A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

Atlas-Based Segmentation of Brain Tumor Images Using a Markov Random Field-Based Tumor Growth Model and Non-Rigid Registration

We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed with the aim of improving the registration accuracy in case of patients with space-occupying lesions. We perform tests on 2D axial slices of five different patient data sets and show that the approach gives good results for the segmentation of white matter, grey matter, cerebrospinal fluid and the tumor.

Validity of an automatic measure protocol in distal femur for allograft selection from a three-dimensional virtual bone bank system

Osteoarticular allograft is one possible treatment in wide surgical resections with large defects. Performing best osteoarticular allograft selection is of great relevance for optimal exploitation of the bone databank, good surgery outcome and patient’s recovery. Current approaches are, however, very time consuming hindering these points in practice. We present a validation study of a software able to perform automatic bone measurements used to automatically assess the distal femur sizes across a databank. 170 distal femur surfaces were reconstructed from CT data and measured manually using a size measure protocol taking into account the transepicondyler distance (A), anterior-posterior distance in medial condyle (B) and anterior-posterior distance in lateral condyle (C). Intra- and inter-observer studies were conducted and regarded as ground truth measurements. Manual and automatic measures were compared. For the automatic measurements, the correlation coefficients between observer one and automatic method, were of 0.99 for A measure and 0.96 for B and C measures. The average time needed to perform the measurements was of 16 h for both manual measurements, and of 3 min for the automatic method. Results demonstrate the high reliability and, most importantly, high repeatability of the proposed approach, and considerable speed-up on the planning.

Anatomically-Driven Soft-Tissue Simulation Strategy for Cranio-Maxillofacial Surgery Using Facial Muscle Template Model

We propose a computationally efficient and biomechanically relevant soft-tissue simulation method for cranio-maxillofacial (CMF) surgery. A template-based facial muscle reconstruction was introduced to minimize the efforts on preparing a patient-specific model. A transversely isotropic mass-tensor model (MTM) was adopted to realize the effect of directional property of facial muscles in reasonable computation time. Additionally, sliding contact around teeth and mucosa was considered for more realistic simulation. Retrospective validation study with postoperative scan of a real patient showed that there were considerable improvements in simulation accuracy by incorporating template-based facial muscle anatomy and sliding contact.

Multi-modal diffeomorphic demons registration based on point-wise mutual information

In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.

Computer Aided Surgery for Percutaneous Nephrolithotomy: Clinical Requirement Analysis and System Design

Percutaneous nephrolithotomy (PCNL) for the treatment of renal stones and other related renal diseases has proved its efficacy and has stood the test of time compared with open surgical methods and extracorporal shock wave lithotripsy. However, access to the collecting system of the kidney is not easy because the available intra-operative image modalities only provide a two dimensional view of the surgical scenario. With this lack of visual information, several punctures are often necessary which, increases the risk of renal bleeding, splanchnic, vascular or pulmonary injury, or damage to the collecting system which sometimes makes the continuation of the procedure impossible. In order to address this problem, this paper proposes a workflow for introduction of a stereotactic needle guidance system for PCNL procedures. An analysis of the imposed clinical requirements, and a instrument guidance approach to provide the physician with a more intuitive planning and visual guidance to access the collecting system of the kidney are presented.

Determination of Pelvic Orientation from Ultrasound Images Using Patch-SSMs and a Hierarchical Speed of Sound Compensation Strategy

In the field of computer assisted orthopedic surgery (CAOS) the anterior pelvic plane (APP) is a common concept to determine the pelvic orientation by digitizing distinct pelvic landmarks. As percutaneous palpation is - especially for obese patients - known to be error-prone, B-mode ultrasound (US) imaging could provide an alternative means. Several concepts of using ultrasound imaging to determine the APP landmarks have been introduced. In this paper we present a novel technique, which uses local patch statistical shape models (SSMs) and a hierarchical speed of sound compensation strategy for an accurate determination of the APP. These patches are independently matched and instantiated with respect to associated point clouds derived from the acquired ultrasound images. Potential inaccuracies due to the assumption of a constant speed of sound are compensated by an extended reconstruction scheme. We validated our method with in-vitro studies using a plastic bone covered with a soft-tissue simulation phantom and with a preliminary cadaver trial.

Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries

Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.

Single fluoroscopic image based 2D/3D reconstruction of a scaled, patient-specific vertebral model

Purpose Accurate three-dimensional (3D) models of lumbar vertebrae can enable image-based 3D kinematic analysis. The common approach to derive 3D models is by direct segmentation of CT or MRI datasets. However, these have the disadvantages that they are expensive, timeconsuming and/or induce high-radiation doses to the patient. In this study, we present a technique to automatically reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image. Methods Our technique is based on a hybrid 2D/3D deformable registration strategy combining a landmark-to-ray registration with a statistical shape model-based 2D/3D reconstruction scheme. Fig. 1 shows different stages of the reconstruction process. Four cadaveric lumbar spine segments (total twelve lumbar vertebrae) were used to validate the technique. To evaluate the reconstruction accuracy, the surface models reconstructed from the lateral fluoroscopic images were compared to the associated ground truth data derived from a 3D CT-scan reconstruction technique. For each case, a surface-based matching was first used to recover the scale and the rigid transformation between the reconstructed surface model Results Our technique could successfully reconstruct 3D surface models of all twelve vertebrae. After recovering the scale and the rigid transformation between the reconstructed surface models and the ground truth models, the average error of the 2D/3D surface model reconstruction over the twelve lumbar vertebrae was found to be 1.0 mm. The errors of reconstructing surface models of all twelve vertebrae are shown in Fig. 2. It was found that the mean errors of the reconstructed surface models in comparison to their associated ground truths after iterative scaled rigid registrations ranged from 0.7 mm to 1.3 mm and the rootmean squared (RMS) errors ranged from 1.0 mm to 1.7 mm. The average mean reconstruction error was found to be 1.0 mm. Conclusion An accurate, scaled 3D reconstruction of the lumbar vertebra can be obtained from a single lateral fluoroscopic image using a statistical shape model based 2D/3D reconstruction technique. Future work will focus on applying the reconstructed model for 3D kinematic analysis of lumbar vertebrae, an extension of our previously-reported imagebased kinematic analysis. The developed method also has potential applications in surgical planning and navigation.

Inhibition of the kynurenine-NAD+ pathway leads to energy failure and exacerbates apoptosis in pneumococcal meningitis

Pneumococcal meningitis causes neurological sequelae, including learning and memory deficits in up to half of the survivors. In both humans and in animal models of the disease, there is apoptotic cell death in the hippocampus, a brain region involved in learning and memory function. We previously demonstrated that in an infant rat model of pneumococcal meningitis, there is activation of the kynurenine (KYN) pathway in the hippocampus, and that there was a positive correlation between the concentration of 3-hydroxykynurenine and the extent of hippocampal apoptosis. To clarify the role of the KYN pathway in the pathogenesis of hippocampal apoptosis in pneumococcal meningitis, we specifically inhibited 2 key enzymes of the KYN pathway and assessed hippocampal apoptosis, KYN pathway metabolites, and nicotinamide adenine dinucleotide (NAD) concentrations by high-performance liquid chromatography. Pharmacological inhibition of kynurenine 3-hydroxylase and kynureninase led to decreased cellular NAD levels and increased apoptosis in the hippocampus. The cerebrospinal fluid levels of tumor necrosis factor and interleukin-1? and -? were not affected. Our data suggest that activation of the KYN pathway in pneumococcal meningitis is neuroprotective by compensating for an increased NAD demand caused by infection and inflammation;this mechanism may prevent energy failure and apoptosis in the hippocampus.

Restoration of Akt activity by the bisperoxovanadium compound bpV(pic) attenuates hippocampal apoptosis in experimental neonatal pneumococcal meningitis

Pneumococcal meningitis causes apoptosis of developing neurons in the dentate gyrus of the hippocampus. The death of these cells is accompanied with long-term learning and memory deficits in meningitis survivors. Here, we studied the role of the PI3K/Akt (protein kinase B) survival pathway in hippocampal apoptosis in a well-characterized infant rat model of pneumococcal meningitis. Meningitis was accompanied by a significant decrease of the PI3K product phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) and of phosphorylated (i.e., activated) Akt in the hippocampus. At the cellular level, phosphorylated Akt was decreased in both the granular layer and the subgranular zone of the dentate gyrus, the region where the developing neurons undergo apoptosis. Protein levels and activity of PTEN, the major antagonist of PI3K, were unaltered by infection, suggesting that the observed decrease in PIP(3) and Akt phosphorylation is a result of decreased PI3K signaling. Treatment with the PTEN inhibitor bpV(pic) restored Akt activity and significantly attenuated hippocampal apoptosis. Co-treatment with the specific PI3K inhibitor LY294002 reversed the restoration of Akt activity and attenuation of hippocampal apoptosis, while it had no significant effect on these parameters on its own. These results indicate that the inhibitory effect of bpV(pic) on apoptosis was mediated by PI3K-dependent activation of Akt, strongly suggesting that bpV(pic) acted on PTEN. Treatment with bpV(pic) also partially inhibited the concentration of bacteria and cytokines in the CSF, but this effect was not reversed by LY294002, indicating that the effect of bpV(pic) on apoptosis was independent of its effect on CSF bacterial burden and cytokine levels. These results indicate that the PI3K/Akt pathway plays an important role in the death and survival of developing hippocampal neurons during the acute phase of pneumococcal meningitis.