Improved image quality and computation reduction in 4-D reconstruction of cardiac-gated SPECT images

Spatiotemporal reconstruction of cardiac-gated SPECT images permits us to obtain valuable information related to cardiac function. However, the task of reconstructing this four-dimensional (4-D) data set is computation intensive. Typically, these studies are reconstructed frame-by-frame: a nonoptimal approach because temporal correlations in the signal are not accounted for. In this work, we show that the compression and signal decorrelation properties of the Karhunen-Loève (KL) transform may be used to greatly simplify the spatiotemporal reconstruction problem. The gated projections are first KL transformed in the temporal direction. This results in a sequence of KL-transformed projection images for which the signal components are uncorrelated along the time axis. As a result, the 4-D reconstruction task is simplified to a series of three-dimensional (3-D) reconstructions in the KL domain. The reconstructed KL components are subsequently inverse KL transformed to obtain the entire spatiotemporal reconstruction set. Our simulation and clinical results indicate that KL processing provides image sequences that are less noisy than are conventional frame-by-frame reconstructions. Additionally, by discarding high-order KL components that are dominated by noise, we can achieve savings in computation time because fewer reconstructions are needed in comparison to conventional frame-by-frame reconstructions.     

Relevance vector machine for automatic detection of clustered microcalcifications

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.     

Selective esterolysis of nitrophenyl acetates in crystalline α-cyclodextrin complexes

The esterolysis ofm-nitrophenyl acetate (mNPA) andp-nitrophenyl acetate (pNPA), complexed with -cyclodextrin, was investigated in the solid state. At 117 and 140°C, the initial half-times ofmNPA esterolysis were 30 and 24 h, respectively, whereasp NPA esterolysis was undetectably slow. At 117°C, themNPA reaction proceeded to completion, and cyclodextrin acetate andm-nitrophenol products were identified. At 140°C,the initial rate was followed by a slow phase with a half-time of 130 h, evidently due to a structural change in the complex. Themeta/para selectivity of the solid-state reaction is considerably enhanced over the selectivity reported in aqueous solution.     

Superconductivity with onset above 23° K in NbGe sputtered films

Sputtered films of nominal composition Nb₃Ge deposited onto hot substrates, of the type recently found by Gavaler to have superconductivity onsets at 22.3° K, have now been obtained with a maximum onset temperature of 23.2 ± 0.2° K and a transition width of ～ 1.2° K. The sputtering conditions necessary to achieve these results are discussed and suggest that the high T_c phase may be detrimentally affected by the presence of high energy particles present under normal sputtering conditions.     

SiFe0.5Co0.5 - a weak itinerant ferromagnet

The high field and high pressure magnetic properties of SiFe_0.5Co_0.5 alloy are characteristic of weak itinerant ferromagnets.     

Optical reflectivity in A15 compounds

Optical reflectivity at normal incidence for a series of A15 compounds shows an absorption peak at 0.7 eV for V₃Si and V₃Ge and is attributed to a critical point in the joint density of states. No such peak was found in Ti₃Sb and V₃Pt.