Drug-induced anti-Ro positive subacute cutaneous lupus in a man treated with olmesartan

A 66-year-old man presented to the outpatient dermatology clinic with a chief complaint of a pruritic rash on his upper trunk and proximal upper extremities, which had been present for three weeks. Upon examination, he was found to have an erythematous, annular, and polycyclic eruption on the chest, upper back, and proximal extremities. A clinical diagnosis of subacute cutaneous lupus erythematosus (SCLE) was made. The patient was found to have a positive anti-nuclear antibody (ANA) in a speckled pattern and a positive anti-Ro antibody. A biopsy revealed an interface and lichenoid dermatitis with dermal mucin deposition, consistent with subacute cutaneous lupus erythematosus. The patient reported that he had recently been diagnosed with hypertension and began treatment with olmesartan, a potassium-sparing diuretic that blocks the angiotensin II receptor, commonly used as an antihypertensive or in patients with heart failure. Cutaneous reactions to olmesartan are rare and reported in <1% of patients in post-marketing surveillance. The patient discontinued use of olmesartan and the rash completely resolved within three weeks. To date, there are no other reported cases of drug induced SCLE in patients taking olmesartan to our knowledge

Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart.

In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images

Toward time resolved 4D cardiac CT imaging with patient dose reduction: estimating the global heart motion

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Development of a computer-generated model for the coronary arterial tree based on multislice CT and morphometric data

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Effect of heart rate on CT angiography using the enhanced cardiac model of the 4D NCAT

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Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.Comment: 8 pages, 3 figures, submitted to Medical Physics Lette

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable

Design and performance of a compact and stationary microSPECT system

Purpose: Over the last ten years, there has been an extensive growth in the development of microSPECT imagers. Most of the systems are based on the combination of conventional, relatively large gamma cameras with poor intrinsic spatial resolution and multipinhole collimators working in large magnification mode. Spatial resolutions range from 0.58 to 0.76 mm while peak sensitivities vary from 0.06% to 0.4%. While pushing the limits of performance is of major importance, the authors believe that there is a need for smaller and less complex systems that bring along a reduced cost. While low footprint and low-cost systems can make microSPECT available to more researchers, the ease of operation and calibration and low maintenance cost are additional factors that can facilitate the use of microSPECT in molecular imaging. In this paper, the authors simulate the performance of a microSPECT imager that combines high space-bandwidth detectors and pinholes with truncated projection, resulting in a small and stationary system. Methods: A system optimization algorithm is used to determine the optimal SPECT systems, given our high resolutions detectors and a fixed field-of-view. These optimal system geometries are then used to simulate a Defrise disk phantom and a hot rod phantom. Finally, a MOBY mouse phantom, with realistic concentrations of Tc99m-tetrofosmin is simulated. Results: Results show that the authors can successfully reconstruct a Defrise disk phantom of 24 mm in diameter without any rotating system components or translation of the object. Reconstructed spatial resolution is approximately 800 mu m while the peak sensitivity is 0.23%. Finally, the simulation of the MOBY mouse phantom shows that the authors can accurately reconstruct mouse images. Conclusions: These results show that pinholes with truncated projections can be used in small magnification or minification mode to obtain a compact and stationary microSPECT system. The authors showed that they can reach state-of-the-art system performance and can successfully reconstruct images with realistic noise levels in a preclinical context. Such a system can be useful for dynamic SPECT imaging. 2013 American Association of Physicists in Medicine

Four-dimensional Cone Beam CT Reconstruction and Enhancement using a Temporal Non-Local Means Method

Four-dimensional Cone Beam Computed Tomography (4D-CBCT) has been developed to provide respiratory phase resolved volumetric imaging in image guided radiation therapy (IGRT). Inadequate number of projections in each phase bin results in low quality 4D-CBCT images with obvious streaking artifacts. In this work, we propose two novel 4D-CBCT algorithms: an iterative reconstruction algorithm and an enhancement algorithm, utilizing a temporal nonlocal means (TNLM) method. We define a TNLM energy term for a given set of 4D-CBCT images. Minimization of this term favors those 4D-CBCT images such that any anatomical features at one spatial point at one phase can be found in a nearby spatial point at neighboring phases. 4D-CBCT reconstruction is achieved by minimizing a total energy containing a data fidelity term and the TNLM energy term. As for the image enhancement, 4D-CBCT images generated by the FDK algorithm are enhanced by minimizing the TNLM function while keeping the enhanced images close to the FDK results. A forward-backward splitting algorithm and a Gauss-Jacobi iteration method are employed to solve the problems. The algorithms are implemented on GPU to achieve a high computational efficiency. The reconstruction algorithm and the enhancement algorithm generate visually similar 4D-CBCT images, both better than the FDK results. Quantitative evaluations indicate that, compared with the FDK results, our reconstruction method improves contrast-to-noise-ratio (CNR) by a factor of 2.56~3.13 and our enhancement method increases the CNR by 2.75~3.33 times. The enhancement method also removes over 80% of the streak artifacts from the FDK results. The total computation time is ~460 sec for the reconstruction algorithm and ~610 sec for the enhancement algorithm on an NVIDIA Tesla C1060 GPU card.Comment: 20 pages, 3 figures, 2 table