A comparative study of surface- and volume-based techniques for the automatic registration between CT and SPECT brain images

Three-Dimensional Registration of Freehand-Tracked Ultrasound to CT Images of the Talocrural Joint

A rigid surface⁻volume registration scheme is presented in this study to register computed tomography (CT) and free-hand tracked ultrasound (US) images of the talocrural joint. Prior to registration, bone surfaces expected to be visible in US are extracted from the CT volume and bone contours in 2D US data are enhanced based on monogenic signal representation of 2D US images. A 3D monogenic signal data is reconstructed from the 2D data using the position of the US probe recorded with an optical tracking system. When registering the surface extracted from the CT scan to the monogenic signal feature volume, six transformation parameters are estimated so as to optimize the sum of monogenic signal features over the transformed surface. The robustness of the registration algorithm was tested on a dataset collected from 12 cadaveric ankles. The proposed method was used in a clinical case study to investigate the potential of US imaging for pre-operative planning of arthroscopic access to talar (osteo)chondral defects (OCDs). The results suggest that registrations with a registration error of 2 mm and less is achievable, and US has the potential to be used in assessment of an OCD' arthroscopic accessibility, given the fact that 51% of the talar surface could be visualized.

Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b -values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b -values (3000 s ·mm^-2 and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.

Emerging technologies for image guidance and device navigation in interventional radiology

Recent developments in image-guidance and device navigation, along with emerging robotic technologies, are rapidly transforming the landscape of interventional radiology (IR). Future state-of-the-art IR procedures may include real-time three-dimensional imaging that is capable of visualizing the target organ, interventional tools, and surrounding anatomy with high spatial and temporal resolution. Remote device actuation is becoming a reality with the introduction of novel magnetic-field enabled instruments and remote robotic steering systems. Robots offer several degrees of freedom and unprecedented accuracy, stability, and dexterity during device navigation, propulsion, and actuation. Optimization of tracking and navigation of interventional tools inside the human body will be critical in converting IR suites into the minimally invasive operating theaters of the future with increased safety and unsurpassed therapeutic efficacy. In the not too distant future, individual image guidance modalities and device tracking methods could merge into autonomous, multimodality, multiparametric platforms that offer real-time data of anatomy, morphology, function, and metabolism along with on-the-fly computational modeling and remote robotic actuation. The authors provide a concise overview of the latest developments in image guidance and device navigation, while critically envisioning what the future might hold for 2020 IR procedures.

Deep brain stimulation for secondary dystonia: results in 8 patients

BACKGROUND

Dystonia is a medically intractable condition characterized by involuntary twisting movements and/or abnormal postures. Deep Brain Stimulation (DBS) has been used successfully in various forms of dystonia. In the present study, we report on eight patients with secondary dystonia, treated with DBS in our clinic.

METHOD

Eight patients (five males, three females) underwent DBS for secondary dystonia. The etiology of dystonia was cerebral palsy (n = 2), drug-induced (n = 1), post encephalitis (n = 2) and postanoxic dystonia (n = 3). The functional capacity was evaluated before and after surgery with the use of Burke-Fahn-Mardsen Dystonia Rating Scale (BFM scale), both movement and disability scale (MS and DS, respectively). The target for DBS was the globus pallidus internus (GPi) in 7 patients and in one patient, with postanoxic damaged pallidum, the ventralis oralis anterior (Voa) nucleus. Brain perfusion scintigraphy using Single Photon Emission Computed Tomography (SPECT) was performed in two separate studies for each patient, one in the "off-DBS" and the other in the "on-DBS" state.

FINDINGS

Postoperative both MS and DS scores were found to be significantly lower compared to preoperative scores (p = 0.018 and p = 0.039, respectively). Mean improvement rate after DBS was 41.4% (0-94.3) and 29.5% (0-84.2) in MS and DS scores, respectively. The SPECT Scan, during the "on-DBS" state, showed a decrease in regional cerebral blood flow (rCBF), compared to the "off-DBS" state.

CONCLUSIONS

Our results seem promising in the field of secondary dystonia treatment. More studies with greater number of patients and longer follow-up periods are necessary in order to establish the role of DBS in the management of secondary dystonia. Finally, the significance of brain SPECT imaging in the investigation of dystonia and functional effects of DBS should be further evaluated.

Evaluation of four volume-based image registration algorithms

We evaluated 4 volume-based automatic image registration algorithms from 2 commercially available treatment planning systems (Philips Syntegra and BrainScan). The algorithms based on cross correlation (CC), local correlation (LC), normalized mutual information (NMI), and BrainScan mutual information (BSMI) were evaluated with: (1) the synthetic computed tomography (CT) images, (2) the CT and magnetic resonance (MR) phantom images, and (3) the CT and MR head image pairs from 12 patients with brain tumors. For the synthetic images, the registration results were compared with known transformation parameters, and all algorithms achieved accuracy of submillimeter in translation and subdegree in rotation. For the phantom images, the registration results were compared with those provided by frame and marker-based manual registration. For the patient images, the results were compared with anatomical landmark-based manual registration to qualitatively determine how the results were close to a clinically acceptable registration. NMI and LC outperformed CC and BSMI, with the sense of being closer to a clinically acceptable result. As for the robustness, NMI and BSMI outperformed CC and LC. A guideline of image registration in our institution was given, and final visual assessment is necessary to guarantee reasonable results.

Anatomical evaluation of CT-MRI combined femoral model

Background

Both CT and MRI are complementary to each other in that CT can produce a distinct contour of bones, and MRI can show the shape of both ligaments and bones. It will be ideal to build a CT-MRI combined model to take advantage of complementary information of each modality. This study evaluated the accuracy of the combined femoral model in terms of anatomical inspection.

Methods

Six normal porcine femora (180 ± 10 days, 3 lefts and 3 rights) with ball markers were scanned by CT and MRI. The 3D/3D registration was performed by two methods, i.e. the landmark-based 3 points-to-3 points and the surface matching using the iterative closest point (ICP) algorithm. The matching accuracy of the combined model was evaluated with statistical global deviation and locally measure anatomical contour-based deviation. Statistical analysis to assess any significant difference between accuracies of those two methods was performed using univariate repeated measures ANOVA with the Turkey post hoc test.

Results

This study revealed that the local 2D contour-based measurement of matching deviation was 0.5 ± 0.3 mm in the femoral condyle, and in the middle femoral shaft. The global 3D contour matching deviation of the landmark-based matching was 1.1 ± 0.3 mm, but local 2D contour deviation through anatomical inspection was much larger as much as 3.0 ± 1.8 mm.

Conclusion

Even with human-factor derived errors accumulated from segmentation of MRI images, and limited image quality, the matching accuracy of CT-&-MRI combined 3D models was 0.5 ± 0.3 mm in terms of local anatomical inspection.

Multimodal image re-registration via mutual information to account for initial tissue motion during image-guided neurosurgery

Multimodal imaging modalities, such as high-resolution pre-operative MR (pMR) and intra-operative US (iUS), have been used widely to provide complementary information for guiding neurosurgery. Proper registration between the images is usually established fiducials placed on the patient skull, which can be identified both in MR and a tracking system that also records location and orientation of each iUS scan. Although such systems can achieve high registration accuracy, we have observed brain tissue motion with respect to the skull after the craniotomy and before opening of the dura. We developed a rigid registration strategy to re-align the brain in iUS and pMR to account for such motion. The mutual information between the two modalities is maximized through a genetic algorithm. Two patient cases are presented to show the improved registration accuracy of soft tissues.

Real-time SPECT and 2D ultrasound image registration

In this paper we present a technique for fully automatic, real-time 3D SPECT (Single Photon Emitting Computed Tomography) and 2D ultrasound image registration. We use this technique in the context of kidney lesion diagnosis. Our registration algorithm allows a physician to perform an ultrasound exam after a SPECT image has been acquired and see in real time the registration of both modalities. An automatic segmentation algorithm has been implemented in order to display in 3D the positions of the acquired US images with respect to the organs.

Mutual-information-based registration of medical images: a survey

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different aspects of mutual-information-based registration. The main division is in aspects of the methodology and of the application. The part on methodology describes choices made on facets such as preprocessing of images, gray value interpolation, optimization, adaptations to the mutual information measure, and different types of geometrical transformations. The part on applications is a reference of the literature available on different modalities, on interpatient registration and on different anatomical objects. Comparison studies including mutual information are also considered. The paper starts with a description of entropy and mutual information and it closes with a discussion on past achievements and some future challenges.

Automated kinetic analysis of FDG uptake in living rat brain slices from dynamic positron autoradiography

Changes in regional cerebral glucose metabolism were investigated for varying levels of tissue oxygenation using a dynamic positron autoradiography technique. While incubating fresh rat brain slices with [18F]FDG in an oxygenated solution, serial images of the tissue slices were obtained over a time period of up to 300 min and archived onto over 20 phosphorous imaging plate exposures. In order to properly create time activity curves of the uptake levels, images of the individual tissue samples were automatically located, digitally extracted, and registered with the later images of the same tissue samples. After applying image processing techniques for aligning tissue sample images, time activity curves were extracted for individual substructures in the rat brain and quantitative results were reported using Patlak plots. Since the levels of oxygenation can be controlled for these experiments, [18F]FDG uptakes can be reported representing states of hypoxia, pseudoischemia, and reoxygenation. The image processing techniques developed for this application have enabled more experiments and tissue samples to be acquired and analyzed than would otherwise be possible using manual ROI techniques. The objective spatial registration of tissue samples and automated extraction of data has increased the analysis accuracy and decreased the operator error associated with the interactive handling of the image data. This supports improved kinetic modeling of FDG uptake in animal studies, and can be used for more accurate dosimetry calculations in humans.