A methodology to accurately quantify patellofemoral cartilage contact kinematics by combining 3D image shape registration and cine-PC MRI velocity data

Patellofemoral osteoarthritis and its potential precursor patellofemoral pain syndrome (PFPS) are common, costly, and debilitating diseases. PFPS has been shown to be associated with altered patellofemoral joint mechanics; however, an actual variation in joint contact stresses has not been established due to challenges in accurately quantifying in vivo contact kinematics (area and location). This study developed and validated a method for tracking dynamic, in vivo cartilage contact kinematics by combining three magnetic resonance imaging (MRI) techniques, cine-phase contrast (CPC), multi-plane cine (MPC), and 3D high-resolution static imaging. CPC and MPC data were acquired from 12 healthy volunteers while they actively extended/flexed their knee within the MRI scanner. Since no gold standard exists for the quantification of in vivo dynamic cartilage contact kinematics, the accuracy of tracking a single point (patellar origin relative to the femur) represented the accuracy of tracking the kinematics of an entire surface. The accuracy was determined by the average absolute error between the PF kinematics derived through registration of MPC images to a static model and those derived through integration of the CPC velocity data. The accuracy ranged from 0.47 mm to 0.77 mm for the patella and femur and from 0.68 mm to 0.86 mm for the patellofemoral joint. For purely quantifying joint kinematics, CPC remains an analytically simpler and more accurate (accuracy <0.33 mm) technique. However, for application requiring the tracking of an entire surface, such as quantifying cartilage contact kinematics, this combined imaging approach produces accurate results with minimal operator intervention.     

MRI vs CT-based 2D-3D auto-registration accuracy for quantifying shoulder motion using biplane video-radiography

Biplane 2D-3D registration approaches have been used for measuring 3D, in vivo glenohumeral (GH) joint kinematics. Computed tomography (CT) has become the gold standard for reconstructing 3D bone models, as it provides high geometric accuracy and similar tissue contrast to video-radiography. Alternatively, magnetic resonance imaging (MRI) would not expose subjects to radiation and provides the ability to add cartilage and other soft tissues to the models. However, the accuracy of MRI-based 2D-3D registration for quantifying glenohumeral kinematics is unknown. We developed an automatic 2D-3D registration program that works with both CT- and MRI-based image volumes for quantifying joint motions. The purpose of this study was to use the proposed 2D-3D auto-registration algorithm to describe the humerus and scapula tracking accuracy of CT- and MRI-based registration relative to radiostereometric analysis (RSA) during dynamic biplanar video-radiography. The GH kinematic accuracy (RMS error) was 0.6–1.0 mm and 0.6–2.2° for the CT-based registration and 1.4–2.2 mm and 1.2–2.6° for MRI-based registration. Higher kinematic accuracy of CT-based registration was expected as MRI provides lower spatial resolution and bone contrast as compared to CT and suffers from spatial distortions. However, the MRI-based registration is within an acceptable accuracy for many clinical research questions.     

Medical image registration using fuzzy theory

Mutual information (MI)-based registration, which uses MI as the similarity measure, is a representative method in medical image registration. It has an excellent robustness and accuracy, but with the disadvantages of a large amount of calculation and a long processing time. In this paper, by computing the medical image moments, the centroid is acquired. By applying fuzzy c-means clustering, the coordinates of the medical image are divided into two clusters to fit a straight line, and the rotation angles of the reference and floating images are computed, respectively. Thereby, the initial values for registering the images are determined. When searching the optimal geometric transformation parameters, we put forward the two new concepts of fuzzy distance and fuzzy signal-to-noise ratio (FSNR), and we select FSNR as the similarity measure between the reference and floating images. In the experiments, the Simplex method is chosen as multi-parameter optimisation. The experimental results show that this proposed method has a simple implementation, a low computational cost, a fast registration and good registration accuracy. Moreover, it can effectively avoid trapping into the local optima. It is adapted to both mono-modality and multi-modality image registrations.     

Phase correlation applied to the 3D registration of CT and CBCT image volumes

PurposeIn this study, a 3D phase correlation algorithm was investigated to test feasibility for use in determining the anatomical changes that occur throughout a patient's radiotherapy treatment. The algorithm determines the transformations between two image volumes through analysis in the Fourier domain and has not previously been used in radiotherapy for 3D registration of CT and CBCT volumes.MethodsVarious known transformations were applied to a patient's prostate CT image volume to create 12 different test cases. The mean absolute error and standard deviation were determined by evaluating the difference between the known contours and those calculated from the registration process on a point-by-point basis. Similar evaluations were performed on images with increasing levels of noise added. The improvement in structure overlap offered by the algorithm in registering clinical CBCT to CT images was evaluated using the Dice Similarity Coefficient (DSC).ResultsA mean error of 2.35 (σ = 1.54) mm was calculated for the 12 deformations applied. When increasing levels of noise were introduced to the images, the mean errors were observed to rise up to a maximum increase of 1.77 mm. For CBCT to CT registration, maximum improvements in the DSC of 0.09 and 0.46 were observed for the bladder and rectum, respectively.ConclusionsThe Fourier-based 3D phase correlation registration algorithm investigated displayed promising results in CT to CT and CT to CBCT registration, offers potential in terms of efficiency and robustness to noise, and is suitable for use in radiotherapy for monitoring patient anatomy throughout treatment.     

An ImageJ-based tool for three-dimensional registration between different types of microscopic images

Three-dimensional (3D) registration (i.e., alignment) between two microscopic images is very helpful to study tissues that do not adhere to substrates, such as mouse embryos and organoids, which are often 3D rotated during imaging. However, there is no 3D registration tool easily accessible for experimental biologists. Here we developed an ImageJ-based tool which allows for 3D registration accompanied with both quantitative evaluation of the accuracy and reconstruction of 3D rotated images. In this tool, several landmarks are manually provided in two images to be aligned, and 3D rotation is computed so that the distances between the paired landmarks from the two images are minimized. By simultaneously providing multiple points (e.g., all nuclei in the regions of interest) other than the landmarks in the two images, the correspondence of each point between the two images, i.e., to which nucleus in one image a certain nucleus in another image corresponds, is quantitatively explored. Furthermore, 3D rotation is applied to one of the two images, resulting in reconstruction of 3D rotated images. We demonstrated that this tool successfully achieved 3D registration and reconstruction of images in mouse pre- and post-implantation embryos, where one image was obtained during live imaging and another image was obtained from fixed embryos after live imaging. This approach provides a versatile tool applicable for various tissues and species.     

Evaluation of the Block Matching deformable registration algorithm in the field of head-and-neck adaptive radiotherapy

Background and purposeTo compare the accuracy of the Block Matching deformable registration (DIR) against rigid image registration (RIR) for head-and-neck multi-modal images CT to cone-beam CT (CBCT) registration.Material and methodsPlanning-CT and weekly CBCT of 10 patients were used for this study. Several volumes, including medullary canal (MC), thyroid cartilage (TC), hyoid bone (HB) and submandibular gland (SMG) were transposed from CT to CBCT images using either DIR or RIR. Transposed volumes were compared with the manual delineation of these volumes on every CBCT. The parameters of similarity used for analysis were: Dice Similarity Index (DSI), 95%-Hausdorff Distance (95%-HD) and difference of volumes (cc).ResultsWith DIR, the major mean difference of volumes was −1.4 cc for MC, revealing limited under-segmentation. DIR limited variability of DSI and 95%-HD. It significantly improved DSI for TC and HB and 95%-HD for all structures but SMG. With DIR, mean 95%-HD (mm) was 3.01 ± 0.80, 5.33 ± 2.51, 4.99 ± 1.69, 3.07 ± 1.31 for MC, TC, HB and SMG, respectively. With RIR, it was 3.92 ± 1.86, 6.94 ± 3.98, 6.44 ± 3.37 and 3.41 ± 2.25, respectively.ConclusionBlock Matching is a valid algorithm for deformable multi-modal CT to CBCT registration. Values of 95%-HD are useful for ongoing development of its application to the cumulative dose calculation.     

Accuracy of target delineation by positron emission tomography-based auto-segmentation methods after deformable image registration: A phantom study

PurposeDiagnostic positron emission tomography and computed tomography (PET/CT) images can be fused to the planning CT images by a deformable image registration (DIR). The aim of this study was to evaluate the standardized uptake value (SUV) and target delineation on deformed PET images.MethodsWe used a cylindrical phantom and removable inserts of four spheres (16–38 mm in diameter) and three ellipsoids with a volume equal to the 38-mm-diameter sphere (S38) in each. S38 was filled with 18F-fluorodeoxyglucose activity, and then PET/CT images were acquired. The contours of S38 were generated using original PET images by PET auto-segmentation (PET-AS) methods of (1) SUV2.5, (2) 40% of maximum SUV (SUV40%max), and (3) gradient-based (GB), and were deformed to the other inserts by DIR. We compared the volumes and the SUV_max with the generated contours using the deformed PET images.ResultsThe SUV_max was slightly decreased by DIR; the mean absolute difference was −0.10 ± 0.04. For SUV2.5 and SUV40%max, the differences in S38 volumes between the original and deformed PET images were less than 5%, regardless of deformation type. For the GB, the contoured volumes obtained from deformed PET images were larger than those of the original PET images for the deformation type of ellipsoids. When the S38 was deformed to the 16-mm-diameter sphere, the maximum volume difference was −22.8%.ConclusionsAlthough SUV fluctuations by DIR were negligible, the target delineation on deformed PET images by the GB should be carefully considered owing to the distortion of intensity profiles.     

A comparison of seven methods of within-subjects rigid-body pedobarographic image registration

Image registration, the process of transforming images such that homologous structures optimally overlap, provides the pre-processing foundation for pixel-level functional image analysis. The purpose of this study was to compare the performances of seven methods of within-subjects pedobarographic image registration: (1) manual, (2) principal axes, (3) centre of pressure trajectory, (4) mean squared error, (5) probability-weighted variance, (6) mutual information, and (7) exclusive OR. We assumed that foot-contact geometry changes were negligibly small trial-to-trial and thus that a rigid-body transformation could yield optimum registration performance. Thirty image pairs were randomly selected from our laboratory database and were registered using each method. To compensate for inter-rater variability, the mean registration parameters across 10 raters were taken as representative of manual registration. Registration performance was assessed using four dissimilarity metrics (#4-7 above). One-way MANOVA found significant differences between the methods (p<0.001). Bonferroni post-hoc tests revealed that the centre of pressure method performed the poorest (p<0.001) and that the principal axes method tended to perform more poorly than remaining methods (p<0.070). Average manual registration was not different from the remaining methods (p=1.000). The results suggest that a variety of linear registration methods are appropriate for within-subjects pedobarographic images, and that manual image registration is a viable alternative to algorithmic registration when parameters are averaged across raters. The latter finding, in particular, may be useful for cases of image peculiarities resulting from outlier trials or from experimental manipulations that induce substantial changes in contact area or pressure profile geometry.     

Evaluation of a 3D object registration method for analysis of humeral kinematics

In 3D image-based studies of joint kinematics, 3D registration methods should be automatic, insensitive to segmentation inconsistencies and use coordinate systems that have clinically relevant orientations and locations because this is important for analyzing rotation angles and translation directions. We developed and evaluated a registration method, which is based on the cylindrical geometry of the humerus shaft and an analysis of the inertia moments of the humerus head, in order to consistently and automatically orient the humerus coordinate system according to its anatomy. Registration techniques must be thoroughly evaluated. In this study we used a well-detectable marker as reference, from which coordinate system determination errors of a 3D object could be measured. This allowed us to quantify by means of unique error analysis the translational and rotational errors in terms of how much and about/along which humeral axis errors occurred. The evaluation experiments were performed using virtual rotations of 3D humeral binary image, a humerus model and a 3D image of a volunteer's shoulder. They indicated that the humeral coordinate system determination errors primarily originated from segmentation inconsistencies, which influenced mostly the humeral transverse axes orientation. The error analysis revealed that the developed registration method reduced the effect of manual segmentation inconsistencies on the orientation of the humeral transverse axes up to 37%, in comparison to the commonly used inertia registration.     

The utility of deformable image registration for small artery visualisation in contrast-enhanced whole body MR angiography

PurposeAn investigation was carried out into the effect of three image registration techniques on the diagnostic image quality of contrast-enhanced magnetic resonance angiography (CE-MRA) images.MethodsWhole-body CE-MRA data from the lower legs of 27 patients recruited onto a study of asymptomatic atherosclerosis were processed using three deformable image registration algorithms. The resultant diagnostic image quality was evaluated qualitatively in a clinical evaluation by four expert observers, and quantitatively by measuring contrast-to-noise ratios and volumes of blood vessels, and assessing the techniques' ability to correct for varying degrees of motion.ResultsThe first registration algorithm (‘AIR’) introduced significant stenosis-mimicking artefacts into the blood vessels' appearance, observed both qualitatively (clinical evaluation) and quantitatively (vessel volume measurements). The two other algorithms (‘Slicer’ and ‘SEMI’), based on the normalised mutual information (NMI) concept and designed specifically to deal with variations in signal intensity as found in contrast-enhanced image data, did not suffer from this serious issue but were rather found to significantly improve the diagnostic image quality both qualitatively and quantitatively, and demonstrated a significantly improved ability to deal with the common problem of patient motion.ConclusionsThis work highlights both the significant benefits to be gained through the use of suitable registration algorithms and the deleterious effects of an inappropriate choice of algorithm for contrast-enhanced MRI data. The maximum benefit was found in the lower legs, where the small arterial vessel diameters and propensity for leg movement during image acquisitions posed considerable problems in making accurate diagnoses from the un-registered images.     

Auto-align - multi-modality fluorescence microscopy image co-registration

Multi-modality microscopes incorporate multiple microscopy techniques into one module, imaging through a common objective lens. Simultaneous or consecutive image acquisition of a single specimen, using multiple techniques, increases the amount of measurable information available. In order to benefit from each modality, it is necessary to accurately co-register data sets. Intrinsic differences in the image formation process employed by each modality result in images which possess different characteristics. In addition, as a result of using different measurement devices, images often differ in size and can suffer relative geometrical deformations including rotation, scale and translation, making registration a complex problem. Current methods generally rely on manual input and are therefore subject to human error. Here, we present an automated image registration tool for fluorescence microscopy. We show that it successfully registers images obtained via total internal reflection fluorescence (TIRF), or epi-fluorescence, and confocal microscopy. Furthermore, we provide several other applications including channel merging following image acquisition through an emission beam splitter, and lateral stage drift correction. We also discuss areas of membrane trafficking which could benefit from application of Auto-Align. Auto-Align is an essential item in the advanced microscopist's toolbox which can create a synergy of single or multi-modality image data.     

Assessment of regional non-linear tissue deformation and air volume change of human lungs via image registration

We evaluate the non-linear characteristics of the human lung via image registration-derived local variables based on volumetric multi-detector-row computed tomographic (MDCT) lung image data of six normal human subjects acquired at three inflation levels: 20% of vital capacity (VC), 60% VC and 80% VC. Local variables include Jacobian (ratio of volume change) and maximum shear strain for assessment of lung deformation, and air volume change for assessment of air distribution. First, the variables linearly interpolated between 20% and 80% VC images to reflect deformation from 20% to 60% VC are compared with those of direct registration of 20% and 60% VC images. The result shows that the linearly-interpolated variables agree only qualitatively with those of registration (P<0.05). Then, a quadratic (or linear) interpolation is introduced to link local variables to global air volumes of three images (or 20% and 80% VC images). A sinusoidal breathing waveform is assumed for assessing the time rate of change of these variables. The results show significant differences between two-image and three-image results (P<0.05). The three-image results for the whole lung indicate that the peak of the maximum shear rate occurs at about 37% of the maximum volume difference between 20% and 80% VC, while the peaks for the Jacobian and flow rate occur at 50%. This is in agreement with accepted physiology whereby lung tissues deform more at lower lung volumes due to lower elasticity and greater compliance. Furthermore, the three-image results show that the upper and middle lobes, even in the recumbent, supine posture, reach full expansion earlier than the lower lobes.     

Fast and Accurate Registration of the Proximal Femurs in Bilateral Hip Joint Images by Using the Random Sub-Sample Points

Background and ObjectiveAs is known, point clouds representing the objects are frequently used in object registration. Although the objects can be registered by using all the points in the corresponding point clouds of the objects, the registration process can also be achieved with a smaller number of the landmark points selected from the entire point clouds of the objects. This paper introduces a research study focusing on the fast and accurate rigid registration of the bilateral proximal femurs in bilateral hip joint images by using the random sub-sample points. For this purpose, Random Point Sub-sampling (RPS) was analyzed and the reduced point sets were used for an accurate registration of the bilateral proximal femurs in coronal hip joint magnetic resonance imaging (MRI) slices.MethodsIn registration, bilateral proximal femurs in MRI slices were registered rigidly by performing a process consisting of three main phases named as MR image preprocessing, proximal femur registration over the random sub-sample points and MR image postprocessing. In the stage of the MR image preprocessing, segmentation maps of the bilateral proximal femurs are obtained as region of interest (RoI) images from the entire MRI slices and then, the edge maps of the segmented proximal femurs are extracted. In the registration phase, the edge maps describing the proximal femur surfaces are represented as point clouds initially. Thereafter, the RPS is performed on the proximal femur point clouds and the number of points representing the proximal femurs is reduced at different ratios. For the registration of the point clouds, the Iterative Closest Point (ICP) algorithm is performed on the reduced sets of points. Finally, the registration procedures are completed by performing MR image postprocessing on the registered proximal femur images.ResultsIn performance evaluation tests performed on healthy and pathological proximal femurs in 13 bilateral coronal hip joint MRI slices of 13 Legg-Calve-Perthes disease (LCPD) patients, bilateral proximal femurs were successfully registered with very small error rates by using the reduced set of points obtained via the RPS and promising results were achieved. The minimum error rate was observed at RPS rate of 30% as the value of 0.41 (±0.31)% on all over the bilateral proximal femurs evaluated. When the range of RPS rate of 20-30% is considered as the reference, the elapsed time in registration can be reduced by almost 30-40% compared to the case where all the proximal femur points were included in registration. Additionally, it was observed that the RPS rate should be selected as at least 25% to achieve a successful registration with an error rate below 1%.ConclusionIt was concluded from the observed results that a more successful and faster registration can be accomplished by selecting fewer points randomly from the point sets of proximal femurs instead of using all the points describing the proximal femurs. Not only an accurate registration with low error rates was performed, but also a faster registration process was performed by means of the limited number of points that are sub-sampled randomly from the whole point sets.     

Effects of radiograph projection parameter uncertainty on TKA kinematics from model-image registration

Model-image registration techniques have been used extensively for the measurement of joint kinematics in vivo. These techniques typically utilize an explicit measurement of X-ray projection parameters (principal distance, principal point), which is easily done for prospective studies. However, there is vast opportunity to derive useful information from previously collected clinical radiographic films where the projection parameters are unknown. The purpose of this study was to determine variation in measured knee arthroplasty kinematics when the X-ray projection parameters were unknown, but bounded. Based on the clinical radiographic protocol, a nominal principal point was chosen and eight additional points ±2 and ±5 cm in the horizontal and vertical directions were defined. Tibiofemoral kinematics were determined for all nine projection parameter sets for a series of 10 lateral radiographs. In addition, the principal distance was varied ±15 cm and tibiofemoral kinematics were determined for these two projection sets. Measured joint kinematics varied less than 0.6° and 0.4 mm for ±2 cm variations in principal point location, and 0.7° and 0.6 mm for ±5 cm variations in principal point location. Measured joint kinematics varied less than 0.6° and 0.7 mm for ±15 cm variations in principal distance. Variation in X-ray principal point and principal distance over clinically bounded ranges has a small effect on knee arthroplasty kinematics computed from model-image registration with high-quality clinical radiographs.     

Effect of image registration on 3D absorbed dose calculations in 177Lu-DOTATOC peptide receptor radionuclide therapy

Peptide receptor radionuclide therapy (PRRT) is an effective MRT (molecular radiotherapy) treatment, which consists of multiple administrations of a radiopharmaceutical labelled with ¹⁷⁷Lu or ⁹⁰Y. Through sequential functional imaging a patient specific 3D dosimetry can be derived. Multiple scans should be previously co-registered to allow accurate absorbed dose calculations. The purpose of this study is to evaluate the impact of image registration algorithms on 3D absorbed dose calculation.A cohort of patients was extracted from the database of a clinical trial in PRRT. They were administered with a single administration of ¹⁷⁷Lu-DOTATOC. All patients underwent 5 SPECT/CT sequential scans at 1 h, 4 h, 24 h, 40 h, 70 h post-injection that were subsequently registered using rigid and deformable algorithms. A similarity index was calculated to compare rigid and deformable registration algorithms. 3D absorbed dose calculation was carried out with the Raydose Monte Carlo code.The similarity analysis demonstrated the superiority of the deformable registrations (p < .001).Average absorbed dose to the kidneys calculated using rigid image registration was consistently lower than the average absorbed dose calculated using the deformable algorithm (90% of cases), with percentage differences in the range [−19; +4]%. Absorbed dose to lesions were also consistently lower (90% of cases) when calculated with rigid image registration with absorbed dose differences in the range [−67.2; 100.7]%. Deformable image registration had a significant role in calculating 3D absorbed dose to organs or lesions with volumes smaller than 100 mL.Image based 3D dosimetry for ¹⁷⁷Lu-DOTATOC PRRT is significantly affected by the type of algorithm used to register sequential SPECT/CT scans.     

Evaluation of a Learning-based Deformable Registration Method on Abdominal CT Images

BackgroundReliable image comparisons, based on fast and accurate deformable registration methods, are recognized as key steps in the diagnosis and follow-up of cancer as well as for radiation therapy planning or surgery. In the particular case of abdominal images, the images to compare often differ widely from each other due to organ deformation, patient motion, movements of gastrointestinal tract or breathing. As a consequence, there is a need for registration methods that can cope with both local and global large and highly non-linear deformations.MethodDeformable registration of medical images traditionally relies on the iterative minimization of a cost function involving a large number of parameters. For complex deformations and large datasets, this process is computationally very demanding, leading to processing times that are incompatible with the clinical routine workflow. Moreover, the highly non-convex nature of these optimization problems leads to a high risk of convergence toward local minima. Recently, deep learning approaches using Convolutional Neural Networks (CNN) have led to major breakthroughs by providing computationally fast unsupervised methods for the registration of 2D and 3D images within seconds. Among all the proposed approaches, the VoxelMorph learning-based framework pioneered to learn in an unsupervised way the complex mapping, parameterized using a CNN, between every couple of 2D or 3D pairs of images and the corresponding deformation field by minimizing a standard intensity-based similarity metrics over the whole learning database. Voxelmorph has so far only been evaluated on brain images. The present study proposes to evaluate this method in the context of inter-subject registration of abdominal CT images, which present a greater challenge in terms of registration than brain images, due to greater anatomical variability and significant organ deformations.ResultsThe performances of VoxelMorph were compared with the current top-performing non-learning-based deformable registration method “Symmetric Normalization” (SyN), implemented in ANTs, on two representative databases: LiTS and 3D-IRCADb-01. Three different experiments were carried out on 2D or 3D data, the atlas-based or pairwise registration, using two different similarity metrics, namely (MSE and CC). Accuracy of the registration was measured by the Dice score, which quantifies the volume overlap for the selected anatomical region.All the three experiments exhibit that the two deformable registration methods significantly outperform the affine registration and that VoxelMorph accuracy is comparable or even better than the reference non-learning based registration method ANTs (SyN), with a drastically reduced computation time.ConclusionBy substituting a time consuming optimization problem, VoxelMorph has made an outstanding achievement in learning-based registration algorithm, where a registration function is trained and thus, able to perform deformable registration almost accurately on abdominal images, while reducing the computation time from minutes to seconds and from seconds to milliseconds in comparison to ANTs (SyN) on a CPU.     

Noninvasive high resolution mechanical strain maps of the spine intervertebral disc using nonrigid registration of magnetic resonance images

High resolution strain measurements are of particular interest in load bearing tissues such as the intervertebral disc (IVD), permitting characterization of biomechanical conditions which could lead to injury and degenerative outcomes. Magnetic resonance (MR) imaging produces excellent image contrast in cartilaginous tissues, allowing for image-based strain determination. Nonrigid registration (NRR) of MR images has previously demonstrated sub-voxel registration accuracy although its accuracy and precision in determining strain has not been evaluated. Accuracy and precision of NRR-derived strain measurements were evaluated using computer generated deformations applied to both computer generated images and MR images. Two different measures of registration similarity--the cost function which drives the registration algorithm--were compared: Mutual Information (MI) and Least Squares (LS). Strain error was evaluated with respect to signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and strain heterogeneity. Additionally, the creep strain response from an in vitro loaded porcine IVD is shown and comparisons between similarity measures are presented. MI showed a decrease in strain precision with increasing CNR and decreasing SNR while LS was insensitive to both. Both similarity measures showed a decrease in strain precision with increasing strain heterogeneity. When computer generated heterogeneous strains were applied to MR images of the IVD, LS showed substantially lower strain error in comparison to MI. Results suggest that LS-driven NRR provides a more accurate image-based method for mapping large and heterogeneous strain fields and this method can be applied to studies of the IVD and, potentially, other soft tissues which present sufficient image texture.     

Analysis of surface-to-surface distance mapping during three-dimensional motion at the ankle and subtalar joints

Joint surface interaction and ligament constraints determine the kinematic characteristics of the ankle and subtalar joints. Joint surface interaction is characterized by joint contact mechanics and by relative joint surface position potentially characterized by distance mapping. While ankle contact mechanics was investigated, limited information is available on joint distance mapping and its changes during motion. The purpose of this study was to use image-based distance mapping to quantify this interaction at the ankle and subtalar joints during tri-planar rotations of the ankle complex. Five cadaveric legs were scanned using Computed Tomography and the images were processed to produce 3D bone models of the tibia, fibula, talus and calcaneus. Each leg was tested on a special linkage through which the ankle complex was loaded in dorsiflexion/plantarflexion, inversion/eversion, and internal/external rotation and the resulting bone movements were recorded. Fiduciary bone markers data and 3D bone models were combined to generate color-coded distance maps for the ankle and subtalar joints. The results were processed focusing on the changes in surface-to-surface distance maps between the extremes of the range of motion and neutral. The results provided detailed insight into the three-dimensional highly coupled nature of these joints showing significant and unique changes in distance mapping from neutral to extremes of the range of motion. The non-invasive nature of the image-based distance mapping technique could result, after proper modifications, in an effective diagnostic and clinical evaluation technique for application such as ligament injuries and quantifying the effect of arthrodesis or total ankle replacement surgery.     

One‐to‐one registration of en‐face optical coherence tomography attenuation coefficients with histology of a prostatectomy specimen

Optical coherence tomography (OCT), enables high‐resolution 3D imaging of the morphology of light scattering tissues. From the OCT signal, parameters can be extracted and related to tissue structures. One of the quantitative parameters is the attenuation coefficient; the rate at which the intensity of detected light decays in depth. To couple the quantitative parameters with the histology one‐to‐one registration is needed. The primary aim of this study is to validate a registration method of quantitative OCT parameters to histological tissue outcome through one‐to‐one registration of OCT with histology. We matched OCT images of unstained fixated prostate tissue slices with corresponding histology slides, wherein different histologic types were demarcated. Attenuation coefficients were determined by a supervised automated exponential fit (corrected for point spread function and sensitivity roll‐off related signal losses) over a depth of 0.32 mm starting from 0.10 mm below the automatically detected tissue edge. Finally, the attenuation coefficients corresponding to the different tissue types of the prostate were compared. From the attenuation coefficients, we produced the squared relative residue and goodness‐of‐fit metric R². This article explains the method to perform supervised automated quantitative analysis of OCT data, and the one‐to‐one registration of OCT extracted quantitative data with histopathological outcomes.      

Point-cloud registration using adaptive radial basis functions

Non-rigid registration is a common part of bioengineering model-generation workflows. Compared to common mesh-based methods, radial basis functions can provide more flexible deformation fields due to their meshless nature. We introduce an implementation of RBF non-rigid registration with iterative knot-placement to adaptively reduce registration error. The implementation is validated on surface meshes of the femur, hemi-pelvis, mandible, and lumbar spine. Mean registration surface errors ranged from 0.37 to 0.99?mm, Hausdorff distance from 1.84 to 2.47?mm, and DICE coefficients from 0.97 to 0.99. The implementation is available for use in the free and open-source GIAS2 library.