An objective comparison of 3-D image interpolation methods

NExpR: Neural Explicit Representation for fast arbitrary-scale medical image super-resolution

Medical images often require rescaling to various spatial resolutions to ensure interpretations at different levels. Conventional deep learning-based image super-resolution (SR) enhances the fixed-scale resolution. Implicit neural representation (INR) is a promising way of achieving arbitrary-scale image SR. However, existing INR-based methods require the repeated execution of the neural network (NN), which is slow and inefficient. In this paper, we present Neural Explicit Representation (NExpR) for fast arbitrary-scale medical image SR. Our algorithm represents an image with an explicit analytical function, whose input is the low-resolution image and output is the parameterization of the analytical function. After obtaining the analytical representation through a single NN inference, SR images of arbitrary scales can be derived by evaluating the explicit functions at desired coordinates. Because of the analytical explicit representation, NExpR is significantly faster than INR-based methods. In addition to speed, our method achieves on-par or better image quality than other strong competitors. Extensive experiments on Magnetic Resonance Imaging (MRI) datasets, including ProstateX, fastMRI, and our in-house clinical prostate dataset, as well as the Computerized Tomography (CT) dataset, specifically the Medical Segmentation Decathlon (MSD) liver dataset, demonstrate the superiority of our method. Our method reduces the rescaling time from the order of 1 ms to the order of 0.01 ms, achieving an over 100× speedup without losing the image quality. Code is available at https://github.com/Calvin-Pang/NExpR.

Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening

Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening.

Flexible Electromagnetic Cap for Three-Dimensional Electromagnetic Head Imaging

The timely treatment is the crucial element for the survival of patients with brain stroke. Thus, a fast, cost-effective, and portable device is needed for the early and on-the-spot diagnosis of stroke patients. A 3D electromagnetic head imaging system for rapid brain stroke diagnosis with a wearable and lightweight platform is presented. The platform comprises a custom-built flexible cap with a 24-element planar antenna array, and a flexible matching medium layer. The custom-built cap is made out of an engineered polymer-ceramic composite substrate of RTV silicone rubber and aluminum oxide (Al₂O₃) for enhanced dielectric properties and mechanical flexibility and robustness. The array is arranged into two elliptical rings that are entirely incorporated into the flexible cap. The employed antenna elements within the system are compact with low SAR values over the utilized frequency range of 0.9-2.5 GHz. Moreover, a flexible matching medium layer is introduced on the front of the apertures of the antenna array to enhance the impedance matching with the skin. The detection capability of the system is experimentally verified on 3D realistic head phantoms at multiple imaging scenarios and different types of strokes. The reconstructed 3D and 2D multi-slice images using the beamforming and polar sensitivity encoding (PSE) image processing algorithms indicate the applicability and potential of the system for onsite brain imaging.

Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets

Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes. In I2-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold, conditioned on intensity features through a generator net. The generator comprises residual blocks with normalisation layers that are modulated with auxiliary slice information, enabling propagation of fine details through the network. In addition, a multi-scale discriminator was implemented, along with a discriminator-based feature matching loss, to further enhance performance and encourage the synthesis of visually realistic slices. Experimental results show that our method achieves significant improvements over the state-of-the-art, in missing slice imputation for CMR, with an average SSIM of 0.872. Linear regression analysis yields good agreement between reference and imputed CMR images for all cardiac measurements, with correlation coefficients of 0.991 for left ventricular volume, 0.977 for left ventricular mass and 0.961 for right ventricular volume.

Volumetric interpolation of tomographic sequences for accurate 3D reconstruction of anatomical parts

BACKGROUND AND OBJECTIVE

Tomographic sequences of biomedical images are commonly used to achieve a three-dimensional visualization of the human anatomy. In some cases, the number of images contained in the sequence is limited, e.g., in low-dose computed tomography acquired on neonatal patients, resulting in a coarse and inaccurate 3D reconstruction.

METHODS

In this paper, volumetric image interpolation methods, devised to increase the axial resolution of tomographic sequences and achieve a refined 3D reconstruction, are proposed and compared. The techniques taken into consideration are based on motion-compensated frame-interpolation concepts, which have been developed for video applications, mainly frame-rate conversion.

RESULTS

The performance of the proposed methods is quantitatively assessed by using sequences with a simulated low axial resolution obtained from the decimation of standard high-resolution computed tomography sequences. Real data with an actual low axial resolution have been used as well for a qualitative evaluation of the proposed methods.

CONCLUSIONS

The experimental results demonstrate that the proposed methods enable an effective slice interpolation and that the achievable 3D models clearly benefit from the increased axial resolution.

3D Visualization of Brain Tumors Using MR Images: A Survey

BACKGROUND

Three-Dimensional visualization of brain tumors is very useful in both diagnosis and treatment stages of brain cancer.

DISCUSSION

It helps the oncologist/neurosurgeon to take the best decision in Radiotherapy and/or surgical resection techniques. 3D visualization involves two main steps; tumor segmentation and 3D modeling.

CONCLUSION

In this article, we illustrate the most widely used segmentation and 3D modeling techniques for brain tumors visualization. We also survey the public databases available for evaluation of the mentioned techniques.

A Fast Segmentation and Efficient Slice Reconstruction Technique for Head CT Images

The three-dimensional (3D) reconstruction of medical images usually requires hundreds of two-dimensional (2D) scan images. Segmentation, an obligatory part in reconstruction, needs to be performed for all the slices consuming enormous storage space and time. To reduce storage space and time, this paper proposes a three-stage procedure, namely, slice selection, segmentation and interpolation. The methodology will have the potential to 3D reconstruct the human head from minimum selected slices. The first stage of slice selection is based on structural similarity measurement, discarding the most similar slices with none or minimal impact on details. The second stage of segmentation of the selected slices is performed using our proposed phase-field segmentation method. Validation of our segmentation results is done via comparison with other deformable models, and results show that the proposed method provides fast and accurate segmentation. The third stage of interpolation is based on modified curvature registration-based interpolation, and it is applied to re-create the discarded slices. This method is compared to both standard linear interpolation and registration-based interpolation in 100 tomographic data sets. Results show that the modified curvature registration-based interpolation reconstructs missing slices with 96% accuracy and shows an improvement in sensitivity (95.802%) on par with specificity (95.901%).

A novel semiautomatic segmentation protocol to evaluate guided bone regeneration outcomes: A pilot randomized, controlled clinical trial

OBJECTIVES

The aims of this study were to (a) present a novel morphological contour interpolation (MCI) algorithm based method to evaluate grafted bone alterations following guided bone regeneration (GBR), (b) compare clinical and radiological outcomes of GBR with two different collagen membranes.

MATERIALS AND METHODS

The data were retrieved from an ongoing randomized controlled trial. Patients were randomly allocated into two groups: (a) control group (CG): Bio-Gide (b) test group (TG): bovine dermis-derived collagen membrane. Cone beam computed tomography examinations were performed 1 week (T0) and 6 months after surgery (T1). PES/WES at T1, grafted bone volume and density changes from T0 to T1 were recorded.

RESULTS

Thirty-six patients (16/20 in test/control group, respectively) were enrolled in the present study. Excellent inter-observer reliability (ICC ≥ 0.97) was revealed for repeated measurements using this method. Significant volumetric reduction of grafted bone were found in both groups (test group: from 0.60 to 0.39 cm³ , p < 0.01; control group: from 0.54 to 0.31 cm³ , p < 0.01). Mean bone density (gray-scale values) significantly increased from 305.12 to 456.69 in CG (p < 0.01). In TG, it slightly increased from 304.75 to 393.27 (p = 0.25). The mean PES/WES values were 13.84 (6.62/7.22) and 13.90 (6.70/7.20) for TG and CG, respectively. As for inter-group comparison, no significant differences of grafted bone volume change, density change and PES/WES were found between two groups.

CONCLUSION

Within the limitations of this study, the novel MCI-based method is a reproducible tool to segment and visualize changes of grafted bone in 3D. Furthermore, both collagen membranes could be used as a barrier membrane for GBR in humans.

Three-Dimensional Electromagnetic Torso Scanner

A three-dimensional (3D) electromagnetic torso scanner system is presented. This system aims at providing a complimentary/auxiliary imaging modality to supplement conventional imaging devices, e.g., ultrasound, computerized tomography (CT) and magnetic resonance imaging (MRI), for pathologies in the chest and upper abdomen such as pulmonary abscess, fatty liver disease and renal cancer. The system is comprised of an array of 14 resonance-based reflector (RBR) antennas that operate from 0.83 to 1.9 GHz and are located on a movable flange. The system is able to scan different regions of the chest and upper abdomen by mechanically moving the antenna array to different positions along the long axis of the thorax with an accuracy of about 1 mm at each step. To verify the capability of the system, a three-dimensional imaging algorithm is proposed. This algorithm utilizes a fast frequency-based microwave imaging method in conjunction with a slice interpolation technique to generate three-dimensional images. To validate the system, pulmonary abscess was simulated within an artificial torso phantom. This was achieved by injecting an arbitrary amount of fluid (e.g., 30 mL of water), into the lungs regions of the torso phantom. The system could reliably and reproducibly determine the location and volume of the embedded target.

The effect of motion correction interpolation on quantitative T1 mapping with MRI

Quantitative magnetic resonance imaging (qMRI) is a technique for mapping the physical properties of the underlying tissue using several MR images with different contrasts. To overcome subject motion between the acquired images, it is necessary to register the images to a common reference frame. A drawback of registration is the use of interpolation and resampling techniques, which can introduce artifacts into the interpolated data. These artifacts could have unfavorable effects on the accuracy of the estimated tissue's physical properties. Here, we quantified the error of interpolation and resampling on T1-weighted images and studied its effects on the mapping of the longitudinal relaxation time (T1) using variable flip angles. We simulated T1-weighted images and calculated the transformation error resulting from interpolation and resampling. We found that the error is a function of the image contrast (i.e., flip angle) and of the translation and rotation of the image. Furthermore, we found that the error in the T1-weighted images has a substantial effect on the T1 estimation, of the order of 10% of the signal in the brain's gray and white matter. Hence, minimizing the registration error can enable more accurate in vivo modeling of brain microstructure.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

A new formula for bivariate Hermite interpolation on variable step grids and its application to image interpolation

In this paper, we present a novel formula of the bivariate Hermite interpolating (BHI) polynomial in the case of support points arranged on a grid with variable step. This expression is applicable when interpolation of a bivariate function is required, given its value and the values of its partial derivatives of arbitrarily high order, at the support points. The proposed formula is a generalization of an existing formula for the bivariate Hermite polynomial. It is also algebraically much simpler, thus can be computed more efficiently. In order to apply Hermite interpolation to image interpolation, we simplify the proposed (BHI) to handle support points on a regular unit-step grid. The values of image partial derivatives are arithmetically approximated using compact finite differences. The proposed method is being assessed in a number of image interpolation experiments that include a synthetic image, for which the values of the partial derivatives are computed analytically, as well as a collection of images from different medical modalities. The proposed BHI with up to second-order image partial derivatives, outperforms the convolution-based interpolation methods, as well as generalized interpolation methods with the same number of support points that was compared with, in the majority of image interpolation experiments. The computational load of the proposed BHI is calculated and its behaviour with respect to its controlling parameters is investigated.

Data-adapted moving least squares method for 3-D image interpolation

In this paper, we present a nonlinear three-dimensional interpolation scheme for gray-level medical images. The scheme is based on the moving least squares method but introduces a fundamental modification. For a given evaluation point, the proposed method finds the local best approximation by reproducing polynomials of a certain degree. In particular, in order to obtain a better match to the local structures of the given image, we employ locally data-adapted least squares methods that can improve the classical one. Some numerical experiments are presented to demonstrate the performance of the proposed method. Five types of data sets are used: MR brain, MR foot, MR abdomen, CT head, and CT foot. From each of the five types, we choose five volumes. The scheme is compared with some well-known linear methods and other recently developed nonlinear methods. For quantitative comparison, we follow the paradigm proposed by Grevera and Udupa (1998). (Each slice is first assumed to be unknown then interpolated by each method. The performance of each interpolation method is assessed statistically.) The PSNR results for the estimated volumes are also provided. We observe that the new method generates better results in both quantitative and visual quality comparisons.

Hermite kernels for slice interpolation in medical images

Univariate Hermite interpolation of the total degree (HTD) is an algebraically demanding interpolation method that utilizes information of the values of the signal to be interpolated at distinct support positions, as well as the values of its derivatives up to a maximum available order. In this work the interpolation kernels of the univariate HTD are derived, using several approximations of the 1st and 2nd order of discrete signal derivative. We assess the derived Hermite kernels in the task of medical image slice interpolation, against several other well established interpolation techniques. Results show that specific Hermite kernels can outperform other established interpolation methods with similar computational complexity, in terms of root mean square error (RMSE), in a number of interpolation experiments, resulting in higher accuracy interpolated images.

Hierarchical patch-based sparse representation--a new approach for resolution enhancement of 4D-CT lung data

4D-CT plays an important role in lung cancer treatment because of its capability in providing a comprehensive characterization of respiratory motion for high-precision radiation therapy. However, due to the inherent high-dose exposure associated with CT, dense sampling along superior-inferior direction is often not practical, thus resulting in an inter-slice thickness that is much greater than in-plane voxel resolutions. As a consequence, artifacts such as lung vessel discontinuity and partial volume effects are often observed in 4D-CT images, which may mislead dose administration in radiation therapy. In this paper, we present a novel patch-based technique for resolution enhancement of 4D-CT images along the superior-inferior direction. Our working premise is that anatomical information that is missing in one particular phase can be recovered from other phases. Based on this assumption, we employ a hierarchical patch-based sparse representation mechanism to enhance the superior-inferior resolution of 4D-CT by reconstructing additional intermediate CT slices. Specifically, for each spatial location on an intermediate CT slice that we intend to reconstruct, we first agglomerate a dictionary of patches from images of all other phases in the 4D-CT. We then employ a sparse combination of patches from this dictionary, with guidance from neighboring (upper and lower) slices, to reconstruct a series of patches, which we progressively refine in a hierarchical fashion to reconstruct the final intermediate slices with significantly enhanced anatomical details. Our method was extensively evaluated using a public dataset. In all experiments, our method outperforms the conventional linear and cubic-spline interpolation methods in preserving image details and also in suppressing misleading artifacts, indicating that our proposed method can potentially be applied to better image-guided radiation therapy of lung cancer in the future.

CT dataset anisotropy management for oral implantology planning software

PURPOSE

Measurements accomplished on most oral implantology software are often affected by some systematic effects, of which those related to the CT dataset anisotropy are the most relevant. In fact, most of these commercial systems do not manage possible anisotropy in input datasets, leaving the responsibility to users and radiologists. Therefore, in order to achieve a better knowledge of the patient's anatomy before inserting the implants, and thus reducing the risk of damaging the surrounding structures, the implementation of a complete and precise anisotropy management system is required.

METHODS

We present an anisotropy management algorithm for pre-operative planning software that is able to handle any anisotropic CT dataset, and, as a result, provides a very precise isotropic equivalent. The developed algorithm exploits two interpolation passes to correct anisotropy and is characterised by linear complexity, needing just a few seconds to accomplish the tasks. The first pass concerns the integer-filling of possible intra-slice void spaces of the original slices, having the responsibility of a correct spreading of the radiographic details along the volume height axis. The second pass, instead, reformats its input dataset under isotropic conditions exploiting a contribution-based interpolation sub-algorithm.

RESULTS

The algorithm has been evaluated by comparing the anisotropy implied systematic effects for both anisotropic and interpolation-reconstructed radiographic volumes of five different scans. The proposed system demonstrated to be able to successfully handle any dataset interslice-pixel-size ratio. Moreover, the precision achieved proved to be even better than that of another precise algorithm that we previously developed and published, validating the proposed approach as a consequence.

CONCLUSIONS

The proposed algorithm makes it possible to handle and correct anisotropy in input CT datasets, helping to avoid anisotropy implied systematic effects on related measurements, and consequently supporting pre-operative planning software by providing a precise and isotropic equivalent volume on which to work.

Medical imaging correction: a comparative study of five contrast and brightness matching methods

Contrast and brightness matching are often required in many medical imaging applications, especially when comparing medical data acquired over different time periods, due to dissimilarities in the acquisition process. Numerous methods have been proposed in this field, ranging from simple correction filters to more complicated recursive techniques. This paper presents a comprehensive comparison of five methods for matching the contrast and brightness of medical image pairs, namely, Contrast Stretching, Ruttimann's Robust Film Correction, Boxcar Filtering, Least-Squares Approximation and Histogram Registration. The five methods were applied to a total of 100 image pairs, divided into five sets, in order to evaluate the performance of the compared methods on images with different levels of contrast, brightness and combinational contrast and brightness variations. Qualitative evaluation was performed by means of visual assessment on the corrected images as well as on digitally subtracted images, in order to estimate the deviations relative to the reference data. Quantitative evaluation was performed by pair-wise statistical evaluation on all image pairs in terms of specific features of merit based on widely used metrics. Following qualitative and quantitative analysis, it was deduced that the Histogram Registration method systematically outperformed the other four methods in comparison in most cases on average.

A Markov random field approach for topology-preserving registration: application to object-based tomographic image interpolation

This paper proposes a topology-preserving multiresolution elastic registration method based on a discrete Markov random field of deformations and a block-matching procedure. The method is applied to the object-based interpolation of tomographic slices. For that purpose, the fidelity of a given deformation to the data is established by a block-matching strategy based on intensity- and gradient-related features, the smoothness of the transformation is favored by an appropriate prior on the field, and the deformation is guaranteed to maintain the topology by imposing some hard constraints on the local configurations of the field. The resulting deformation is defined as the maximum a posteriori configuration. Additionally, the relative influence of the fidelity and smoothness terms is weighted by the unsupervised estimation of the field parameters. In order to obtain an unbiased interpolation result, the registration is performed both in the forward and backward directions, and the resulting transformations are combined by using the local information content of the deformation. The method is applied to magnetic resonance and computed tomography acquisitions of the brain and the torso. Quantitative comparisons offer an overall improvement in performance with respect to related works in the literature. Additionally, the application of the interpolation method to cardiac magnetic resonance images has shown that the removal of any of the main components of the algorithm results in a decrease in performance which has proven to be statistically significant.

Constrained reverse diffusion for thick slice interpolation of 3D volumetric MRI images

Due to physical limitations inherent in magnetic resonance imaging scanners, three dimensional volumetric scans are often acquired with anisotropic voxel resolution. We investigate several interpolation approaches to reduce the anisotropy and present a novel approach - constrained reverse diffusion for thick slice interpolation. This technique was compared to common methods: linear and cubic B-Spline interpolation and a technique based on non-rigid registration of neighboring slices. The methods were evaluated on artificial MR phantoms and real MR scans of human brain. The constrained reverse diffusion approach delivered promising results and provides an alternative for thick slice interpolation, especially for higher anisotropy factors.

Curvature interpolation method for image zooming

We introduce a novel image zooming algorithm, called the curvature interpolation method (CIM), which is partial-differential-equation (PDE)-based and easy to implement. In order to minimize artifacts arising in image interpolation such as image blur and the checkerboard effect, the CIM first evaluates the curvature of the low-resolution image. After interpolating the curvature to the high-resolution image domain, the CIM constructs the high-resolution image by solving a linearized curvature equation, incorporating the interpolated curvature as an explicit driving force. It has been numerically verified that the new zooming method can produce clear images of sharp edges which are already denoised and superior to those obtained from linear methods and PDE-based methods of no curvature information. Various results are given to prove effectiveness and reliability of the new method.

Adaptive-weighted cubic B-spline using lookup tables for fast and efficient axial resampling of 3D confocal microscopy images

Confocal laser scanning microscopy has become a most powerful tool to visualize and analyze the dynamic behavior of cellular molecules. Photobleaching of fluorochromes is a major problem with confocal image acquisition that will lead to intensity attenuation. Photobleaching effect can be reduced by optimizing the collection efficiency of the confocal image by fast z-scanning. However, such images suffer from distortions, particularly in the z dimension, which causes disparities in the x, y, and z directions of the voxels with the original image stacks. As a result, reliable segmentation and feature extraction of these images may be difficult or even impossible. Image interpolation is especially needed for the correction of undersampling artifact in the axial plane of three-dimensional images generated by a confocal microscope to obtain cubic voxels. In this work, we present an adaptive cubic B-spline-based interpolation with the aid of lookup tables by deriving adaptive weights based on local gradients for the sampling nodes in the interpolation formulae. Thus, the proposed method enhances the axial resolution of confocal images by improving the accuracy of the interpolated value simultaneously with great reduction in computational cost. Numerical experimental results confirm the effectiveness of the proposed interpolation approach and demonstrate its superiority both in terms of accuracy and speed compared to other interpolation algorithms.

Context-based interpolation of 3-D medical images

A context-based 3D interpolation technique is proposed to enhance the out-of plane resolution of 3D medical images. The proposed technique represents a new approach of aiding 3D interpolation and improving its performance by efficient use of domain knowledge about the anatomy, objects and imaging modalities. In the new approach a family of adaptive 3D interpolation filters are designed and conditioned on different spatial contexts (classes of feature vectors). Training is used to incorporate the domain knowledge into the design of these interpolators. Experimental results show significant improvement of the new approach over some existing 3D interpolation techniques.

Fusion of multi-planar images for improved three-dimensional object reconstruction

Due to the scan time limitation, our MRI studies of the human tongue can acquire only a limited number of contiguous two-dimensional (2D) slices to form a volumetric data set in a given series. An interpolated three-dimensional (3D) reconstruction using images acquired in a single plane presents artifacts. To address this issue, we developed a wavelet-based bidirectional linear fusion method that uses slices acquired from sagittal and coronal planes to estimate the unknown values of the inter-slice voxels. We use an interpolation method to estimate the voxel value based on neighboring fiducial voxels in the bounding slices. This interpolation is followed by a wavelet fusion to recover image details by integrating prominent coefficients from the interpolated images. Our method was evaluated using 2D MR images and 3D phantoms. Experiments demonstrated that our method reduces interpolation artifacts and greatly improves the 3D reconstruction accuracy. The advantage of our method casts new light on MR imaging and image processing and permits us to achieve high resolution and short acquisition time simultaneously.

Improved tensor scale computation with application to medical image interpolation

Tensor scale (t-scale) is a parametric representation of local structure morphology that simultaneously describes its orientation, shape and isotropic scale. At any image location, t-scale represents the largest ellipse (an ellipsoid in three dimensions) centered at that location and contained in the same homogeneous region. Here, we present an improved algorithm for t-scale computation and study its application to image interpolation. Specifically, the t-scale computation algorithm is improved by: (1) enhancing the accuracy of identifying local structure boundary and (2) combining both algebraic and geometric approaches in ellipse fitting. In the context of interpolation, a closed form solution is presented to determine the interpolation line at each image location in a gray level image using t-scale information of adjacent slices. At each location on an image slice, the method derives normal vector from its t-scale that yields trans-orientation of the local structure and points to the closest edge point. Normal vectors at the matching two-dimensional locations on two adjacent slices are used to compute the interpolation line using a closed form equation. The method has been applied to BrainWeb data sets and to several other images from clinical applications and its accuracy and response to noise and other image-degrading factors have been examined and compared with those of current state-of-the-art interpolation methods. Experimental results have established the superiority of the new t-scale based interpolation method as compared to existing interpolation algorithms. Also, a quantitative analysis based on the paired t-test of residual errors has ascertained that the improvements observed using the t-scale based interpolation are statistically significant.

A non-local approach for image super-resolution using intermodality priors

Image enhancement is of great importance in medical imaging where image resolution remains a crucial point in many image analysis algorithms. In this paper, we investigate brain hallucination (Rousseau, 2008), or generating a high-resolution brain image from an input low-resolution image, with the help of another high-resolution brain image. We propose an approach for image super-resolution by using anatomical intermodality priors from a reference image. Contrary to interpolation techniques, in order to be able to recover fine details in images, the reconstruction process is based on a physical model of image acquisition. Another contribution to this inverse problem is a new regularization approach that uses an example-based framework integrating non-local similarity constraints to handle in a better way repetitive structures and texture. The effectiveness of our approach is demonstrated by experiments on realistic Brainweb Magnetic Resonance images and on clinical images from ADNI, generating automatically high-quality brain images from low-resolution input.

Directional view interpolation for compensation of sparse angular sampling in cone-beam CT

In flat detector cone-beam computed tomography and related applications, sparse angular sampling frequently leads to characteristic streak artifacts. To overcome this problem, it has been suggested to generate additional views by means of interpolation. The practicality of this approach is investigated in combination with a dedicated method for angular interpolation of 3-D sinogram data. For this purpose, a novel dedicated shape-driven directional interpolation algorithm based on a structure tensor approach is developed. Quantitative evaluation shows that this method clearly outperforms conventional scene-based interpolation schemes. Furthermore, the image quality trade-offs associated with the use of interpolated intermediate views are systematically evaluated for simulated and clinical cone-beam computed tomography data sets of the human head. It is found that utilization of directionally interpolated views significantly reduces streak artifacts and noise, at the expense of small introduced image blur.

Asymmetry in lung sound intensities detected by respiratory acoustic thoracic imaging (RATHI) and clinical pulmonary auscultation

RATHI was introduced as an attempt to further improve the association between anatomical zones and specific breathing activity, both spatially and temporally. This work compares RATHI with clinical pulmonary auscultation (PA) to assess the concordance between both procedures to detect asymmetries in lung sound (LS) intensities. Twelve healthy young males participated in the study and were auscultated by two experts. RATHI consisted in the acquisition of acoustical signals with an array of 5x5 sensors, while experts auscultated and described the intensity of LS heard using the same stethoscope on each sensor's position within the array. Comparisons were established looking for intensity asymmetries between apical vs. basal pulmonary regions and right vs. left hemithorax. By RATHI, most of the subjects showed asymmetries between apical and basal regions higher than 20%, whereas between left and right hemithorax asymmetries higher than 20% occurred only half of the time. RATHI and PA agreed 83 to 100% when apical to base acoustical information was compared, but when left to right asymmetries were considered these figures were about 40 to 50%. We concluded that RATHI has advantages as it gave more detailed and measurable information on LS than clinicians, who could not detect intensity asymmetries mainly below 20%.

Automatic correspondence on medical images: a comparative study of four methods for allocating corresponding points

The accurate estimation of point correspondences is often required in a wide variety of medical image-processing applications. Numerous point correspondence methods have been proposed in this field, each exhibiting its own characteristics, strengths, and weaknesses. This paper presents a comprehensive comparison of four automatic methods for allocating corresponding points, namely the template-matching technique, the iterative closest points approach, the correspondence by sensitivity to movement scheme, and the self-organizing maps algorithm. Initially, the four correspondence methods are described focusing on their distinct characteristics and their parameter selection for common comparisons. The performance of the four methods is then qualitatively and quantitatively compared over a total of 132 two-dimensional image pairs divided into eight sets. The sets comprise of pairs of images obtained using controlled geometry protocols (affine and sinusoidal transforms) and pairs of images subject to unknown transformations. The four methods are statistically evaluated pairwise on all image pairs and individually in terms of specific features of merit based on the correspondence accuracy as well as the registration accuracy. After assessing these evaluation criteria for each method, it was deduced that the self-organizing maps approach outperformed in most cases the other three methods in comparison.

Respiratory acoustic thoracic imaging (RATHI): assessing intrasubject variability

Respiratory acoustic thoracic imaging (RATHI) permits analysing lung sounds (LS) temporal and spatial distribution, however, a deep understanding of RATHI repeatability associated with the pulmonary function is necessary. As a consequence, in the current work intrasubject variability of RATHI is evaluated at different airflows. For generating RATHIs, LS were acquired at the posterior thoracic surface. The associated image was computed at the inspiratory phases by interpolation through a Hermite function. The acoustic information of eleven subjects was considered at airflows of 1.0, 1.5 and 2.0 L/s. Several RATHIs were generated for each subject according to the number of acquired inspiratory phases. Quadratic mutual information based on Cauchy-Schwartz inequality (I(CS)) was used to evaluate the degree of similitude between intrasubject RATHIs. The results indicated that, for the same subject, I(CS) averaged 0.893, 0.897, and 0.902, for airflows of 1.0, 1.5, and 2 L/s, respectively. In addition, when the airflow was increased, increments in intensity values and in the dispersion of the spatial distribution reflected in RATHI were observed. In conclusion, since the intrasubject variability of RATHI was low for airflows between 1.0 and 2.0 L/s, the pattern of sound distribution during airflow variations is repeatable but differences in sound intensity should be considered.

A morphology-based approach for interslice interpolation of anatomical slices from volumetric images

This paper proposes a new morphology-based approach for the interslice interpolation of current transformer (CT) and MRI datasets composed of parallel slices. Our approach is object based and accepts as input data binary slices belonging to the same anatomical structure. Such slices may contain one or more regions, since topological changes between two adjacent slices may occur. Our approach handles explicitly interslice topology changes by decomposing a many-to-many correspondence into three fundamental cases: one-to-one, one-to-many, and zero-to-one correspondences. The proposed interpolation process is iterative. One iteration of this process computes a transition sequence between a pair of corresponding input slices, and selects the element located at equal distance from the input slices. This algorithmic design yields a gradual, smooth change of shape between the input slices. Therefore, the main contribution of our approach is its ability to interpolate between two anatomic shapes by creating a smooth, gradual change of shape, and without generating over-smoothed interpolated shapes.

A new method for registration-based medical image interpolation

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.

Optical flow based volumetric spatio-temporal interpolation

Acquisition of three-dimensional medical images with excellent spatial resolution is achieved almost always at the cost-dictating expense of increased acquisition time. This can be mitigated using shape-respecting inter-slice interpolation of sparsely acquired datasets. Existing shape and registration based interpolation techniques are suboptimal. This paper proposes a new optical flow based approach to interpolate between sparse data. The Horn and Schunck technique is used to estimate the pixel-wise flow vectors across the slices; intermediate data planes can be reconstructed from these flow vectors. The proposed technique is accurate and computationally fast. Coupled with anatomy aware sparse acquisitions, the proposed technique shows promise of judiciously managing the patient-specific acquisition.

Volume reconstruction based on non-rigid registration

Volume reconstruction is one of the key problems in 3D image rendering and analysis. Inter slice interpolation methods have been widely discussed in the literature and object-based algorithms have been shown to well behave. In this paper, we present a non-rigid registration based strategy to improve the volume reconstruction. A level set evolution technique is proposed to yield the deformation between adjacent slices. A modified bilinear interpolation method is then designed to generate propagating image. A multi-resolution scheme is applied to decrease the computation time and support large deformation. The resulting images show good results on regions enclosing different anatomic structures.

Polygon interpolation for serial cross sections

In this paper, a new technique for contour interpolation between slices is presented. We assumed that contour interpolation is equivalent to the interpolation of a polygon that approximates the object shape. The location of each polygon vertex is characterized by a set of parameters. Polygon interpolation can be performed on these parameters. These interpolated parameters are then used to reconstruct the vertices of the new polygon. Finally, the contour is approximated from this polygon using a cubic spline interpolation. This new technique takes into account the shape, the translation, the size, and the orientation of the object's contours. A comparison with regular shape-based interpolation is made on several object contours. The preliminary results show that this new method yields a better contour and is computationally more efficient than shape-based interpolation. This technique can be applied to gray-level images too. The interpolation result of an MR image does not show artifact of intermediate substance commonly seen in a typical linear gray-level interpolation.

High-quality two-stage resampling for 3-D volumes in medical imaging

This paper introduces a simple method of two-stage resampling where Fourier-domain up-sampling is followed by traditional resampling. Practical aspects as well as efficient implementation techniques are considered. A new version of pruned FFT algorithms to calculate the up-sampling stage is also introduced. The suggested two-stage resampling method provides very high-quality results exceeding those of the previous algorithms. It excels with higher dimensional datasets due to its ability to employ small-support kernels. The applied FFT algorithms make the method most efficient with dataset sizes of powers of two. These reasons and the importance of minimal resampling artifacts make the suggested method especially suitable for 3-D volumes in medical imaging. Furthermore, for repeated uses, only the second stage is recalculated allowing an increase in performance for motion correction applications in functional magnetic resonance imaging (fMRI), for example.

Comparison of scene-based interpolation methods applied to CT abdominal images

Three-dimensional (3-D) interpolation from 2-D image slices is widely used to aid the display, analysis and other biomedical image processing. We investigate the performance of 5 scene-based interpolation methods: linear, cubic spline, modified cubic spline and sine-based functions (Dirichlet apodization and Hanning apodization). We test our methods on four sets of computed tomography (CT) abdominal images, which have more organs in them compared to other biomedical images. Results show that, contrary to the 1-D or 2-D cases, linear interpolation acts as well as, even slightly better than all the other methods in the sense of signal to noise ratio in most cases, while the computational load of linear interpolation is only about half of the other methods. The reason for the relative high performance of linear interpolation is probably the large distance between consecutive images, which indicates low inter-slice correlation.

An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

Respiratory motion degrades anatomic position reproducibility and leads to issues affecting image acquisition, treatment planning, and radiation delivery. Four-dimensional (4D) computer tomography (CT) image acquisition can be used to measure the impact of organ motion and to explicitly account for respiratory motion during treatment planning and radiation delivery. Modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. A respiratory signal (spirometer signal or surface tracking) is used to reconstruct a 4D data set by sorting the CT scans according to the couch position and signal coherence with predefined respiratory phases. But artifacts can occur if there are no acquired data segments for exactly the same respiratory state for all couch positions. These artifacts are caused by device-dependent limitations of gantry rotation, image reconstruction times and by the variability of the patient's respiratory pattern. In this paper an optical flow based method for improved reconstruction of 4D CT data sets from multislice CT scans is presented. The optical flow between scans at neighboring respiratory states is estimated by a non-linear registration method. The calculated velocity field is then used to reconstruct a 4D CT data set by interpolating data at exactly the predefined respiratory phase. Our reconstruction method is compared with the usually used reconstruction based on amplitude sorting. The procedures described were applied to reconstruct 4D CT data sets for four cancer patients and a qualitative and quantitative evaluation of the optical flow based reconstruction method was performed. Evaluation results show a relevant reduction of reconstruction artifacts by our technique. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.

Shape-based averaging

A new method for averaging multidimensional images is presented, which is based on signed Euclidean distance maps computed for each of the pixel values. We refer to the algorithm as "shape-based averaging" (SBA) because of its similarity to Raya and Udupa's shape-based interpolation method. The new method does not introduce pixel intensities that were not present in the input data, which makes it suitable for averaging nonnumerical data such as label maps (segmentations). Using segmented human brain magnetic resonance images, SBA is compared to label voting for the purpose of averaging image segmentations in a multiclassifier fashion. SBA, on average, performed as well as label voting in terms of recognition rates of the averaged segmentations. SBA produced more regular and contiguous structures with less fragmentation than did label voting. SBA also was more robust for small numbers of atlases and for low atlas resolutions, in particular, when combined with shape-based interpolation. We conclude that SBA improves the contiguity and accuracy of averaged image segmentations.

Efficient 3-D medical image registration using a distributed blackboard architecture

A major drawback of 3-D medical image registration techniques is the performance bottleneck associated with re-sampling and similarity computation. Such bottlenecks limit registration applications in clinical situations where fast execution times are required and become particularly apparent in the case of registering 3-D data sets. In this paper a novel framework for high performance intensity-based volume registration is presented. Geometric alignment of both reference and sensed volume sets is achieved through a combination of scaling, translation, and rotation. Crucially, resampling and similarity computation is performed intelligently by a set of knowledge sources. The knowledge sources work in parallel and communicate with each other by means of a distributed blackboard architecture. Partitioning of the blackboard is used to balance communication and processing workloads. Large-scale registrations with substantial speedups, when compared with a conventional implementation, have been demonstrated.

Three-dimensional velocity field reconstruction

The problem of inter-slice magnetic resonance (MR) image reconstruction is encountered often in medical imaging applications, in such scenarios, there is a need to approximate information not captured in contiguously acquired MR images due to hardware sampling limitations. In the context of velocity field reconstruction, these data are required for visualization and computational analyses of flow fields to be effective. To provide more complete velocity information, a method has been developed for the reconstruction of flow fields based on adaptive control grid interpolation (ACGI). In this study, data for reconstruction were acquired via MRJ from in vitro models of surgically corrected pediatric cardiac vasculatures. Reconstructed velocity fields showed strong qualitative agreement with those obtained via other acquisition techniques. Quantitatively reconstruction was shown to produce data of comparable quality to accepted velocity data acquisition methods. Results indicate that ACGI-based velocity field reconstruction is capable of producing information suitable for a variety of applications demanding three-dimensional in vivo velocity data.

3D long bone reconstruction based on level sets

In medical imaging a three-dimensional (3D) object must often be reconstructed from serial cross-sections to aid in the comprehension of the object's structure as well as to facilitate its automatic manipulation and analysis. The most popular interpolation scheme for a sequence of image slices is the shape-based method, where object information extracted from a given 3D volume image is used in guiding the interpolation process. The paper presents a level set reformulation of the well-known shape-based method as well as a new automatic level set method, which offers better performance. In particular, we focus on X-ray examinations of long bones, which also requires us to deal with the problem of an optimal slice positioning. To this aim, a 2D version of the proposed algorithm will be used to localize a subset of slices from the entire volume image. A number of experiments were performed on computed tomographic real images to evaluate the proposed approach. The experimental results show a substantial improvement of visual effects (qualitative evaluation) using the proposed method in comparison to both the conventional gray-level interpolation scheme and the shape-based method. Compared with the shape-based interpolation scheme the proposed method has much lower computational cost.

Respiratory acoustic thoracic imaging (RATHI): Assessing deterministic interpolation techniques

As respiratory sounds contain mechanical and clinical pulmonary information, technical efforts have been devoted during the past decades to analysing, processing and visualising them. The aim of this work was to evaluate deterministic interpolating functions to generate surface respiratory acoustic thoracic images (RATHIs), based on multiple acoustic sensors. Lung sounds were acquired from healthy subjects through a 5 x 5 microphone array on the anterior and posterior thoracic surfaces. The performance of five interpolating functions, including the linear, cubic spline, Hermite, Lagrange and nearest neighbour method, were evaluated to produce images of lung sound intensity during both breathing phases, at low (approximately 0.5ls(-1)) and high (approximately 1.0ls(-1)) airflows. Performance indexes included the normalised residual variance nrv (i.e. inaccuracy), the prediction covariance cv (i.e. precision), the residual covariance rcv (i.e. bias) and the maximum squared residual error semax (i.e. tolerance). Among the tested interpolating functions and in all experimental conditions, the Hermite function (nrv=0.146 +/- 0.059, cv= 0.925 +/- 0.030, rcv = -0.073 +/- 0.068, semax = 0.005 +/- 0.004) globally provided the indexes closest to the optimum, whereas the nearest neighbour (nrv=0.339 +/- 0.023, cv = 0.870 +/- 0.033, rcv= 0.298 +/- 0.032, semax = 0.007 +/- 0.005) and the Lagrange methods (nrv = 0.287 +/- 0.148, cv = 0.880 +/- 0.039, rcv = -0.524 +/- 0.135, semax = 0.007 +/- 0.0001) presented the poorest statistical measurements. It is concluded that, although deterministic interpolation functions indicate different performances among tested techniques, the Hermite interpolation function presents a more confident deterministic interpolation for depicting surface-type RATHI.

Registration-based interpolation

A method is presented to interpolate between neighboring slices in a grey-scale tomographic data set. Spatial correspondence between adjacent slices is established using a nonrigid registration algorithm based on B-splines which optimizes the normalized mutual information similarity measure. Linear interpolation of the image intensities is then carried out along the directions calculated by the registration algorithm. The registration-based method is compared to both standard linear interpolation and shape-based interpolation in 20 tomographic data sets. Results show that the proposed method statistically significantly outperforms both linear and shape-based interpolation.