Dynamic cardiac metrics, including myocardial strains and displacements, provide a quantitative approach to evaluate cardiac function. However, in current clinical diagnosis, largely 2D strain measures are used despite that cardiac motions are complex 3D volumes over time. Recent advances in 4D ultrasound enable the capability to capture such complex motion in a single image data set. In our previous work, a 4D optical flow based motion tracking algorithm was developed to extract full 4D dynamic cardiac metrics from such 4D ultrasound data. In order to quantitatively evaluate this tracking method, in-vivo coronary artery occlusion experiments at various locations were performed on three canine hearts. Each dog was screened with 4D ultrasound and sonomicrometry data was acquired during each occlusion study. The 4D ultrasound data from these experiments was then analyzed with the tracking method and estimated principal strain measures were directly compared to those recorded by sonomicrometry. Strong agreement was observed independently for the three canine hearts. This is the first validation study of optical flow based strain estimation for 4D ultrasound with a direct comparison with sonomicrometry using in-vivo data.

Abstract. In this paper, intravascular ultrasound (IVUS) grayscale images, acquired with a single-element mechanically rotating transducer, are processed with wavelet denoising and region-based segmentation to extract various layers of lumen contours and plaques. First, IVUS volumetric data is expanded on complex exponential multi-resolution basis functions, also known as Brushlets, which are well localized in the time and frequency domains. Brushlet denoising has previously demonstrated a great aptitude for denoising ultrasound data and removal of blood speckle. A region-based segmentation framework is then applied for detection of lumen border layers, which remains a challenging problem in IVUS image analysis for images acquired with a single element, mechanically rotating 45 MHz transducer. We evaluated a hard thresholding operator for Brushlet denoising, and compared segmentation results to manually traced lumen borders. We observed good agreement and suggest that the proposed algorithm has a potential to be used as a reliable pre-processing step for accurate lumen border detection.

Purpose: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice due to issues with analytic precision and practicality. We investigated whether AREG/EREG IHC could predict benefit from the anti-EGFR agent panitumumab. Experimental Design: Artificial intelligence algorithms were developed to assess AREG/EREG IHC in 274 patients from the PICCOLO trial of irinotecan with or without panitumumab (Ir vs. IrPan) in RAS wild-type mCRC. The primary endpoint was progression-free survival (PFS). Secondary endpoints were RECIST response rate (RR) and overall survival (OS). Models were repeated adjusting separately for BRAF mutation status and primary tumor location (PTL). Results: High ligand expression was associated with significant PFS benefit from IrPan compared with Ir [8.0 vs. 3.2 months; HR, 0.54; 95% confidence interval (CI), 0.37–0.79; P = 0.001]; whereas low ligand expression was not (3.4 vs. 4.4 months; HR, 1.05; 95% CI, 0.74–1.49; P = 0.78). The ligand-treatment interaction was significant (Pinteraction = 0.02) and remained significant after adjustment for BRAF-mutation status and PTL. Likewise, RECIST RR was significantly improved in patients with high ligand expression (IrPan vs. Ir: 48% vs. 6%; P < 0.0001) but not those with low ligand expression (25% vs. 14%; P = 0.10; Pinteraction = 0.01). The effect on OS was similar but not statistically significant. Conclusions: AREG/EREG IHC identified patients who benefitted from the addition of panitumumab to irinotecan chemotherapy. IHC is a practicable assay that may be of use in routine practice.

Our goal is to validate a spectral CT system design that uses a conventional X-ray source with multiple balanced K-edge filters. By performing a simultaneously synthetic reconstruction in multiple energy bins, we obtained a good agreement between measurements and model expectations for a reasonably complex phantom. We performed simulation and data acquisition on a phantom containing multiple rods of different materials using a NeuroLogica CT scanner. Five balanced K-edge filters including Molybdenum, Cerium, Dysprosium, Erbium, and Tungsten were used separately proximal to the X-ray tube. For each sinogram bin, measured filtered vector can be defined as a product of a transmission matrix, which is determined by the filters and is independent of the imaging object, and energy-binned intensity vector. The energy-binned sinograms were then obtained by inverting the transmission matrix followed by a multiplication of the filter measurement vector. For each energy bin defined by two consecutive K-edges, a synthesized energy-binned attenuation image was obtained using filtered back-projection reconstruction. The reconstructed attenuation coefficients for each rod obtained from the experiment was in good agreement with the corresponding simulated results. Furthermore, the reconstructed attenuation coefficients for a given energy bin, agreed with National Institute of Standards and Technology reference values when beam hardening within the energy bin is small. The proposed cost-effective system design using multiple balanced K-edge filters can be used to perform spectral CT imaging at clinically relevant flux rates using conventional detectors and integrating electronics.

Real-time three-dimensional echocardiography (RT3DE) offers an efficient way to obtain complete 3D images of the heart over an entire cardiac cycle in just a few seconds. The complex 3D wall motion and temporal information contained in these 4D data sequences has the potential to enhance and supplement other imaging modalities for clinical diagnoses based on cardiac motion analysis. In our previous work, a 4D optical flow based method was developed to estimate dynamic cardiac metrics, including strains anddisplacements, from 4D ultrasound. In this study, in order to evaluate the ability of our method in detecting ischemic regions, coronary artery occlusion experiments at various locations were performed on five dogs. 4D ultrasound data acquired during these experiments were analyzed with our proposed method. Ischemia regions predicted by the outcome of strain measurements were compared to predictions from cardiac physiology with strong agreement. This is the first direct validation study of our image analysis method for clinical diagnoses and outcome.

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-The main aim of this paper is to develop dental implant surgical navigation system based on homogenous transformation algorithms. This work is a partial section of robot-assisted surgical development. The previous works are presented in numerous basic research. They are methodology design on tool tip calibration, optical marker recognition, and pose determination using neural networks. This paper concerns with tracking path generation system based on fundamental of optical tracking. The intraoperative system is the principal focus area of this study. The homogenous transformation has been calculated in term of kinematics equation among marker relationship. The stereo camera is utilized to retrieve 3D position of different pattern styles of markers. The beneath marker recognition algorithm using rotation-invariant neural network and physical method is performed to identify markers. The fundamental relationship among markers are computed to obtain the orientation and translation between the guided path and the instrument's tool tip. The experiment has been demonstrated and performed under prototype model. The method is to work on procedure step by step. They begin with patient information input and continuously perform on marker recognition, tool tip calibrations and marker digitization. The path tracking is executed to observe the accuracy of the system. The result shows that the system can be performed to track path based on beforehand planning.

Abstract. Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dualenergy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p < 0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p < 0.01) for FBP-Ramp images and 53.2% to 67.7% (all p < 0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energ...