Heterogeneous expression of GABA receptor-like subunits LCCH3 and GRD reveals functional diversity of GABA receptors in the honeybee Apis mellifera.

BACKGROUND AND PURPOSE: Despite a growing awareness, annual losses of honeybee colonies worldwide continue to reach threatening levels for food safety and global biodiversity. Among the biotic and abiotic stresses probably responsible for these losses, pesticides, including those targeting ionotropic GABA receptors, are one of the major drivers. Most insect genomes include the ionotropic GABA receptor subunit gene, Rdl, and two GABA-like receptor subunit genes, Lcch3 and Grd. Most studies have focused on Rdl which forms homomeric GABA-gated chloride channels, and a complete analysis of all possible molecular combinations of GABA receptors is still lacking. EXPERIMENTAL APPROACH: We cloned the Rdl, Grd, and Lcch3 genes of Apis mellifera and systematically characterized the resulting GABA receptors expressed in Xenopus oocytes, using electrophysiological assays, fluorescence microscopy and co-immunoprecipitation techniques. KEY RESULTS: The cloned subunits interacted with each other, forming GABA-gated heteromeric channels with particular properties. Strikingly, these heteromers were always more sensitive than AmRDL homomer to all the pharmacological agents tested. In particular, when expressed together, Grd and Lcch3 form a non-selective cationic channel that opens at low concentrations of GABA and with sensitivity to insecticides similar to that of homomeric Rdl channels. CONCLUSION AND IMPLICATIONS: For off-target species like the honeybee, chronic sublethal exposure to insecticides constitutes a major threat. At these concentration ranges, homomeric RDL receptors may not be the most pertinent target to study and other ionotropic GABA receptor subtypes should be considered in order to understand more fully the molecular mechanisms of sublethal toxicity to insecticides.

Prior-Guided Metal Artifact Reduction for Iterative X-Ray Computed Tomography.

High-attenuation materials pose significant challenges to computed tomographic imaging. Formed of high mass-density and high atomic number elements, they cause more severe beam hardening and scattering artifacts than do water-like materials. Pre-corrected line-integral density measurements are no longer linearly proportional to the path lengths, leading to reconstructed image suffering from streaking artifacts extending from metal, often along highest-density directions. In this paper, a novel prior-based iterative approach is proposed to reduce metal artifacts. It combines the superiority of statistical methods with the benefits of sinogram completion methods to estimate and correct metal-induced biases. Preliminary results show minimized residual artifacts and significantly improved image quality.

Modeling and Pre-Treatment of Photon-Starved CT Data for Iterative Reconstruction.

An increasing number of X-ray CT procedures are being conducted with drastically reduced dosage, due at least in part to advances in statistical reconstruction methods that can deal more effectively with noise than can traditional techniques. As data become photon-limited, more detailed models are necessary to deal with count rates that drop to the levels of system electronic noise. We present two options for sinogram pre-treatment that can improve the performance of photon-starved measurements, with the intent of following with model-based image reconstruction. Both the local linear minimum mean-squared error (LLMMSE) filter and pointwise Bayesian restoration (PBR) show promise in extracting useful, quantitative information from very low-count data by reducing local bias while maintaining the lower noise variance of statistical methods. Results from clinical data demonstrate the potential of both techniques.

Model-Based Iterative Reconstruction for Dual-Energy X-Ray CT Using a Joint Quadratic Likelihood Model.

Dual-energy X-ray CT (DECT) has the potential to improve contrast and reduce artifacts as compared to traditional CT. Moreover, by applying model-based iterative reconstruction (MBIR) to dual-energy data, one might also expect to reduce noise and improve resolution. However, the direct implementation of dual-energy MBIR requires the use of a nonlinear forward model, which increases both complexity and computation. Alternatively, simplified forward models have been used which treat the material-decomposed channels separately, but these approaches do not fully account for the statistical dependencies in the channels. In this paper, we present a method for joint dual-energy MBIR (JDE-MBIR), which simplifies the forward model while still accounting for the complete statistical dependency in the material-decomposed sinogram components. The JDE-MBIR approach works by using a quadratic approximation to the polychromatic log-likelihood and a simple but exact nonnegativity constraint in the image domain. We demonstrate that our method is particularly effective when the DECT system uses fast kVp switching, since in this case the model accounts for the inaccuracy of interpolated sinogram entries. Both phantom and clinical results show that the proposed model produces images that compare favorably in quality to previous decomposition-based methods, including FBP and other statistical iterative approaches.

Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer.

Statistical image reconstruction algorithms in X-ray computed tomography (CT) provide improved image quality for reduced dose levels but require substantial computation time. Iterative algorithms that converge in few iterations and that are amenable to massive parallelization are favorable in multiprocessor implementations. The separable quadratic surrogate (SQS) algorithm is desirable as it is simple and updates all voxels simultaneously. However, the standard SQS algorithm requires many iterations to converge. This paper proposes an extension of the SQS algorithm that leads to spatially nonuniform updates. The nonuniform (NU) SQS encourages larger step sizes for the voxels that are expected to change more between the current and the final image, accelerating convergence, while the derivation of NU-SQS guarantees monotonic descent. Ordered subsets (OS) algorithms can also accelerate SQS, provided suitable "subset balance" conditions hold. These conditions can fail in 3-D helical cone-beam CT due to incomplete sampling outside the axial region-of-interest (ROI). This paper proposes a modified OS algorithm that is more stable outside the ROI in helical CT. We use CT scans to demonstrate that the proposed NU-OS-SQS algorithm handles the helical geometry better than the conventional OS methods and "converges" in less than half the time of ordinary OS-SQS.

Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise.

OBJECTIVE: The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. MATERIALS AND METHODS: Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. RESULTS: Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). CONCLUSION: In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.

Dual-energy CT and its potential use for quantitative myocardial CT perfusion.

Application of quantitative myocardial CT perfusion (CTP) for the assessment of coronary artery disease may have a significant effect on patient care as the functional significance of a coronary stenosis can be evaluated through absolute measurement of the downstream myocardial perfusion (MP) both at rest and under exercise or pharmacologic stress. A main challenge of myocardial CTP is beam hardening (BH), arising from the polychromatic nature of x-rays used in CT scanning and the presence of highly attenuating contrast agent in the heart chambers during the CT acquisition. The BH effect induces significant nonuniform shifts in CT numbers which, if uncorrected, can lead to inaccurate assessment of MP. With the recent developments of dual-energy CT (DECT) scanning on clinical scanners, the BH effect on MP measurement could be reduced with the generation of monochromatic images relatively free of BH artifacts from the acquired dual-energy data. Here, we review the different techniques of acquiring dual-energy scans and generating monochromatic images, followed by discussion on the progress of developing a DECT technique with reduced radiation dose for quantitative myocardial CTP.

Comparison of hybrid and pure iterative reconstruction techniques with conventional filtered back projection: dose reduction potential in the abdomen.

PURPOSE: Assess the effect of filtered back projection (FBP) and hybrid (adaptive statistical iterative reconstruction [ASIR]) and pure (model-based iterative reconstruction [MBIR]) iterative reconstructions on abdominal computed tomography (CT) acquired with 75% radiation dose reduction. MATERIALS AND METHODS: In an institutional review board-approved prospective study, 10 patients (mean [standard deviation] age, 60 (8) years; 4 men and 6 women) gave informed consent for acquisition of additional abdominal images on 64-slice multidetector-row CT (GE 750HD, GE Healthcare). Scanning was repeated over a 10-cm scan length at 200 and 50 milliampere second (mA s), with remaining parameters held constant at 120 kilovolt (peak), 0.984:1 pitch, and standard reconstruction kernel. Projection data were deidentified, exported, and reconstructed to obtain 4 data sets (200-mA s FBP, 50-mA s FBP, 50-mA s ASIR, 50-mA s MBIR), which were evaluated by 2 abdominal radiologists for lesions and subjective image quality. Objective noise and noise spectral density were measured for each image series. RESULTS: Among the 10 patients, the maximum weight recorded was 123 kg, with maximum transverse diameter measured as 43.7 cm. Lesion conspicuity at 50-mA s MBIR was better than on 50-mA s FBP and ASIR images (P < 0.01). Image noise was rated as suboptimal on low-dose FBP and ASIR but deemed acceptable in MBIR images. Objective noise with 50-mA s MBIR was 2 to 3 folds lower compared to 50-mA s ASIR, 50-mA s FBP, and 200-mA s FBP (P < 0.0001). Noise spectral density analyses demonstrated that ASIR retains the noise spectrum signature of FBP, whereas MBIR has much lower noise with a more regularized noise spectrum pattern. CONCLUSION: Model-based iterative reconstruction renders acceptable image quality and diagnostic confidence in 50- mA s abdominal CT images, whereas FBP and ASIR images are associated with suboptimal image quality at this radiation dose level.

Fast model-based X-ray CT reconstruction using spatially nonhomogeneous ICD optimization.

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.

A three-dimensional statistical approach to improved image quality for multislice helical CT.

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

A three-dimensional weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT under a circular source trajectory.

The original FDK algorithm proposed for cone beam (CB) image reconstruction under a circular source trajectory has been extensively employed in medical and industrial imaging applications. With increasing cone angle, CB artefacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few 'circular plus' trajectories have been proposed in the past to help the original FDK algorithm to reduce CB artefacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as head imaging, breast imaging, cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artefacts existing in the original FDK algorithm is the inconsistency between conjugate rays that are 180 degrees apart in view angle (namely conjugate ray inconsistency). The conjugate ray inconsistency is pixel dependent, varying dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artefacts that can be avoided if appropriate weighting strategies are exercised. Along with an experimental evaluation and verification, a three-dimensional (3D) weighted axial cone beam filtered backprojection (CB-FBP) algorithm is proposed in this paper for image reconstruction in volumetric CT under a circular source trajectory. Without extra trajectories supplemental to the circular trajectory, the proposed algorithm applies 3D weighting on projection data before 3D backprojection to reduce conjugate ray inconsistency by suppressing the contribution from one of the conjugate rays with a larger cone angle. Furthermore, the 3D weighting is dependent on the distance between the reconstruction plane and the central plane determined by the circular trajectory. The proposed 3D weighted axial CB-FBP algorithm can be implemented in either the native CB geometry or the so-called cone-parallel geometry. By taking the cone-parallel geometry as an example, the experimental evaluation shows that, up to a moderate cone angle corresponding to a detector dimension of 64 x 0.625 mm, the CB artefacts can be substantially suppressed by the proposed algorithm, while advantages of the original FDK algorithm, such as the filtered backprojection algorithm structure, 1D ramp filtering and data manipulation efficiency, are maintained.