Myocardial Defect Detection Using PET-CT: Phantom Studies

It is expected that both noise and activity distribution can have impact on the detectability of a myocardial defect in a cardiac PET study. In this work, we performed phantom studies to investigate the detectability of a defect in the myocardium for different noise levels and activity distributions. We evaluated the performance of three reconstruction schemes: Filtered Back-Projection (FBP), Ordinary Poisson Ordered Subset Expectation Maximization (OP–OSEM), and Point Spread Function corrected OSEM (PSF–OSEM). We used the Channelized Hotelling Observer (CHO) for the task of myocardial defect detection. We found that the detectability of a myocardial defect is almost entirely dependent on the noise level and the contrast between the defect and its surroundings

Seismic reservoir characterization of a deep water sandstone reservoir using hydraulic and electrical flow units: A case study from the Shah Deniz gas field, the South Caspian Sea

Seismic reservoir characterization of deep water sandstones using hydraulic and electrical flow units plays an important role in delineation of oil and gas traps in the Caspian Sea basin. The proposed methodology of this study comprises two major steps. Firstly, the reservoir rock types, including Hydraulic Flow Units (HFUs) and Electrical Flow Units (EFUs) are estimated from petrophysical data. Secondly, seismic data are converted into HFUs and EFUs by using the seismic attributes technology in conjunction with the neural networks and fuzzy clustering algorithms. Optimal seismic attributes for the estimation of Current Zone Index (CZI) and Flow Zone Indicator (FZI) include acoustic impedance, which was derived from a model based inversion, together with dominant frequency and amplitude envelope data. High porosity and permeability zones are delineated by using the seismic derived flow zone indicator data. Since there is a strong correlation between water saturation and current zone indicator, hydrocarbon saturation changes within the sandstone packages are investigated by using the EFU model. The integrated approach introduced in this study is successful in highlighting high porosity and low water saturations zones of the Shah Deniz sandstone packages

Stigmaeus maraghehiensis, a new species of the genus Stigmaeus Koch (Acari: Stigmaeidae) from northwest Iran

A new species of Stigmaeus Koch (Acari: Stigmaeidae), Stigmaeus maraghehiensis sp. nov., is described and illustrated from the soil in apple orchards in Maragheh, East Azerbaijan Province, Iran. A key to all known Iranian species of the genus is provided.http://www.tandfonline.com/doi/pdf/10.1080/01647954.2011.583272http://dx.doi.org/10.1080/01647954.2011.58327

Bias atlases for segmentation-based pet attenuation correction using PET-CT and MR

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.close0

CHO SNR versus defect/myocardium (A) and myocardium/background (B) concentration ratios.

<p>The dashed lines were obtained using weighted least squares linear regression.</p

The anthropomorphic torso phantom used for the study.

<p>The anthropomorphic torso phantom used for the study.</p

Reconstructed image slices and line profiles through the myocardial defect.

<p>Reconstructed image slices and line profiles through the myocardial defect.</p

CHO SNR versus liver/background (A) and lung/background (B) concentration ratios.

<p>CHO SNR versus liver/background (A) and lung/background (B) concentration ratios.</p

CHO SNR versus total number of counts in the background (A) and iteration number using PSF-OSEM for the default case described in Sec. 2, Materials and Methods.

<p>CHO SNR versus total number of counts in the background (A) and iteration number using PSF-OSEM for the default case described in Sec. 2, Materials and Methods.</p