Multimodal target correction by local bone registration: a PET/CT evaluation

PET/CT guidance for percutaneous interventions allows biopsy of suspicious metabolically active bone lesions even when no morphological correlation is delineable in the CT images. Clinical use of PET/CT guidance with conventional step-by-step technique is time consuming and complicated especially in cases in which the target lesion is not shown in the CT image. Our recently developed multimodal instrument guidance system (IGS) for PET/CT improved this situation. Nevertheless, bone biopsies even with IGS have a trade-off between precision and intervention duration which is proportional to patient and personnel exposure to radiation. As image acquisition and reconstruction of PET may take up to 10 minutes, preferably only one time consuming combined PET/CT acquisition should be needed during an intervention. In case of required additional control images in order to check for possible patient movements/deformations, or to verify the final needle position in the target, only fast CT acquisitions should be performed. However, for precise instrument guidance accounting for patient movement and/or deformation without having a control PET image, it is essential to be able to transfer the position of the target as identified in the original PET/CT to a changed situation as shown in the control CT.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

ECM versus ICP for point registration

Iterative Closest Point (ICP) is a widely exploited method for point registration that is based on binary point-to-point assignments, whereas the Expectation Conditional Maximization (ECM) algorithm tries to solve the problem of point registration within the framework of maximum likelihood with point-to-cluster matching. In this paper, by fulfilling the implementation of both algorithms as well as conducting experiments in a scenario where dozens of model points must be registered with thousands of observation points on a pelvis model, we investigated and compared the performance (e.g. accuracy and robustness) of both ICP and ECM for point registration in cases without noise and with Gaussian white noise. The experiment results reveal that the ECM method is much less sensitive to initialization and is able to achieve more consistent estimations of the transformation parameters than the ICP algorithm, since the latter easily sinks into local minima and leads to quite different registration results with respect to different initializations. Both algorithms can reach the high registration accuracy at the same level, however, the ICP method usually requires an appropriate initialization to converge globally. In the presence of Gaussian white noise, it is observed in experiments that ECM is less efficient but more robust than ICP.

Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics

Locally affine (polyaffine) image registration methods capture intersubject non-linear deformations with a low number of parameters, while providing an intuitive interpretation for clinicians. Considering the mandible bone, anatomical shape differences can be found at different scales, e.g. left or right side, teeth, etc. Classically, sequential coarse to fine registration are used to handle multiscale deformations, instead we propose a simultaneous optimization of all scales. To avoid local minima we incorporate a prior on the polyaffine transformations. This kind of groupwise registration approach is natural in a polyaffine context, if we assume one configuration of regions that describes an entire group of images, with varying transformations for each region. In this paper, we reformulate polyaffine deformations in a generative statistical model, which enables us to incorporate deformation statistics as a prior in a Bayesian setting. We find optimal transformations by optimizing the maximum a posteriori probability. We assume that the polyaffine transformations follow a normal distribution with mean and concentration matrix. Parameters of the prior are estimated from an initial coarse to fine registration. Knowing the region structure, we develop a blockwise pseudoinverse to obtain the concentration matrix. To our knowledge, we are the first to introduce simultaneous multiscale optimization through groupwise polyaffine registration. We show results on 42 mandible CT images.

Expression of antagonists of WNT and BMP signaling after non-rigid fixation of osteotomies

Delayed fracture healing and non-unions represent rare but severe complications in orthopedic surgery. Further knowledge on the mechanisms of the bone repair process and of the development of a pseudoarthrosis is essential to predict and prevent impaired healing of fractures. The present study aimed at elucidating differences in gene expression during the repair of rigidly and non-rigidly fixed osteotomies. For this purpose, the MouseFix™ and the FlexiPlate™ systems (AO Development Institute, Davos, CH), allowing the creation of well defined osteotomies in mouse femora, were employed. A time course following the healing process of the osteotomy was performed and bones and periimplant tissues were analyzed by high-resolution X-ray, MicroCT and by histology. For the assessment of gene expression, Low Density Arrays (LDA) were done. In animals with rigid fixation, X-ray and MicroCT revealed healing of the osteotomy within 3 weeks. Using the FlexiPlate™ system, the osteotomy was still visible by X-ray after 3 weeks and a stabilizing cartilaginous callus was formed. After 4.5 weeks, the callus was remodeled and the osteotomy was, on a histological level, healed. Gene expression studies revealed levels of transcripts encoding proteins associated with inflammatory processes not to be altered in tissues from bones with rigid and non-rigid fixation, respectively. Levels of transcripts encoding proteins of the extracellular matrix and essential for bone cell functions were not increased in the rigidly fixed group when compared to controls without osteotomy. In the FlexiPlate™ group, levels of transcripts encoding the same set of genes were significantly increased 3 weeks after surgery. Expression of transcripts encoding BMPs and BMP antagonists was increased after 3 weeks in repair tissues from bones fixed with FlexiPlate™, as were inhibitors of the WNT signaling pathways. Little changes only were detected in transcript levels of tissues from rigidly fixed bones. The data of the present study suggest that rigid fixation enables accelerated healing of an experimental osteotomy as compared to non-rigid fixation. The changes in the healing process after non-rigid fixation are accompanied by an increase in the levels of transcripts encoding inhibitors of osteogenic pathways and, probably as a consequence, by temporal changes in bone matrix synthesis.