process. 2017; 26: 3142-3155
- A deep convolutional neural network using directional wavelets for low-dose x-ray CT reconstruction.Med Phys. 2018; 44: e360-e375
- Non-blind image deblurring method by local and nonlocal total variation models.Signal Process. 2014; 94: 339-349
- Variational Bayesian blind image deconvolution: A review.Digital Signal Process. 2015; 47: 116-127
Chen L, Zhang J, Lin S, Fang F, Ren JS. Blind deblurring for saturated images. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, virtual 2021:6308-6316.
Levin A, Weiss Y, Durand F, Freeman WT. Understanding and evaluating blind deconvolution algorithm. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA 2009:1964-1971.
Xu L, Zheng S, Jia J. Unnatural L0 sparse representation for natural image deblurring. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA 2016:1628-1636.
Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA 2011:20-25.5.
- Image deblurring via enhanced low-rank prior.IEEE Trans Image Process. 2016; 25: 3426-3437
- Blur kernel estimation via salient edges and low rank prior for blind image deblurring.Signal Process Image Commun. 2017; 58: 134-145
Sun J, Cao W, Xu Z, Ponce J. Learning a convolutional neural network for non-uniform motion blur removal. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA 2015:769-777.
- Blind deblurring with sparse representation via external patch priors.Digital Signal Process. 2018; 78: 322-331
Sun S, Xu Z, Zhang J. Spectral norm regularization for blind image deblurring. Symmetry 2021;13: doi.org/10.3390/sym13101856.
Ren D, Zhang K, Wang Q, Hu Q, Zuo W. Neural blind deconvolution using deep priors. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, virtual 2020: doi:10.1109/CVPR42600.2020.00340.
- Image super-resolution via deep recursive residual network.in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA. 2017: 3147-3155
- On the convergence of learning-based iterative methods for nonconvex inverse problems.IEEE Trans Pattern Anal Machin Intell. 2020; 42: 3027-3039
Chakrabarti A. A neural approach to blind motion deblurring. In European Conference on Computer Vision, Amsterdam, Netherlands 2016;221-235.
- Scale-recurrent network for deep image deblurring.in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA. 2018: 8174-8182
- Non-local mean denoising using multiple PET reconstructions.Ann Nucl Med. 2021; 35: 176-186
Buades A, Coll B, Morel JM. A non-local algorithm for image denoising. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA 2005: doi:10.1109/CVPR.2005.38.
- Non-local means denoising of dynamic PET images.PLoS ONE. 2013; 8: e81390
- MRI denoising using non-local means.Med Image Anal. 2008; 12: 514-523
- A blind-deblurring method based on a compressed-sensing scheme in digital breast tomosynthesis.Opt Lasers Eng. 2018; 110: 228-235
- Total variation as a local filter.SIAM J Imag Sci. 2011; 4: 651-694
- An augmented lagrangian method for total variation video restoration.IEEE Trans Image Process. 2011; 20: 3097-3111
- Making a “Completely Blind” image quality analyzer.IEEE Signal Process Lett. 2013; 20: 209-212
- No-reference image quality assessment in the spatial domain.IEEE Trans Image Process. 2012; 21: 4695-4708
- Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient.Phys Med. 2021; 81: 52-59
- Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease.Sci Rep. 2017; 7https://doi.org/10.1038/s41598-017-13339-7
- Application of blind deconvolution based on the new weighted L1-norm regularization with alternating direction method of multipliers in light microscopy images.Microsc Microanal. 2020; 26: 929-937
- Quantitative evaluation of the image quality using the fast nonlocal means denoising approach in diffusion-weighted magnetic resonance imaging with high b-value.J Korean Phys Soc. 2021; 78: 244-250