Evaluation of several similarity measures for deformable image registration using T1-weighted MR images of the brain

2017 ◽  
Author(s):  
Wafa Boukellouz ◽  
Abdelouahab Moussaoui
Keyword(s):  
Image Registration ◽  
Similarity Measures ◽  
Deformable Image Registration ◽  
Mr Images ◽  
The Brain

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Iterative framework for image registration and partial volume correction in brain positron emission tomography

2020 ◽  
Vol 13 (4) ◽  
pp. 348-357
Author(s):  
Keisuke Matsubara ◽  
◽  
Masanobu Ibaraki ◽  
Miho Shidahara ◽  
Toshibumi Kinoshita
Keyword(s):  
Positron Emission Tomography ◽  
Image Registration ◽  
Standardized Uptake Value ◽  
Partial Volume Correction ◽  
Emission Tomography ◽  
Mr Images ◽  
Partial Volume ◽  
Volume Correction ◽  
Positron Emission ◽  
The Brain

AbstractImprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration. We applied PoR to the [11C]PiB PET data of 92 participants obtained from the Alzheimer’s Disease Neuroimaging Initiative database and compared the registration results, PV-corrected standardized uptake value (SUV) and its ratio to the cerebellum (SUVR), and intra-region coefficient of variation (CoV) between PoR and conventional registration. Significant differences in registration of as much as 2.74 mm and 3.02° were observed between the two methods (effect size <  − 0.8 or > 0.8), which resulted in considerable SUVR differences throughout the brain, reaching a maximal difference of 62.3% in the sensory motor cortex. Intra-region CoV was significantly reduced using the PoR throughout the brain. These results suggest that PoR reduces error as a result of imprecise registration in PVC and is a useful method for accurately quantifying the amyloid burden in PET.

Image Registration in Ultrasound-Assisted Brain Surgery

2018 ◽  
pp. 123-145
Author(s):  
Haradhan Chel ◽  
Prabin Kumar Bora
Keyword(s):  
Image Registration ◽  
Similarity Measures ◽  
Optimization Method ◽  
Brain Surgery ◽  
Deformation Model ◽  
Brain Images ◽  
Diseased Person ◽  
Preoperative Magnetic Resonance ◽  
Mr Brain Images ◽  
The Brain

Image registration is an essential step in the image guided brain surgery. A preoperative magnetic resonance (MR) image guides the neurosurgeon about the size and the location of the tumor inside the brain of the diseased person. Due to several reasons, brain shift occurs during the surgery, results in the shift of the actual position of the tumor. Intra-operative MR imaging is expensive and may not be financially viable for many hospitals. An effective intraoperative US can be used in replacement of MR. For performing registration of US and MR images, the most of the state-of-the-art methods use a suitable similarity or dissimilarity measure, a spline based deformation model, a smoothing technique and an effective fast optimization method. This chapter starts with a discussion on various types of brain tumors and their clinical significance. It also covers on various similarity measures, optimizations and the available database of US and MR brain images.

scholarly journals Learning-based deformable image registration for infant MR images in the first year of life

2017 ◽  
Vol 44 (1) ◽  
pp. 158-170 ◽  
Author(s):  
Shunbo Hu ◽  
Lifang Wei ◽  
Yaozong Gao ◽  
Yanrong Guo ◽  
Guorong Wu ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
First Year ◽  
Mr Images ◽  
First Year Of Life

scholarly journals MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION

2012 ◽  
Vol 31 (1) ◽  
pp. 43 ◽  
Author(s):  
Dejan Tomaževič ◽  
Boštjan Likar ◽  
Franjo Pernuš
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Spatial Information ◽  
Similarity Measures ◽  
Feature Space ◽  
Efficient Estimation ◽  
Mr Images ◽  
Feature Correspondence ◽  
Information Theoretic ◽  
Novel Approach

Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity.

OC-0444: Impact of deformable image registration on inter-fractional variations in lung cancer proton therapy

2020 ◽  
Vol 152 ◽  
pp. S245
Author(s):  
L. Nenoff ◽  
C.O. Ribeiro ◽  
M. Matter ◽  
L. Hafner ◽  
A.C. Knopf ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Registration ◽  
Proton Therapy ◽  
Deformable Image Registration

scholarly journals Magnetic Resonace–Based Attenuation Correction for Micro–Single-Photon Emission Computed Tomography

2012 ◽  
Vol 11 (2) ◽  
pp. 7290.2011.00036 ◽  
Author(s):  
Vincent Keereman ◽  
Yves Fierens ◽  
Christian Vanhove ◽  
Tony Lahoutte ◽  
Stefaan Vandenberghe
Keyword(s):  
Attenuation Correction ◽  
Single Photon ◽  
Photon Emission ◽  
The Body ◽  
Emission Computed Tomography ◽  
Micro Ct ◽  
Mr Images ◽  
Brain Scans ◽  
The Brain ◽  
Photon Emission Computed Tomography

Attenuation correction is necessary for quantification in micro–single-photon emission computed tomography (micro-SPECT). In general, this is done based on micro–computed tomographic (micro-CT) images. Derivation of the attenuation map from magnetic resonance (MR) images is difficult because bone and lung are invisible in conventional MR images and hence indistinguishable from air. An ultrashort echo time (UTE) sequence yields signal in bone and lungs. Micro-SPECT, micro-CT, and MR images of 18 rats were acquired. Different tracers were used: hexamethylpropyleneamine oxime (brain), dimercaptosuccinic acid (kidney), colloids (liver and spleen), and macroaggregated albumin (lung). The micro-SPECT images were reconstructed without attenuation correction, with micro-CT-based attenuation maps, and with three MR-based attenuation maps: uniform, non-UTE-MR based (air, soft tissue), and UTE-MR based (air, lung, soft tissue, bone). The average difference with the micro-CT-based reconstruction was calculated. The UTE-MR-based attenuation correction performed best, with average errors ≤ 8% in the brain scans and ≤ 3% in the body scans. It yields nonsignificant differences for the body scans. The uniform map yields errors of ≤ 6% in the body scans. No attenuation correction yields errors ≥ 15% in the brain scans and ≥ 25% in the body scans. Attenuation correction should always be performed for quantification. The feasibility of MR-based attenuation correction was shown. When accurate quantification is necessary, a UTE-MR-based attenuation correction should be used.

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