A new method for registrationbased medical image interpolation

Abstract—A new technique is presented for interpolating between grey-scale images in a medical data set. Registration between neighboring slices is achieved with a modified control grid interpolation algorithm that selectively accepts displacement field updates in a manner optimized for performance. A cubic interpolator is then applied to pixel intensities correlated by the displacement fields. Special considerations are made for efficiency, interpolation quality, and compression in the implementation of the algorithm. Experimental results show that the new method achieves good quality, while offering dramatic improvement in efficiency relative to the best competing method. Index Terms—Interpolation, reconstruction, registration. I

Improved neonatal brain MRI segmentation by interpolation of motion corrupted slices

BACKGROUND AND PURPOSE: To apply and evaluate an intensity‐based interpolation technique, enabling segmentation of motion‐affected neonatal brain MRI. METHODS: Moderate‐late preterm infants were enrolled in a prospective cohort study (Brain Imaging in Moderate‐late Preterm infants “BIMP‐study”) between August 2017 and November 2019. T2‐weighted MRI was performed around term equivalent age on a 3T MRI. Scans without motion (n = 27 [24%], control group) and with moderate‐severe motion (n = 33 [29%]) were included. Motion‐affected slices were re‐estimated using intensity‐based shape‐preserving cubic spline interpolation, and automatically segmented in eight structures. Quality of interpolation and segmentation was visually assessed for errors after interpolation. Reliability was tested using interpolated control group scans (18/54 axial slices). Structural similarity index (SSIM) was used to compare T2‐weighted scans, and Sørensen‐Dice was used to compare segmentation before and after interpolation. Finally, volumes of brain structures of the control group were used assessing sensitivity (absolute mean fraction difference) and bias (confidence interval of mean difference). RESULTS: Visually, segmentation of 25 scans (22%) with motion artifacts improved with interpolation, while segmentation of eight scans (7%) with adjacent motion‐affected slices did not improve. Average SSIM was .895 and Sørensen‐Dice coefficients ranged between .87 and .97. Absolute mean fraction difference was ≤0.17 for less than or equal to five interpolated slices. Confidence intervals revealed a small bias for cortical gray matter (0.14‐3.07 cm(3)), cerebrospinal fluid (0.39‐1.65 cm(3)), deep gray matter (0.74‐1.01 cm(3)), and brainstem volumes (0.07‐0.28 cm(3)) and a negative bias in white matter volumes (–4.47 to –1.65 cm(3)). CONCLUSION: According to qualitative and quantitative assessment, intensity‐based interpolation reduced the percentage of discarded scans from 29% to 7%

A Deep Learning-Based Fully Automated Pipeline for Regurgitant Mitral Valve Anatomy Analysis From 3D Echocardiography

Three-dimensional transesophageal echocardiography (3DTEE) is the recommended imaging technique for the assessment of mitral valve (MV) morphology and lesions in case of mitral regurgitation (MR) requiring surgical or transcatheter repair. Such assessment is key to thorough intervention planning and to intraprocedural guidance. However, it requires segmentation from 3DTEE images, which is timeconsuming, operator-dependent, and often merely qualitative. In the present work, a novel workflow to quantify the patient-specific MV geometry from 3DTEE is proposed. The developed approach relies on a 3D multi-decoder residual convolutional neural network (CNN) with a U-Net architecture for multi-class segmentation of MV annulus and leaflets. The CNN was trained and tested on a dataset comprising 55 3DTEE examinations of MR-affected patients. After training, the CNN is embedded into a fully automatic, and hence fully repeatable, pipeline that refines the predicted segmentation, detects MV anatomical landmarks and quantifies MV morphology. The trained 3D CNN achieves an average Dice score of 0.82 +/- 0.06, mean surface distance of 0.43 +/- 0.14 mm and 95% Hausdorff Distance (HD) of 3.57 +/- 1.56 mm before segmentation refinement, outperforming a state-of-the-art baseline residual U-Net architecture, and provides an unprecedented multi-class segmentation of the annulus, anterior and posterior leaflet. The automatic 3D linear morphological measurements of the annulus and leaflets, specifically diameters and lengths, exhibit differences of less than 1.45 mm when compared to ground truth values. These measurements also demonstrate strong overall agreement with analyses conducted by semi-automated commercial software. The whole process requires minimal user interaction and requires approximately 15 seconds

Three-dimensional Ultrasound Measurements of Carotid Atherosclerosis in Vulnerable Patient Populations

Atherosclerosis is a progressive disease that leads to plaque development and is associated with cardiovascular events such as myocardial infarction and stroke. Several biomarkers have been established as surrogates of plaque development yet none can provide direct, noninvasive, rapid measurements of atherosclerotic disease. Three-dimensional Ultrasound (3DUS) image acquisition is safe, inexpensive and fast, however 3DUS image measurements are limited due to time consuming manual image analyses. In addition, the true clinical meaning of 3DUS carotid imaging measurements has not yet been established. A semi-automated approach for the estimation of 3DUS Total Plaque Volume (TPV) was developed with similar variability and high agreement with manual measurements. 3DUS measurements such as Vessel Wall Volume (VWV) and TPV were shown to have similar associations of plaque and Intima-media Thickness (IMT) with age in males however this relationship did not exist in females. 3DUS measurements of carotid atherosclerosis can provide a more sensitive estimation of disease burden in vulnerable patient populations than traditional measures

Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images

Purpose: Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manual tracing to automated segmentations have also been investigated. Of those metrics, quantification of carotid plaques by measuring vessel-wall-volume (VWV) using the segmented media-adventitia (MAB) and lumen-intima (LIB) boundaries has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, semi-automatic MAB and LIB segmentation methods are required to help generate VWV measurements with high accuracy and less user interaction. Methods: In this paper, we propose a semiautomatic segmentation method based on deep learning to segment the MAB and LIB from carotid three-dimensional ultrasound (3DUS) images. For the MAB segmentation, we convert the segmentation problem to a pixel-by-pixel classification problem. A dynamic convolutional neural network (Dynamic CNN) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine-tuned dynamically in each test task. The LIB is segmented by applying a region-of-interest of carotid images to a U-Net model, which allows the network to be trained end-to-end for pixel-wise classification. Results: A total of 144 3DUS images were used in this development, and a threefold cross-validation technique was used for evaluation of the proposed algorithm. The proposed algorithm-generated accuracy was significantly higher than the previous methods but with less user interactions. Comparing the algorithm segmentation results with manual segmentations by an expert showed that the average Dice similarity coefficients (DSC) were 96.46 ± 2.22% and 92.84 ± 4.46% for the MAB and LIB, respectively, while only an average of 34 s (vs 1.13, 2.8 and 4.4 min in previous methods) was required to segment a 3DUS image. The interobserver experiment indicated that the DSC was 96.14 ± 1.87% between algorithm-generated MAB contours of two observers\u27 initialization. Conclusions: Our results showed that the proposed carotid plaque segmentation method obtains high accuracy and repeatability with less user interactions, suggesting that the method could be used in clinical practice to measure VWV and monitor the progression and regression of carotid plaques

Reduced cingulate gyrus volume in Cavalier King Charles Spaniels with syringomyelia and neuropathic pain revealed by voxel-based morphometry: a pilot study

Objective: Pathomorphological alterations of the central nervous system in dogs, such as syringomyelia and Chiari-like malformation, can cause cranial and cervical hyperesthesia and neuropathic pain. The long-term activity of the pain network can induce functional alteration and eventually even morphological changes in the pain network. This may happen especially in the prefrontal and cingulate cortex, where atrophy of the gray matter (GM) was observed in humans with chronic pain, irrespective of the nature of the pain syndrome. We tested the hypothesis that Cavalier King Charles Spaniels (CKCS) with Chiari-like malformation and associated syringomyelia (SM) and pain show cerebral morphological differences compared to animals without signs of syringomyelia and pain. Methods: Volumetric datasets of 28 different brain structures were analyzed in a retrospective manner, including voxel-based morphometry, using magnetic resonance imaging data obtained from 41 dogs. Results: Volumetric analyses revealed a decrease in GM volumes in the cingulate gyrus (CG) in CKCS with SM and chronic pain when normalized to brain volume. This finding was supported by voxel-based morphometry, which showed a cluster of significance within the CG. Conclusion: GM atrophy in the CG is associated with chronic pain and thus may serve as an objective readout parameter for the diagnosis or treatment of canine pain syndromes

Optimal method for fetal brain age prediction using multiplanar slices from structural magnetic resonance imaging

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979

3-D data handling and registration of multiple modality medical images

The many different clinical imaging modalities used in diagnosis and therapy deliver two different types of information: morphological and functional. Clinical interpretation can be assisted and enhanced by combining such information (e.g. superimposition or fusion). The handling of such data needs to be performed in 3-D. Various methods for registration developed by other authors are reviewed and compared. Many of these are based on registering external reference markers, and are cumbersome and present significant problems to both patients and operators. Internal markers have also been used, but these may be very difficult to identify. Alternatively, methods based on the external surface of an object have been developed which eliminate some of the problems associated with the other methods. Thus the methods which have been extended, developed, and described here, are based primarily on the fitting of surfaces, as determined from images obtained from the different modalities to be registered. Annex problems to that of the surface fitting are those of surface detection and display. Some segmentation and surface reconstruction algorithms have been developed to identify the surface to be registered. Surface and volume rendering algorithms have also been implemented to facilitate the display of clinical results. An iterative surface fitting algorithm has been developed based on the minimization of a least squares distance (LSD) function, using the Powell method and alternative minimization algorithms. These algorithms and the qualities of fit so obtained were intercompared. Some modifications were developed to enhance the speed of convergence, to improve the accuracy, and to enhance the display of results during the process of fitting. A common problem with all such methods was found to be the choice of the starting point (the initial transformation parameters) and the avoidance of local minima which often require manual operator intervention. The algorithm was modified to apply a global minimization by using a cumulative distance error in a sequentially terminated process in order to speed up the time of evaluating of each search location. An extension of the algorithm into multi-resolution (scale) space was also implemented. An initial global search is performed at coarse resolution for the 3-D surfaces of both modalities where an appropriate threshold is defined to reject likely mismatch transformations by testing of only a limited subset of surface points. This process is used to define the set of points in the transformation space to be used for the next level of resolution, again with appropriately chosen threshold levels, and continued down to the finest resolution level. All these processes were evaluated using sets of well defined image models. The assessment of this algorithm for 3-D surface registration of data from (3-D) MRI with MRI, MRI with PET, MRI with SPECT, and MRI with CT data is presented, and clinical examples are illustrated and assessed. In the current work, the data from multi-modality imaging of two different types phantom (e.g. Hoffman brain phantom, Jaszczak phantom), thirty routinely imaged patients and volunteer subjects, and ten patients with setting external markers on their head were used to assess and verify 3-D registration. The accuracy of the sequential multi-resolution method obtained by the distance values of 4-10 selected reference points on each data set gave an accuracy of 1.44±0.42 mm for MR-MR, 1.82±0.65 for MR-CT, 2.38±0.88 for MR-PET, and 3.17±1.12 for MR-SPECT registration. The cost of this process was determined to be of the order of 200 seconds (on a Micro-VAX II), although this is highly dependent on some adjustable parameters of the process (e.g. threshold and the size of the geometrical transformation space) by which the accuracy is aimed

Spatial Normalization of Diffusion Tensor MRI Using Multiple Channels

Diffusion Tensor MRI (DT-MRI) can provide important in vivo information for the detection of brain abnormalities in diseases characterized by compromised neural connectivity. To quantify diffusion tensor abnormalities based on voxel-based statistical analysis, spatial normalization is required to minimize the anatomical variability between studied brain structures. In this article, we used a multiple input channel registration algorithm based on a demons algorithm and evaluated the spatial normalization of diffusion tensor image in terms of the input information used for registration. Registration was performed on 16 DT-MRI data sets using different combinations of the channels, including a channel of T2-weighted intensity, a channel of the fractional anisotropy, a channel of the difference of the first and second eigenvalues, two channels of the fractional anisotropy and the trace of tensor, three channels of the eigenvalues of the tensor, and the six channel tensor components. To evaluate the registration of tensor data, we defined two similarity measures, i.e., the endpoint divergence and the mean square error, which we applied to the fiber bundles of target images and registered images at the same seed points in white matter segmentation. We also evaluated the tensor registration by examining the voxel-by-voxel alignment of tensors in a sample of 15 normalized DT-MRIs. In all evaluations, nonlinear warping using six independent tensor components as input channels showed the best performance in effectively normalizing the tract morphology and tensor orientation. We also present a nonlinear method for creating a group diffusion tensor atlas using the average tensor field and the average deformation field, which we believe is a better approach than a strict linear one for representing both tensor distribution and morphological distribution of the population.ope

Effects of different segmentation methods on geometric morphometric data collection from primate skulls

1. An increasing number of studies are analysing the shapes of objects using geometric morphometrics with tomographic data, which are often segmented and transformed to three‐dimensional (3D) surface models before measurement. This study aimed to evaluate the effects of different image segmentation methods on geometric morphometric data collection using computed tomography data collected from non‐human primate skulls. 2. Three segmentation methods based on a visually selected threshold, a half‐maximum height protocol and a gradient and watershed algorithm were compared. For each method, the efficiency of surface reconstruction, the accuracy of landmark placement and the level of variation in shape and size compared with various levels of biological variation were evaluated. 3. The visual‐based method inflated the surface in high‐density anatomical regions, whereas the half‐maximum height protocol resulted in a large number of artificial holes and erosion. However, the gradient‐based method mitigated these issues and generated the most efficient surface model. The segmentation method used had a much smaller effect on shape and size variation than interspecific and inter‐individual differences. However, this effect was statistically significant and not negligible when compared with intra‐individual (fluctuating asymmetric) variation. 4. Although the gradient‐based method is not widely used in geometric morphometric analyses, it may be one of promising options for reconstructing 3D surfaces. When evaluating small variations, such as fluctuating asymmetry, care should be taken around combining 3D data that were obtained using different segmentation methods