Inpainting as a Technique for Estimation of Missing Voxels in Brain Imaging

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI) can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels, and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). In order to evaluate the performance across varying image contrasts and spatial resolutions, we applied the same techniques to a T1-weighted MRI brain dataset, and N -acetylaspartate (NAA) spectroscopy maps from a CSI dataset. For all image types, inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered ( p ’s < 0.001). Inpainting maintained an average NRMSE of less than 5% even with 50% missing voxels, whereas the other techniques demonstrated up to three times that value, depending on the nature of the image. For CSI maps, inpainting maintained its superiority whether the previously unusable voxels were randomly distributed, or located in regions most commonly affected by voxel loss in real-world data. Inpainting is a promising approach for recovering unusable or missing voxels in voxel-wise analyses, particularly in imaging modalities characterized by low SNR such as CSI. We hypothesize that this technique may also be applicable for datasets from other imaging modalities, such as positron emission tomography, or dynamic susceptibility contrast MRI.

[1]  Michael Unser,et al.  Image interpolation and resampling , 2000 .

[2]  B. Meier,et al.  Computer Simulations in Magnetic Resonance. An Object-Oriented Programming Approach , 1994 .

[3]  L. Astrakas,et al.  Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping , 2010, Pediatric Radiology.

[4]  J. A. Parker,et al.  Comparison of Interpolating Methods for Image Resampling , 1983, IEEE Transactions on Medical Imaging.

[5]  Paul J. Laurienti,et al.  Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data , 2010, NeuroImage.

[6]  Robert R. Edwards,et al.  Brain glial activation in fibromyalgia – A multi-site positron emission tomography investigation , 2019, Brain, Behavior, and Immunity.

[7]  B. Rosen,et al.  Evidence for brain glial activation in chronic pain patients. , 2015, Brain : a journal of neurology.

[8]  Yi Liu,et al.  A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations , 2012, Environ. Model. Softw..

[9]  D. Weinberger,et al.  Analysis of interpolation effects in the reslicing of functional MR images. , 1997, Journal of computer assisted tomography.

[10]  A. Kucyi,et al.  The neuroinflammatory component of negative affect in patients with chronic pain , 2019, Molecular Psychiatry.

[11]  Guillermo Sapiro,et al.  Structure and texture filling-in of missing image blocks in wireless transmission and compression applications , 2003, IEEE Trans. Image Process..

[12]  Guillermo Sapiro,et al.  Simultaneous structure and texture image inpainting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[13]  Patrick Pérez,et al.  Region filling and object removal by exemplar-based image inpainting , 2004, IEEE Transactions on Image Processing.

[14]  V. Calhoun,et al.  Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia , 2005, Schizophrenia Research.

[15]  Damien Garcia,et al.  Robust smoothing of gridded data in one and higher dimensions with missing values , 2010, Comput. Stat. Data Anal..

[16]  Kirby G. Vosburgh,et al.  3D Slicer: A Platform for Subject-Specific Image Analysis, Visualization, and Clinical Support , 2014 .

[17]  Danielle Graveron-Demilly,et al.  Quantification in magnetic resonance spectroscopy based on semi-parametric approaches , 2014, Magnetic Resonance Materials in Physics, Biology and Medicine.

[18]  Christian Beaulieu,et al.  Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment , 2007, NeuroImage.

[19]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[20]  Wolfgang Bogner,et al.  Real-time motion- and B0-correction for LASER-localized spiral-accelerated 3D-MRSI of the brain at 3T , 2014, NeuroImage.

[21]  Eva-Maria Ratai,et al.  Spectroscopic imaging with improved gradient modulated constant adiabaticity pulses on high-field clinical scanners. , 2010, Journal of magnetic resonance.

[22]  Omar ElHarrouss,et al.  Image Inpainting: A Review , 2019, Neural Processing Letters.

[23]  S. Provencher Estimation of metabolite concentrations from localized in vivo proton NMR spectra , 1993, Magnetic resonance in medicine.

[24]  T. Chan,et al.  Variational image inpainting , 2005 .

[25]  A Gregory Sorensen,et al.  Neurologic 3D MR spectroscopic imaging with low-power adiabatic pulses and fast spiral acquisition. , 2012, Radiology.

[26]  Marco L. Loggia,et al.  Inpainting as a Technique for Estimation of Missing Voxels in Chemical Shift Imaging , 2020, bioRxiv.

[27]  Sébastien Ourselin,et al.  A multi-time-point modality-agnostic patch-based method for lesion filling in multiple sclerosis , 2016, NeuroImage.

[28]  Dong Liu,et al.  Image Compression With Edge-Based Inpainting , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[29]  D. Louis Collins,et al.  Non-Local Means Inpainting of MS Lesions in Longitudinal Image Processing , 2015, Front. Neurosci..

[30]  Christine Guillemot,et al.  Image Inpainting : Overview and Recent Advances , 2014, IEEE Signal Processing Magazine.

[31]  Thierry Blu,et al.  Chapter 28 – Image Interpolation and Resampling , 2009 .

[32]  M. Sdika,et al.  Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping , 2009, Human brain mapping.

[33]  Bin Yang,et al.  Adversarial Inpainting of Medical Image Modalities , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).