An open environment CT-US fusion for tissue segmentation during interventional guidance

Feasibility of Cochlea High-frequency Ultrasound and Microcomputed Tomography Registration for Cochlear Computer-assisted Surgery: A Testbed

INTRODUCTION

There remains no standard imaging method that allows computer-assisted surgery of the cochlea in real time. However, recent evidence suggests that high-frequency ultrasound (HFUS) could permit real-time visualization of cochlear architecture. Registration with an imaging modality that suffers neither attenuation nor conical deformation could reveal useful anatomical landmarks to surgeons. Our study aimed to address the feasibility of an automated three-dimensional (3D) HFUS/microCT registration, and to evaluate the identification of cochlear structures using 2D/3D HFUS and microCT.

METHODS

MicroCT, and 2D/3D 40 MHz US in B-mode were performed on ex vivo guinea pig cochlea. An automatic rigid registration algorithm was applied to segmented 3D images. This automatic registration was then compared to a reference method using manual annotated landmarks placed by two senior otologists. Inter- and intrarater reliabilities were evaluated using intraclass correlation coefficient (ICC) and the mean registration error was calculated.

RESULTS

3D HFUS/microCT automatic registration was successful. Excellent levels of concordance were achieved with regards intra-rater reliability for both raters with micro-CT and US images (ICC ranging from 0.98 to 1, p < 0.001) and with regards inter-rater reliability (ICC ranging from 0.99 to 1, p < 0.001). The mean HFUS/microCT automated RE for both observers was 0.17 ± 0.03 mm [0.10-0.25]. Identification of the basilar membrane, modiolus, scala tympani, and scala vestibuli was possible with 2D/3D HFUS and micro-CT.

CONCLUSIONS

HFUS/microCT image registration is feasible. 2D/3D HFUS and microCT allow the visualization of cochlear structures. Many potential clinical applications are conceivable.

Neuromodulation of sensory networks in monkey brain by focused ultrasound with MRI guidance and detection

Focused ultrasound (FUS) has gained recognition as a technique for non-invasive neuromodulation with high spatial precision and the ability to both excite and inhibit neural activity. Here we demonstrate that MRI-guided FUS is capable of exciting precise targets within areas 3a/3b in the monkey brain, causing downstream activations in off-target somatosensory and associated brain regions which are simultaneously detected by functional MRI. The similarity between natural tactile stimulation-and FUS- evoked fMRI activation patterns suggests that FUS likely can excite populations of neurons and produce associated spiking activities that may be subsequently transmitted to other functionally related touch regions. The across-region differences in fMRI signal changes relative to area 3a/3b between tactile and FUS conditions also indicate that FUS modulated the tactile network differently. The significantly faster rising (>1 sec) fMRI signals elicited by direct FUS stimulation at the targeted cortical region suggest that a different neural hemodynamic coupling mechanism may be involved in generating fMRI signals. This is the first demonstration of imaging neural excitation effects of FUS with BOLD fMRI on a specific functional circuit in non-human primates.

Segmentation of neuronal structures using SARSA (λ)-based boundary amendment with reinforced gradient-descent curve shape fitting

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.

Creation and characterization of an ultrasound and CT phantom for noninvasive ultrasound thermometry calibration

Ultrasound thermometry provides noninvasive 2-D temperature monitoring, and in this paper, we have investigated the use of computed tomography (CT) radiodensity to characterize tissues to improve the accuracy of ultrasound thermometry. Agarose-based tissue-mimicking phantoms were created with glyceryl trioleate (a fat-mimicking material) concentration of 0%, 10%, 20%, 30%, 40%, and 50%. The speed of sound (SOS) of the phantoms was measured over a temperature range of 22.1-41.1 °C. CT images of the phantoms were acquired by a clinical dedicated breast CT scanner, followed by calculation of the Hounsfield units (HU). The phantom was heated with a therapeutic acoustic pulse (1.54 MHz), while RF data were acquired with a 10-MHz linear-array transducer. Two-dimensional speckle tracking was used to calculate the thermal strain offline. The tissue-dependent thermal strain parameter required for ultrasound thermometry was analyzed and correlated with CT radiodensity, followed by the validation of the temperature prediction. Results showed that the change in SOS with the temperature increase was opposite in sign between the 0%-10% and 20%-50% trioleate phantoms. The inverse of the tissue-dependent thermal strain parameter of the phantoms was correlated with the CT radiodensity (R(2) = 0.99). A blinded ultrasound thermometry study on phantoms with a trioleate range of 5%-35% demonstrated the capability to estimate the tissue-dependent thermal strain parameter and estimate temperature with error less than ~1 °C. In conclusion, CT radiodensity may provide a method for improving ultrasound thermometry in heterogeneous tissues.

In vivo validation and 3D visualization of broadband ultrasound molecular imaging

Ultrasound can selectively and specifically visualize upregulated vascular receptors through the detection of bound microbubbles. However, most current ultrasound molecular imaging methods incur delays that result in longer acquisition times and reduced frame rates. These delays occur for two main reasons: 1) multi-pulse imaging techniques are used to differentiate microbubbles from tissue and 2) acquisition occurs after free bubble clearance (>6 minutes) in order to differentiate bound from freely circulating microbubbles. In this paper, we validate tumor imaging with a broadband single pulse molecular imaging method that is faster than the multi-pulse methods typically implemented on commercial scanners. We also combine the single pulse method with interframe filtering to selectively image targeted microbubbles without waiting for unbound bubble clearance, thereby reducing acquisition time from 10 to 2 minutes. The single pulse imaging method leverages non-linear bubble behavior by transmitting at low and receiving at high frequencies (TLRH). We implemented TLRH imaging and visualized the accumulation of intravenously administrated integrin-targeted microbubbles in a phantom and a Met-1 mouse tumor model. We found that the TLRH contrast imaging has a ~2-fold resolution improvement over standard contrast pulse sequencing (CPS) imaging. By using interframe filtering, the tumor contrast was 24.8±1.6 dB higher after the injection of integrin-targeted microbubbles than non-targeted control MBs, while echoes from regions lacking the target integrin were suppressed by 26.2±2.1 dB as compared with tumor echoes. Since real-time three-dimensional (3D) molecular imaging provides a more comprehensive view of receptor distribution, we generated 3D images of tumors to estimate their volume, and these measurements correlated well with expected tumor sizes. We conclude that TLRH combined with interframe filtering is a feasible method for 3D targeted ultrasound imaging that is faster than current multi-pulse strategies.

A Practical Approach Based on Analytic Deformable Algorithm for Scenic Image Registration

Background

Image registration is to produce an entire scene by aligning all the acquired image sequences. A registration algorithm is necessary to tolerance as much as possible for intensity and geometric variation among images. However, captured image views of real scene usually produce unexpected distortions. They are generally derived from the optic characteristics of image sensors or caused by the specific scenes and objects.

Methods and Findings

An analytic registration algorithm considering the deformation is proposed for scenic image applications in this study. After extracting important features by the wavelet-based edge correlation method, an analytic registration approach is then proposed to achieve deformable and accurate matching of point sets. Finally, the registration accuracy is further refined to obtain subpixel precision by a feature-based Levenberg-Marquardt (FLM) method. It converges evidently faster than most other methods because of its feature-based characteristic.

Conclusions

We validate the performance of proposed method by testing with synthetic and real image sequences acquired by a hand-held digital still camera (DSC) and in comparison with an optical flow-based motion technique in terms of the squared sum of intensity differences (SSD) and correlation coefficient (CC). The results indicate that the proposed method is satisfactory in the registration accuracy and quality of DSC images.

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.