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Physical and mechanical properties of aligners determine the clinical success of orthodontic treatments. A main element of the successful orthodontic tooth movements is the fitting of the aligner’s surface to the backside of the related teeth. The complex human tooth anatomy and the aligner’s production make gaps inevitable. The aim of the tomography study is the morphological assessment of the recently introduced NaturAligner (Bottmedical AG, Basel, Switzerland). Using the advanced micro-CT system (nanotom m, phoenix|xray, Waygate Technologies, Wunstorf, Germany), a series of eight different aligners, placed on the 3D-printed model of the upper jaw, were visualized. Based on these 3D datasets, the gaps between model and aligner were automatically segmented and the thickness distribution of the aligners automatically determined. This quantification, validated by manual inspection, clearly indicated that aligners fitted better the model, when higher process temperatures were applied.

Tooth cementum annulation (TCA) is used for determining age-at-death and stress periods based on yearly deposited lines in the root cementum of human teeth. Traditionally, TCA analysis employs optical microscopy, which requires cutting sections of the root and provides only sparse sampling in the third dimension. Ancient teeth are unique specimens that should not be sliced. In this imaging study, we show that extended field of view synchrotron radiation-based tomography provides true micrometer resolution and coverage for non-destructively surveying for incremental lines. To rapidly review the root cementum layer of four teeth from early 19th century cemetery with historical records of life events, we employed machine learning for semi-automatic detection and analysis of incremental lines. Surveying large regions of the root cementum enables detection of incremental lines and hence improves TCA analysis as an alternative to slicing of the unique teeth.

Inline phase tomography using synchrotron radiation with sub-micrometer voxel sizes is nowadays the gold standard for investigation of soft and hard tissues with micron resolution. Recent developments on detectors and X-ray sources allow the transfer of the technique into laboratory environment. For the comparison of three manufacturers, we performed microtomography with advanced laboratory microtomography devices with micron resolution on a porcine nerve, a zebrafish embryo and a historic human tooth. These data sets were also compared with data acquired at the ANATOMIX beamline at Synchrotron Soleil and the TOMCAT beamline at SLS. For the lab-based experiments following scanners were chosen: Skyscan 2214 (Bruker-microCT, Kontich, Belgium), Xradia 620 Versa (Zeiss, Oberkochen, Germany) and a prototype with a MetalJet X-ray source from Exillum from the company Exciscope (Stockholm, Sweden). All devices contained detectors including X-ray optics.

Formalin fixation and paraffin embedding of post mortem tissue specimens is widely used for high-resolution neuroimaging with both conventional and X-ray virtual histology. Exchange of embedding solutions generates non-uniform brain shrinkage and changes relative tissue densities. We used synchrotron radiation-based X-ray micro computed tomography to visualize the embedding process for a single mouse brain. Non-rigid registration was employed to determine the volumetric strain fields and to track the X-ray absorption changes of corresponding features. This allows for a correction of the observed microanatomy to improve the anatomical context. Through embedding, the entire brain shrinks to around 40% of its volume in formalin. Shrinkage is non-uniform and varies over anatomical regions and the distance to external surfaces.

X rays have been used for medical imaging since RONTGEN's fascinating discovery 125 years ago. The first radiographs of human hands were made public less than a month after his famous paper. The conventional X-ray sources integrated into the CT-machines of today’s hospitals still rely on the same physical principles. X-ray imaging has traditionally offered high spatial resolution and low contrast for soft tissues such as the brain. Magnetic resonance imaging is therefore the method of choice for brain imaging in a clinical setting, although for cellular resolution studies it suffers from limited spatial resolution. The gold standard in post mortem brain imaging is histology, which involves fixation, embedding, physical sectioning, staining, and optical microscopy. Currently, section thickness limits isotropic voxel sizes to 20 μm. Advanced X-ray sources including synchrotron radiation facilities offer complementary modalities such as phase-contrast imaging and spatially resolved small-angle X-ray scattering. We showed that X-ray phase contrast of the human cerebellum with micrometer resolution yields complementary three-dimensional images to magnetic resonance microscopy with even better contrast and spatial resolution. Grating interferometry enabled us to visualize individual Purkinje cells in the nonstained cerebellum. Taking advantage of well-established paraffin embedding, Purkinje cells were visualized within the human cerebellum even with conventional instrumentation. Hard X-ray nano-holotomography allowed for label-free, three-dimensional neuroimaging beyond the optical limit with a spatial resolution below 100 nm. Spatially resolved smallangle X-ray scattering permitted the localization of periodic nanostructures such as myelin sheaths on square-inch brain slices and included the orientational information on the axons. These developments have contributed to the establishment of virtual histology and extended the conventional histology to the third dimension. Further advances are required to image the entire human brain with an isotropic micrometer resolution and to suitably handle the petabyte datasets.

Hard X-ray micro computed tomography can be used for three-dimensional histological phenotyping of zebrafish embryos down to 1 µm or below without the need for staining or physical slicing. Current advances in ze- brafish embryo imaging, however, mostly rely on synchrotron radiation sources or highly advanced laboratory sources, which despite their evident strengths with regard to their beam properties and the implementation of phase contrast imaging techniques, lack accessibility. Therefore, we evaluated the performance of a conventional SkyScan 1275 laboratory µCT scanner in absorption contrast mode for the visualization of anatomical features in ethanol- and paraffin-embedded zebrafish embryos. We compare our results to readily available synchrotron data where 35 anatomical structures were identified. Despite having a more than ten times larger voxel length, approximately two thirds of the features could also be determined with laboratory microtomography. This could allow to monitor morphological changes during development or disease progression on large sample numbers, enabling the performance of preclinical studies in a local laboratory.

Biological matter may change shape via water absorption or loss. For example, brain tissue shows non-uniform shrinkage during formalin fixation and paraffin embedding, which is the most common tissue preparation for conventional histological analysis. Local deformations can be analyzed with non-rigid registration of non-destructive three-dimensional imaging datasets. We utilized synchrotron radiation microtomography at the ANATOMIX beamline of Synchrotron SOLEIL to image a mouse brain with 3 micron voxel length after formalin fixation, immersion in ascending alcohol series and xylene, and after paraffin embedding. We created a pipeline for non- rigid registration to align the volumes and extract volumetric strain fields. In this way, we could visualize the swelling/shrinkage of anatomical features. This method avoids time-consuming segmentation of brain regions, however it is sensitive to the registration parameters. In this proceedings paper, we discuss the selection of registration parameters in order to generate plausible volumetric strain fields. This protocol can be deployed to any type of shape change of biological matter and allows for the quantification of the related processes.

Successful tomographic imaging of soft tissues with micrometer resolution includes preparation, acquisition, re- construction, and data evaluation. Tissue preparation is essential for hard X-ray microtomography, because staining- and embedding materials can substantially alter the biological tissue post mortem. We performed to- mographic imaging of zebrafish embryos in alcohol and after paraffin embedding with a conventional X-ray source and at a synchrotron radiation facility. The resulting multi-modal, three-dimensional data were registered for direct comparison. Single-cell precision was reached for the synchrotron radiation-based approach, which allows for segmentation of full organs such as the embryonic kidneys. While this approach offers an order of magnitude higher spatial resolution, many of the anatomical features can be readily recognized with the more accessible laboratory system. Propagation-based data acquisition enabled us to demonstrate the complementary nature of the edge-enhanced absorption contrast- and the phase contrast-based modality for visualizing multiple microanatomical features. While ethanol and paraffin embeddings allowed for identification of the same anatomical structures, paraffin-embedding, however, led to more artefacts and shrinkage. The presented multi-modal imaging approaches can be further extended to visualize three to four orders of magnitude larger volumes such as adult zebrafish or complete organs of larger animals such as mouse brains. Going towards even larger volumes, such as the human brain, presents further challenges related to paraffin embedding, data acquisition and handling of the peta-byte scale data volumes. This study provided a multi-modal imaging strategy by the combination of X-ray sources and sample embeddings which can play a role in addressing these challenges.

Treatment of tumor sites affected by respiratory motion requires knowledge of the position and the shape of the tumor and the surrounding organs during breathing. As not all structures of interest can be observed in real-time, their position needs to be predicted from partial information (so-called surrogates) like motion of diaphragm, internal markers or patients surface. Here, we present an approach to model respiratory lung motion and predict the position and shape of the lungs from surrogates. 4D-MRI lung data of 10 healthy subjects was acquired and used to create a model based on Principal Component Analysis (PCA). The mean RMS motion ranged from 1.88 mm to 9.66 mm. Prediction was done using a Bayesian approach and an average RMSE of 1.44 mm was achieved.

This work applies fractional differentiation (differentiation to non-integer order) to the gradients determined from image intensities for enhanced image registration. The technique is used to correct known simulated deformations of volumetric breast MR data using two algorithms: direct registration of gradient magnitude images and an extension of a previously published method that incorporates both image intensity and image gradient information to enhance registration performance. Better recovery of known deformations are seen when using non-integer order derivatives: half-derivative breast images are better registered when these methods are incorporated into a standard diffusion-based registration algorithm.

To improve cancer detection in mammography, breast exams usually consist of two views per breast. To combine information from both views, radiologists and multiview computer-aided detection (CAD) systems need to match corresponding regions in the two views. In digital breast tomosynthesis (DBT), finding corresponding regions in ipsilateral volumes may be a difficult and time-consuming task for radiologists, because many slices have to be inspected individually. In this study we developed a method to quickly estimate corresponding locations in ipsilateral tomosynthesis views by applying a mathematical transformation. First a compressed breast model is matched to the tomosynthesis view containing a point of interest. Then we decompress, rotate and compress again to estimate the location of the corresponding point in the ipsilateral view. In this study we use a simple elastically deformable sphere model to obtain an analytical solution for the transformation in a given DBT case. The model is matched to the volume by using automatic segmentation of the pectoral muscle, breast tissue and nipple. For validation we annotated 181 landmarks in both views and applied our method to each location. Results show a median 3D distance between the actual location and estimated location of 1.5 cm; a good starting point for a feature based local search method to link lesions for a multiview CAD system. Half of the estimated locations were at most 1 slice away from the actual location, making our method useful as a tool in mammographic workstations to interactively find corresponding locations in ipsilateral tomosynthesis views.

Stable features under simulated mammographic compressions, which will become candidate landmarks for a temporal mammographic feature-based registration algorithm, are discussed in this paper. Using these simulated mammograms, we explore the extraction of features based on standard intensity projection images and local phase projection images. One approach to establishing corresponding features is by template matching using a similarity measure. Simulated mammographic projections from deformed MR volumes are employed, as the mean projected 3D displacements are computed and therefore validation of the technique is possible. Tracking is done by template matching using normalized cross correlation as the similarity measure. The performance of standard projection images and local phase projection images is compared. The preliminary results reveal that although the majority of the points within the breast are difficult to track, a small number may be successfully tracked, which is indicative of their stability and thus their suitability as candidate landmarks. Whilst matching using the standard projection images achieves an overall error of 14.46mm, this error increases to 22.7mm when computing local phase of the projection images. These results suggest that using local phase alone does not improve template matching. For the identification of stable landmarks for feature-based mammogram registration, we conclude that intensity based template matching using normalized correlation is a feasible approach for identifying stable features.

This paper describes the development of a cylindrical affine transformation model for image registration. The usefulness of the model for initial alignment was demonstrated for the application of registering prone and supine 3D MR images of the breast. Final registration results visually improved when using the cylindrical affine transformation model instead of none or a Cartesian affine transformation model before non-rigid registration.

This paper presents a novel approach to X-ray mammography - MRI registration. The proposed method uses an intensity-based technique and an affine transformation matrix to approximate the 3D deformation of the breast resulting from the compression applied during mammogram acquisition. The registration is driven by a similarity measure that is calculated at each iteration of the algorithm between the target X-ray mammogram and a simulated X-ray image, created from the MR volume. Although the similarity measure is calculated in 2D, we compute a 3D transformation that is updated at each iteration. We have performed two types of experiments. In the first set, we used simulated X-ray target data, for which the ground truth deformation of the volume was known and thus the results could be validated. For this case, we examined the performance of 4 different similarity measures and we show that Normalized Cross Correlation and Gradient Difference perform best. The calculated mean reprojection error was for both similarity measures 4mm, for an initial misregistration of 14mm. In the second set of experiments, we present the initial results of registering real X-ray mammograms with MR volumes. The results indicate that the breast boundaries were registered well and the volume was deformed in 3D in a similar way to the deformation of the breast during X-ray mammogram acquisition. The experiments were carried out on five patients.

We have previously proposed a system for image-guided breast surgery that compensates for the deformation of the breast during patient set-up. Since breast surgery is performed with the patient positioned supine, but MR imaging is performed with the patient positioned prone, a large soft tissue deformation must be accounted for. A biomechanical model can help to constrain the associated registrations. However the necessary material properties for breast tissue under such strains are not available in the literature. This paper describes a method to determine these properties. We first show that the stress-free or 'reference' state of an object can be approximated by submerging it in liquid of a similar density. MR images of the breast submerged in water and in a pendulous prone position are acquired. An intensity-based non-rigid image registration algorithm is used to establish point-by-point correspondence between these images. A finite element model of the breast is then constructed from the submerged images and the deformation to free-pendulous is simulated. The material properties for which the model deformation best fits the observed deformation are determined. Assuming neo-Hookean material properties, the initial shear moduli of fibroglandular and adipose tissue are found to be 0.4 kPa and 0.3 kPa respectively.

This paper describes a novel method for registering multimodal breast images. The method is based on guiding initial alignment by a 3D statistical deformation model (SDM) followed by a standard non-rigid registration method for fine alignment. The method was applied to the problem of compensating for large breast compressions, namely registering magnetic resonance (MR) images to tomosynthesis images and X-ray mammograms. The SDM was based on simulating plausible breast compressions for a population of 20 subjects via finite element models created from segmented 3D MR breast images. Leave-one-out tests on simulated data showed that using SDM guided registration rather than affine registration for the initial alignment led on average to lower mean registration errors, namely 3.2 mm versus 4.2 mm for MR to tomosynthesis images (17.1 mm initially) and 5.0 mm versus 6.2 mm for MR to X-ray mammograms (15.0 mm initially).

Image registration is a very important procedure in medical imaging analysis. However, the intensive computations involved in image registration have to some extent made it impractical for interactive use as well as limiting its general availability. This paper presents our current Grid project to facilitate image registration tasks. We have set up an image registration Grid by combining the attractive features of both Globus and Condor distributed computing environments. In order to make it more convenient to use, we have also developed a web interface for potential clients to specify and submit their image registration jobs to the Grid. The initial experiments in 3D breast MR images have shown encouraging results and demonstrated the suitability of Grid technology to this type of computationally intensive applications. The image registration Grid makes it much more straightforward for different institutes to use the identical registration program and protocols to register images consistently, quickly and efficiently. This can greatly improve data sharing and comparative studies in multi-centre trials. The Grid presented here could be an important step for clinical applications of image registration. Future work will focus on refining the Grid with the aim of upgrading it to a Grid Service and testing the system more extensively with medical imaging dataset.

We present initial results from evaluating the accuracy with which biomechanical breast models based on finite element methods can predict the displacements of tissue within the breast. We investigate the influence of different tissue elasticity values, Poisson's ratios, boundary conditions, finite element solvers and mesh resolutions on one data set. MR images were acquired before and after compressing a volunteer's breast gently. These images were aligned using a 3D non-rigid registration algorithm. The boundary conditions were derived from the result of the non-rigid registration or by assuming no patient motion at the deep or medial side. Three linear and two non-linear elastic material models were tested. The accuracy of the BBMs was assessed by the Euclidean distance of twelve corresponding anatomical landmarks. Overall, none of the tested material models was obviously superior to another regarding the set of investigated values. A major average error increase was noted for partially inaccurate boundary conditions at high Poisson's ratios due to introduced volume change. Maximal errors remained, however, high for low Poisson's ratio due to the landmarks closeness to the inaccurate boundary conditions. The choice of finite element solver or mesh resolution had almost no effect on the performance outcome.

This work presents a validation study for non-rigid registration of 3D contrast enhanced magnetic resonance mammography images. We are using our previously developed methodology for simulating physically plausible, biomechanical tissue deformations using finite element methods to compare two non-rigid registration algorithms based on single-level and multi-level free-form deformations using B-splines and normalized mutual information. We have constructed four patient-specific finite element models and applied the solutions to the original post-contrast scans of the patients, simulating tissue deformation between image acquisitions. The original image pairs were registered to the FEM-deformed post-contrast images using different free-form deformation mesh resolutions. The target registration error was computed for each experiment with respect to the simulated gold standard on a voxel basis. Registration error and single-level free-form deformation resolution were found to be intrinsically related: the smaller the spacing, the higher localized errors, indicating local registration failure. For multi-level free-form deformations, the registration errors improved for increasing mesh resolution. This study forms an important milestone in making our non-rigid registration framework applicable for clinical routine use.