Representative Regions Of Interest Research Articles

Distribution of 2-deoxy-2-fluoro-d-glucose in the coelom of healthy bald eagles (Haliaeetus leucocephalus)

To determine 2-deoxy-2-fluoro (fluorine 18)-d-glucose ((18)FDG) biodistribution in the coelom of bald eagles (Haliaeetus leucocephalus). 8 healthy adult bald eagles. For each eagle, whole-body transmission noncontrast CT, 60-minute dynamic positron emission tomography (PET) of the celomic cavity (immediately after (18)FDG injection), whole-body static PET 60 minutes after (18)FDG injection, and whole-body contrast CT with iohexol were performed. After reconstruction, images were analyzed. Regions of interest were drawn over the ventricular myocardium, liver, spleen, proventriculus, cloaca, kidneys, and lungs on dynamic and static PET images. Standardized uptake values were calculated. Kidneys had the most intense (18)FDG uptake, followed by cloaca and intestinal tract; liver activity was mild and slightly more intense than that of the spleen; proventricular activity was always present, whereas little to no activity was identified in the wall of the ventriculus. Activity in the myocardium was present in all birds but varied in intensity among birds. The lungs had no visibly discernible activity. Mean ± SD standardized uptake values calculated with representative regions of interest at 60 minutes were as follows: myocardium, 1. 6 ± 0.2 (transverse plane) and 1.3 ± 0.3 (sagittal plane); liver, 1.1 ± 0.1; spleen, 0.9 ± 0.1; proventriculus, 1.0 ± 0.1; cloaca, 4.4 ± 2.7; right kidney, 17.3 ± 1.0; left kidney, 17.6 ± 0.3; and right and left lungs (each), 0.3 ± 0.02. The study established the biodistribution of (18)FDG in adult eagles, providing a baseline for clinical investigation and future research.

Voting-Based Motion Estimation for Real-Time Video Transmission in Networked Mobile Camera Systems

In a video-based surveillance system, a mobile camera can provide dynamical and wider monitoring range and the video data transmitted from cooperative mobile cameras can be used to actively detect the objects of interest. However, it is a difficult task to accurately detect the moving objects from the image frames captured by the mobile cameras and the data flow of surveillance video from multiple cameras could be huge. The camera motion usually causes the shifting of static background as well as the moving objects in the captured image frames. In order to correctly estimate the motion of moving objects, a voting-based motion estimation algorithm is proposed to process the image frames captured by the mobile camera. Based on the estimation, a content-based video transmission mechanism is then implemented to further effectively decrease encoding cost and bandwidth utilization. The overall approach consists of voting-based motion estimation, moving object edges detection and content-based sampling coding at temporal and spatial scales. Without knowing the prior knowledge of camera motion, the motion estimation algorithm only utilizes the shifting information of edges of static background to estimate the camera movement. The shifting information is determined based on the voting decision of several representative regions of interest and the estimated motion is then used to compensate for the visual content obtained from the captured image frames. The proposed algorithms have been experimentally tested on several practical scenarios and it is demonstrated that, under limited network bandwidth, the transmitted image quality can be progressively achieved and the transmission bandwidth utilization can be effectively decreased.

Detection of common defects on oranges using hyperspectral reflectance imaging

To detect various common defects on oranges, a hyperspectral imaging system has been built for acquiring reflectance images from orange samples in the spectral region between 400 and 1000 nm. Oranges with insect damage, wind scarring, thrips scarring, scale infestation, canker spot, copper burn, phytotoxicity, heterochromatic stripe, and normal surface were studied. Hyperspectral images of samples were evaluated using principal component analysis (PCA) with the goal of selecting several wavelengths that could potentially be used in an in-line multispectral imaging system. The third principal component images using six wavelengths (630, 691, 769, 786, 810 and 875 nm) in the visible spectral (VIS) and near-infrared (NIR) regions, or the second principal component images using two wavelengths (691 and 769 nm) in VIS region gave better identification results under investigation. However, the stem-ends were easily confused with defective areas. In order to solve this problem, representative regions of interest (ROIs) reflectance spectra of samples with different types of skin conditions were visually analyzed. The researches revealed that a two-band ratio (R875/R691) image could be used to differentiate stem-ends from defects effectively. Finally, the detection algorithm of defects was developed based on PCA and band ratio coupled with a simple thresholding method. For the investigated independent test samples, accuracies of 91.5% and 93.7% with no false positives were achieved for both sets of selected wavelengths using proposed method, respectively. The disadvantage of this algorithm is that it could not discriminate between different types of defects.

Quantitative Diffusion Tensor Imaging of White Matter Microstructure in Dog Brain at 7T

The purpose of this study is to develop a noninvasive quantitative imaging scheme to assess the functional microanatomy of dog brain using high resolution diffusion tensor imaging (DTI) at 7-Tesla (T) magnetic resonance imaging (MRI) scanner. Diffusion weighted images (DWIs) along 25 diffusion-encoding directions, combined with T2 weighted images, were acquired using the standard spin-echo diffusion encoding scheme to determine the diffusion tensor matrix. Three diffusion indices: fractional anisotropy (FA), Trace/3 apparent diffusion coefficient (ADC), and volume ratio (VR) were measured within seven representative regions of interest (ROIs) from internal capsule, corpus callosum, caudate nucleus, hippocampus, thalamus, fornix, and cerebral cortex. Experimental results show much higher scalar contrast and more accurate fiber structures based on the FA weighted color map and fiber orientation map, indicating this high angular DTI technique at 7T is an accurate scheme to map the microstructures in fixed dog brains, which is also applicable for studying the functional microanatomy of other animal models.

Digital Image Analysis Is a Useful Adjunct to Endoscopic Ultrasonographic Diagnosis of Subepithelial Lesions of the Gastrointestinal Tract

The purpose of this study was to explore the role of digital image analysis in differentiating endoscopic ultrasonographic (EUS) features of potentially malignant gastrointestinal subepithelial lesions (SELs) from those of benign lesions. Forty-six patients with histopathologically confirmed gastrointestinal stromal tumors (GISTs), carcinoids, and lipomas who had undergone EUS evaluation were identified from our database. Representative regions of interest (ROIs) were selected from the EUS images, and features were extracted by texture analysis. On the basis of these features, an artificial neural network (ANN) was built, trained, and internally validated by unsupervised learning followed by supervised learning. Outcomes were the performance characteristics of the ANN. A total of 106, 111, and 124 ROIs were selected from EUS images of 8, 10, and 28 patients with lipomas, carcinoids, and GISTs, respectively. For each ROI, 228 statistical parameters were extracted and later reduced to the 11 most informative features by principal component analysis. After training with 50% of the data, the remainder of the data were used to validate the ANN. The model was "good" in differentiating carcinoids and GISTs, with area under the receiver operating characteristic curve (AUC) values of 0.86 and 0.89, respectively. The model was "excellent" in identifying lipomas correctly, with an AUC of 0.92. Digital image analysis of EUS images is a useful noninvasive adjunct to EUS evaluation of SELs.

Routine MRI findings of the asymptomatic foot in diabetic patients with unilateral Charcot foot

BackgroundImaging studies of bones in patients with sensory deficits are scarce.AimTo investigate bone MR images of the lower limb in diabetic patients with severe sensory polyneuropathy, and in control subjects without sensory deficits.MethodsRoutine T1 weighted and T2-fat-suppressed-STIR-sequences without contrast media were performed of the asymptomatic foot in 10 diabetic patients with polyneuropathy and unilateral inactive Charcot foot, and in 10 matched and 10 younger, non-obese unmatched control subjects. Simultaneously, a Gadolinium containing phantom was also assessed for reference. T1 weighted signal intensity (SI) was recorded at representative regions of interest at the peritendineal soft tissue, the tibia, the calcaneus, and at the phantom. Any abnormal skeletal morphology was also recorded.ResultsMean SI at the soft tissue, the calcaneus, and the tibia, respectively, was 105%, 105% and 84% of that at the phantom in the matched and unmatched control subjects, compared to 102% (soft tissue), 112% (calcaneus) and 64% (tibia) in the patients; differences of tibia vs. calcaneus or soft tissue were highly significant (p < 0.005). SI at the tibia was lower in the patients than in control subjects (p < 0.05). Occult traumatic skeletal lesions were found in 8 of the 10 asymptomatic diabetic feet (none in the control feet).ConclusionMR imaging did not reveal grossly abnormal bone marrow signalling in the limbs with severe sensory polyneuropathy, but occult sequelae of previous traumatic injuries.

Soft tissue discrimination ex vivo by dual energy computed tomography

Purpose Dual Energy Computed Tomography (DECT) may provide additional information about the chemical composition of tissues compared to examination with a single X-ray energy. The aim of this in vitro study was to test whether combining two energies may significantly improve the detection of soft tissue components commonly present in arterial plaques. Methods Tissue samples of myocardial and psoas muscle, venous and arterial thrombus as well as fat from different locations were scanned using a SOMATOM Definition Dual Source CT system (Siemens AG, Medical Solutions, Forchheim, Germany) with simultaneous tube voltages of 140 and 80 kV. The attenuation (Hounsfield units, HU) at 80 and 140 kV was measured in representative regions of interest, and the association between measured HU values and tissue types was tested with logistic regression. Results The combination of two energy levels (80 and 140 kV) significantly improved ( p < 0.001) the ability to correctly classify venous thrombus vs arterial thrombus, myocardium or psoas; arterial thrombus vs myocardium or psoas; myocardium vs psoas; as well as the differentiation between fat tissue from various locations. Single energy alone was sufficient for distinguishing fat from other tissues. Conclusion DECT offers significantly improved in vitro differentiation between soft tissues occurring in plaques. If this corresponds to better tissue discrimination in vivo needs to be clarified in future studies.

A multi-classifier system for the characterization of normal, infectious, and cancerous prostate tissues employing transrectal ultrasound images

A computer-aided diagnostic system has been developed for the discrimination of normal, infectious and cancer prostate tissues based on texture analysis of transrectal ultrasound images. The proposed system has been designed using a panel of three classifiers, which have been evaluated individually or as a mutli-classifier scheme, using the external cross-validation procedure. Clinical data consisted of 165 transrectal ultrasound images, characterized by an experienced physician as normal (55/165), cancerous (55/165), and infectious (55/165) prostate cases. From each image, the physician delineated the most representative regions of interest, from which, 23 textural features were extracted. Classification was seen as a two level hierarchical decision tree. Normal from infectious and infectious from cancer cases were discriminated at the 1st and 2nd level of the decision tree, respectively. The best classification results for the 1st level were 89.5%, whereas for the 2nd level 90.1%. The utilization of multi-classifier system improved the discrimination of prostate pathologies as compared to individual classifiers; for infectious prostate cases improvement was from 87.3% to 88.7% and for cancer prostate cases improvement was from 84.1% to 91.4%. In terms of overall system performance (the decision tree's node propagating error taken into account), best classification accuracies were 89.5%, 79.6% and 82.7% for the recognition of normal, infectious and cancer cases, respectively. The proposed system might be used as a second opinion tool for assisting diagnosis of different prostate pathologies.

Intravenous application of fluorescein for confocal laser scanning microscopy: evaluation of contrast dynamics and image quality with increasing injection-to-imaging time

Confocal laser scanning microscopy (CLSM) is reported to be a promising tool for in vivo histopathology during an endoscopy. The most commonly used fluorophore is fluorescein sodium given intravenously. However, so far, there are no objective data on contrast dynamics and image quality over time after injection of the substance. We aimed to investigate the ideal time period for the best CLSM imaging when using fluorescein sodium. The proximal stomachs of 4 anesthetized pigs were examined via standard endoscopes with a portable confocal miniprobe. After injection of 1% fluorescein intravenously (0.1 mL/kg body weight), consecutive sequences were recorded after 1, 2, 3, 5, 8, 20, 30, 40, 50, and 60 minutes. After the procedure, histograms, which represented signal intensity of 20 images (at respective time points), were analyzed from representative regions of interest (ROI). The mean (standard deviation [SD]) pixel value of the pixel values in the ROI were noted. A signal-to-noise ratio (SNR), defined as the ratio between the mean and the SD, was calculated. In addition, 5 images per time period were put into a random order and then blindly evaluated by 2 endoscopists. A total of 560 histograms from selected ROIs were analyzed. The SNR decreased significantly after 8 minutes, with the lowest values being reached after 30 minutes. Subjective evaluation of still images also demonstrated a continuous decrease, with the lowest image quality after 30 minutes. A slight increase of image quality after 40 minutes occurred because of extramucosal leakage of fluorescein, with fluorophore deposits in the crypt orifices. In the pig model, fluorescein-based CLSM of the GI tract delivered the best contrast and image quality within the first 8 minutes after injection of the fluorophore. These data might facilitate standardization of the procedure for research purposes but may also help when applying this new technology in a daily routine manner.

Digital Image Analysis (DIA) Is a Useful Adjunct to Endosonographic Diagnosis of Submucosal Lesions (SML) of the Gastro-Intestinal Tract

Establishing definitive diagnosis of gastrointestinal SML remains a challenge. Information from EUS further delineates the lesions, but histologic data remain the gold standard. To enhance the diagnostic capability of EUS, with the hope to circumvent the need for tissue acquisition, EUS images were digitally analyzed using the technique described pervious by us to discern any parameters that can differentiate clinically significant lesions such as, gastrointestinal stromal cell tumors (GIST) and carcinoids from more benign lesions such as such as, lipoma. Objective: To evaluate the role of DIA of EUS images of SML. Methods: Patients with histopathologically confirmed SML who had undergone EUS evaluation were identified from our database. Representative regions of interest (ROIs) were digitally selected from their EUS images [Gastrointest Endosc 2006;63(5): AB 256, Gastrointest Endosc 2007;65(5):AB 103, & Das, Gastrointest Endosc 2008 (in press)]. Texture analyses were performed using histogram, run-length and co-occurrence matrix (measuring runs of pixels and distribution of pairs of pixels), gradient analysis (spatial distribution of pixels), auto-regressive analysis (measurement of local interaction amongst pixels of different grey level values) and also wavelet analysis (which measures parameters of spatial frequency). Results: A total of 106, 111 and 124 ROIs were selected from EUS images of 8, 10 and 28 patients with lipoma, carcinoids and GIST, respectively. Principal component analysis (PCA) was used for data reduction and later a neural network (ANN) based predictive model was built, trained and validated using the extracted texture features for classification. The performance characteristics of the ANN model in classifying lipoma, carcinoids and GIST is shown in Table. Three types of features of pixel distribution had high discriminatory power: Kurtosis (measure of the steepness of the pixel distribution), angular second momentum and horizontal and vertical grey level non-uniformity (parameters estimating the second order joint conditional probability density functions of spatial distribution of two neighboring pixels). Conclusion: DIA of texture features of EUS images is a useful non-invasive adjunct to endosonographic diagnosis of submucosal lesions. Tabled 1 SML Sensitivity Specificity PPV NPV AUC ∗ AUC of ROC curve. Lipoma 0.90 0.77 0.64 0.95 0.92 Carcinoid 0.79 0.81 0.67 0.89 0.86 GIST 0.81 0.79 0.74 0.86 0.89 ∗ AUC of ROC curve. Open table in a new tab

Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue

Concomitant changes of chronic pancreatitis markedly degrade the performance of EUS in diagnosing pancreatic adenocarcinoma (PC). Digital image analysis (DIA) of the spatial distribution of pixels in a US image has been used as an effective approach to tissue characterization. We applied the techniques of DIA to EUS images of the pancreas to develop a classification model capable of differentiating pancreatic adenocarcinoma from non-neoplastic tissue. Representative regions of interest were digitally selected in EUS images of 3 groups of patients with normal pancreas (group I), chronic pancreatitis (group II), and pancreatic adenocarcinoma (group III). Texture analyses were then performed by using image analysis software. Principal component analysis (PCA) was used for data reduction, and, later, a neural-network-based predictive model was built, trained, and validated. Tertiary academic medical center. Patients undergoing EUS of the pancreas. A total of 110, 99, and 110 regions of interest in groups I, II, III, respectively, were available for analysis. For each region, a total of 256 statistical parameters were extracted. Eleven parameters were subsequently retained by PCA. A neural network model was built, trained by using these parameters as input variables for prediction of PC, and then validated in the remainder of the data set. This model was very accurate in classifying PC with an area under the receiver operating characteristic curve of 0.93. Exploratory study with a small number of patients. DIA of EUS images is accurate in differentiating PC from chronic inflammation and normal tissue. With the potential availability of real-time application, DIA can develop into a useful clinical diagnostic tool in pancreatic diseases and in certain situations may obviate EUS-guided FNA.

Digital Image Analysis (DIA) of EUS Images Accurately Differentiates Pancreatic Cancer (PC) from Chronic Pancreatitis (CP)

Changes of CP markedly degrades the accuracy of EUS in diagnosing PC. We previously reported (DDW 2006) DIA based on spatial distribution of ultrasound amplitude or grey-level pixels of EUS images as an approach to tissue characterization. Objective: We performed quantitative analysis of texture parameters of EUS images to evaluate if DIA can reliably distinguish PC from CP. Methods: Representative regions of interest (ROIs) were digitally selected from EUS images of patients with normal pancreas (group I), established CP (diagnosed by clinical features and multiple imaging modalities, group II) and histologically confirmed PC (group III,) and were analyzed with a DIA software. Texture analyses were performed using histogram, run-length and co-occurrence matrix (measuring runs of pixels and distribution of pairs of pixels), gradient analysis (spatial distribution of pixels), auto-regressive analysis (measurement of local interaction amongst pixels of different grey level values) and also wavelet analysis (which measures parameters of spatial frequency). Principal component analysis (PCA) was used for data reduction and later a neural network (NN) based predictive model was built, trained and validated using the extracted texture features for classification. Methods: A total of 110, 99 and 110 ROIs in Group I, II, III, respectively, were available for analysis and for each ROI, a total 256 statistical parameters were initially extracted and 11 features (representing 96% of initial eigenvalues) were retained subsequently by PCA. With a random selection of the half of the dataset, a multi-layered perceptron NN model was built, and trained using the 11 PCA selected parameters as input variables for prediction of PC and then the model was validated in the remainder of the dataset. The NN model had a very high classification performance in classifying ROIs representing PC with an area under Receiver Operating Characteristic Curve of 0.93 in the validation set. Three types of features of pixel distribution had high discriminatory power: Kurtosis (measure of the steepness of the pixel distribution), angular second momentum and horizontal and vertical grey level non-uniformity (parameters estimating the second order joint conditional probability density functions of spatial distribution of two neighboring pixels). Conclusion: DIA of texture features of EUS images is highly accurate in differentiating PC from CP and normal tissue, with performance characteristics similar to aspiration cytology. If real-time application of DIA achieves similar results, this technology can potentially eliminate the need for tissue aquisition.

Particle Image Velocimetry Measurements in a Model Proton Exchange Membrane Fuel Cell

Particle image velocimetry was used to measure 2D velocity fields in representative regions of interest within flow channels of interdigitated and single-serpentine proton exchange membrane (PEM) fuel cell models. The model dimensions, gas diffusion layer (GDL) permeability, working fluid, and flow rates were selected to be geometrically and dynamically similar to the cathode-side airflow in a typical PEM fuel cell. The model was easily reconfigurable between parallel, single-serpentine, and interdigitated flow fields, and was constructed from transparent materials to enable optical imaging. Velocity maps were obtained of both the primary and secondary flow within the channels. Measurements of the secondary flows in interdigitated and single-serpentine flow fields indicate that significant portions of the flow travel between adjacent channels through the porous medium. Such convective bypass can enhance fuel cell performance by supplying fresh reactant to the lands regions and also by driving out product water from under the lands to the flow channels.

Positive or negative blips? The effect of phase encoding scheme on susceptibility-induced signal losses in EPI

The observation of blood oxygenation level-dependent (BOLD) effect in functional magnetic resonance imaging (fMRI) studies is often hampered by the presence of magnetic field inhomogeneities. These are caused by abrupt changes in the magnetic susceptibility that typically occur near air/tissue interfaces and may result in substantial image distortions and signal losses. In this article, we investigate the effect of susceptibility-induced magnetic field inhomogeneities on the signal intensity ( I) and the BOLD sensitivity ( BS) for two different phase encoding schemes in blipped echo-planar imaging (EPI), which use either positive (EPI pos) or negative (EPI neg) phase gradient blips for stepping through k-space. Based on magnetic field maps, we generate computer simulations of I and BS for both phase encoding schemes and demonstrate good agreement with the experimental image intensities. We show that regions compromised by susceptibility effects are affected very differently by EPI pos and EPI neg. Further simulations are performed in two representative regions of interest (orbitofrontal cortex and lower temporal lobe) to investigate the dependence of I and BS on the slice angle ( α), the magnitude of a moderate compensation gradient applied in the slice direction ( G S comp), and the phase encoding scheme. We find that I and BS can be considerably increased if the appropriate phase encoding scheme is applied in addition to optimizing α and G S comp. Our results suggest that this optimization method would be useful in future fMRI studies to improve the sensitivity in regions compromised by susceptibility effects.

Thyroid echogenicity in manic-depressive patients receiving lithium therapy

Background: Lithium is known to induce subclinical or overt hypothyroidism and changes of thyroid volume in manic-depressive patients. Little is known about alterations of thyroid echogenicity due to drug-induced dysfunction. Methods: Twenty manic-depressive patients receiving lithium therapy for at least 6 months and 20 euthyroid volunteers without any antidepressive medication as control group, matched in age and gender, were investigated by laboratory measurements and thyroid ultrasonography including standardized grey scale analyses in representative regions of interest (ROI). Results: Thyroid function was normal in all patients (mean FT4 1.1±0.2 ng/dl, mean TSH 1.6±0.9 μ U/ml) and controls (mean FT4 1.5±0.4 ng/dl, TSH 1.1±0.3 μ U/ml). Except for two patients, no thyroid autoantibody levels could been detected. Thyroid volumetry revealed significant higher mean values for the lithium treated patients (16.9±11.9 ml) compared with the controls (11.4±4.5 ml, P<0.05) with a considerable number of goiters (six patients vs. one control). Thyroid echogenicities in both groups were similar (patients 23.9±3.7 grey scales, Grauwerteinheiten=GWE, controls 24.2±1.3 GWE) and did not depend on the size of the organs. Conclusion: Lithium treatment contributes to increased thyroid volumes, probably due to inhibition of thyroid function and TSH upregulation, but not to changes of thyroid echo levels in patients with still euthyroid function. Further echogenicity studies on patients with lithium-induced overt hypothyroidism and autoimmune activity will be of special interest.

Evaluation of Efficacy of an Automated Single-Voxel Proton MRS Algorithm on a 3T System

To evaluate the efficacy of a fully automatic, single-voxel, proton MR spectroscopy algorithm on a 3.0T MR system. The PROBE/SVQ algorithm (GE Medical Systems) was evaluated on a General Electric (GE) Signa LX-3T system. Ten normal volunteers (female, 21.1+/-2.0 years old) participated in the study. Nine representative regions of interest were examined, namely, right and left frontal white matter; right and left parietal white matter; right and left basal ganglia; right and left dentate nucleus; and pons. The mean coefficients of variation in all regions for the N-acetyl-aspartate to creatine ratio (NAA/Cr), choline to creatine ratio (Cho/Cr), and choline to N-acetyl-aspartate ratio (Cho/NAA) were 10.2+/-2.4%, 11.4+/-3.2% and 12.6+/-1.4%, respectively. A fully automated spectroscopic examination under clinical setting utilizing the PROBE/SVQ algorithm appears to be a reliable method, extending the window of routine clinical assessment of brain metabolism.

Regional improvement of signal-to-noise and contrast-to-noise ratios in dual-screen CR chest imaging--a phantom study.

The improvement of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in dual-screen computed radiography (CR) has been investigated for various regions in images of an anthropomorphic chest phantom. With the dual-screen CR technique, two image plates are placed in a cassette and exposed together during imaging. The exposed plates are separately scanned to form a front image and a back image, which are then registered and superimposed to form a composite image with improved SNRs and CNRs. The improvement can be optimized by applying specifically selected weighting factors during superimposition. In this study, dual-screen CR images of an anthropomorphic chest phantom were acquired and formed with four different combinations of standard resolution (ST) and high-resolution (HR) screens: ST-ST, ST-HR, HR-ST, and HR-HR. SNRs and their improvements were measured and compared over twelve representative regions-of-interest (ROIs) in these images. A 19.1%-45.7% increase of the SNR was observed, depending on the ROI and screen combination used. The optimal weighting factors were found to vary by only 4.5%-12.4%. Largest improvement was found in the lung field for all screen combinations. Improvement of CNRs was investigated over two ROIs in the lung field using the rib bones as the contrast objects and a 29.2%-43.9% improvement of the CNR was observed. Among the four screen combinations, ST-ST resulted in the most SNR and CNR improvement, followed in order by HR-ST, HR-HR, and ST-HR. The HR-ST combination yielded the lowest spatial variation of the optimal weighting factors with improved SNRs and CNRs close to those of the ST-ST combination.

Attenuation compensation of cone beam SPECT images using maximum likelihood reconstruction

Attenuation compensation for cone beam single-photon emission computed tomography (SPECT) imaging is performed by cone beam maximum likelihood reconstruction with attenuation included in the transition matrix. Since the transition matrix is too large to be stored in conventional computers, the E-M maximum likelihood estimator is implemented with a ray-tracing algorithm, efficiently recalculating each matrix element as needed. The method was applied and tested in both uniform and nonuniform density phantoms. Test projections sets were obtained from Monte Carlo simulations and experiments using a commercially available cone beam collimator. For representative regions of interest. reconstruction of a uniform sphere is accurate to within 3% throughout, in comparison to a reference image simulated and reconstructed without attenuation. High- and low-activity regions in a uniform density are reconstructed accurately, except that low-activity regions in a more active background have a small error. This error is explainable by the nonnegativity constraints of the E-M estimator and the image statistical noise.