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.
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