Homer Pien

Reducing abdominal CT radiation dose with adaptive statistical iterative reconstruction technique.

Purpose:To assess radiation dose reduction for abdominal computed tomography (CT) examinations with adaptive statistical iterative reconstruction (ASIR) technique. Materials and Methods:With institutional review board approval, retrospective review of weight adapted abdominal CT exams were performed in 156 consecutive patients with ASIR and in 66 patients with filtered back projection (FBP) on a 64-slice MDCT. Patients were categorized into 3 groups of <60 kg (n = 42), 61 to 90 kg (n = 100), and ≥91 kg (n = 80) for weight-based adjustment of automatic exposure control technique. Remaining scan parameters were held constant at 1.375:1 pitch, 120 kVp, 55 mm table feed per rotation, 5 mm section thickness. Two radiologists reviewed all CT examinations for image noise and diagnostic acceptability. CT dose index volume, and dose length product were recorded. Image noise and transverse abdominal diameter were measured in all patients. Data were analyzed using analysis of variance. Results:ASIR allowed for an overall average decrease of 25.1% in CT dose index volume compared with the FBP technique (ASIR, 11.9 ± 3.6 mGy; FBP, 15.9 ± 4.3 mGy) (P < 0.0001). In each of the 3 weight categories, CT examinations reconstructed with ASIR technique were associated with significantly lower radiation dose compared with FBP technique (P < 0.0001). There was also significantly less objective image noise with ASIR (6.9 ± 2.2) than with FBP (9.5 ± 2.0) (P < 0.0001). For the subjective analysis, all ASIR and FBP reconstructed abdominal CTs had optimal or less noise. However, 9% of FBP and 3.8% of ASIR reconstructed CT examinations were diagnostically unacceptable because of the presence of artifacts. Use of ASIR reconstruction kernel results in a blotchy pixilated appearance in 39% of CT sans which however, was mild and did not affect the diagnostic acceptability of images. The critical reproduction of visually sharp anatomic structures was preserved in all but one ASIR 40% reconstructed CT examination. Conclusion:ASIR technique allows radiation dose reduction for abdominal CT examinations whereas improving image noise compared with the FBP technique.

Silicon micromachining to tissue engineer branched vascular channels for liver fabrication

To date, many approaches to engineering new tissue have emerged and they have all relied on vascularization from the host to provide permanent engraftment and mass transfer of oxygen and nutrients. Although this approach has been useful in many tissues, it has not been as successful in thick, complex tissues, particularly those comprising the large vital organs such as the liver, kidney, and heart. In this study, we report preliminary results using micromachining technologies on silicon and Pyrex surfaces to generate complete vascular systems that may be integrated with engineered tissue before implantation. Using standard photolithography techniques, trench patterns reminiscent of branched architecture of vascular and capillary networks were etched onto silicon and Pyrex surfaces to serve as templates. Hepatocytes and endothelial cells were cultured and subsequently lifted as single-cell monolayers from these two-dimensional molds. Both cell types were viable and proliferative on these surfaces. In addition, hepatocytes maintained albumin production. The lifted monolayers were then folded into compact three-dimensional tissues. Thus, with the use microfabrication technology in tissue engineering, it now seems feasible to consider lifting endothelial cells as branched vascular networks from two-dimensional templates that may ultimately be combined with layers of parenchymal tissue, such as hepatocytes, to form three-dimensional conformations of living vascularized tissue for implantation.

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.

A label-free optical technique for detecting small molecule interactions

A novel approach for the label-free detection of molecular interactions is presented in which a colorimetric resonant grating is used as a surface binding platform. The grating, when illuminated with white light, is designed to reflect only a single wavelength. When molecules are attached to the surface, the reflected wavelength (color) is shifted due to the change of the optical path of light that is coupled into the grating. By linking receptor molecules to the grating surface, complementary binding molecules can be detected without the use of any kind of fluorescent probe or radioactive label. The detection technique is capable of detecting the addition and removal of small molecules as they interact with receptor molecules on the sensor surface or enzymes in the solution surrounding the sensor. Two assays are presented to exemplify the detection of small molecule interactions with the biosensor. First, an avidin receptor layer is used to detect 244 Da biotin binding. Second, a protease assay is performed in which a 136 Da p-nitroanilide (pNA) moeity is cleaved from an immobilized substrate. Because the sensor structure can be embedded in the plastic surfaces of microtiter plates or the glass surfaces of microarray slides, it is expected that this technology will be most useful in applications where large numbers of biomolecular interactions are measured in parallel, particularly when molecular labels will alter or inhibit the functionality of the molecules under study. Screening of pharmaceutical compound libraries with protein targets, and microarray screening of protein-protein interactions for proteomics are examples of applications that require the sensitivity and throughput afforded by this approach.

Sinogram-affirmed iterative reconstruction of low-dose chest CT: effect on image quality and radiation dose.

OBJECTIVE. The purpose of this study is to compare sinogram-affirmed iterative reconstruction (SAFIRE) and filtered back projection (FBP) reconstruction of chest CT acquired with 65% radiation dose reduction. MATERIALS AND METHODS. In this prospective study involving 24 patients (11 women and 13 men; mean [± SD] age, 66 ± 10 years), two scan series were acquired using 100 and 40 Quality Reference mAs over a 10-cm scan length in the chest with a 128-MDCT scanner. The 40 Quality Reference mAs CT projection data were reconstructed with FBP and four settings of SAFIRE (S1, S2, S3, and S4). Six image datasets (FBP with 100 and 40 Quality Reference mAs, and S1, S2, S3, S4 with 40 Quality Reference mAs) were displayed on a DICOM-compliant 55-inch 2-megapixel monitor for blinded evaluation by two thoracic radiologists for number and location of lesions, lesion size, lesion margins, visibility of small structures and fissures, and diagnostic confidence. Objective noise and CT values were measured in thoracic aorta for each image series, and the noise power spectrum was assessed. Data were analyzed with analysis of variance and Wilcoxon signed rank tests. RESULTS. All 186 lesions were seen on 40 Quality Reference mAs SAFIRE images. Diagnostic confidence on SAFIRE images was higher than that for FBP images. Except for the minor blotchy appearance on SAFIRE settings S3 and S4, no significant artifacts were noted. Objective noise with 40 Quality Reference mAs S1 images (21.1 ± 6.1 SD of HU) was significantly lower than that for 40 Quality Reference mAs FBP images (28.5 ± 8.1 SD of HU) (p < 0.001). Noise power spectra were identical for SAFIRE and FBP with progressive noise reduction with higher iteration SAFIRE settings. CONCLUSION. Iterative reconstruction (SAFIRE) allows reducing the radiation exposure by approximately 65% without losing diagnostic information in chest CT.

Calcified Plaque: Measurement of Area at Thin-Section Flat-Panel CT and 64-Section Multidetector CT and Comparison with Histopathologic Findings

The purpose of this study was to assess the blooming artifacts in ex vivo coronary arteries at multidetector computed tomography (CT) and flat-panel-volume CT by comparing measured areas of calcified plaque with respect to the reference standard of histopathologic findings. Three ex vivo hearts were scanned with multidetector CT and flat-panel-volume CT after institutional review board approval. The area of calcified plaque was measured at histopathologic examination, multidetector CT, and flat-panel-volume CT. The plaque area was overestimated at multidetector CT by 400% (4.61/1.15) on average, and the predicted difference between the measurements was significant (3.46 mm(2), P = .018). The average overestimation of plaque area at flat-panel-volume CT was twofold (214% [2.18/1.02]), and the predicted difference was smaller (1.16 mm(2), P = .08). The extent of the blooming artifact in visualizing calcified coronary plaque is reduced by using flat-panel-volume CT.

Functional MRI activity characterization using response time shift estimates from curve evolution

Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brains response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.

A Spatio-Temporal Deconvolution Method to Improve Perfusion CT Quantification

Perfusion imaging is a useful adjunct to anatomic imaging in numerous diagnostic and therapy-monitoring settings. One approach to perfusion imaging is to assume a convolution relationship between a local arterial input function and the tissue enhancement profile of the region of interest via a ¿residue function¿ and subsequently solve for this residue function. This ill-posed problem is generally solved using singular-value decomposition based approaches, and the hemodynamic parameters are solved for each voxel independently. In this paper, we present a formulation which incorporates both spatial and temporal correlations, and show through simulations that this new formulation yields higher accuracy and greater robustness with respect to image noise. We also show using rectal cancer tumor images that this new formulation results in better segregation of normal and cancerous voxels.

A projection-driven pre-correction technique for iterative reconstruction of helical cone-beam cardiac CT images

Modern CT systems have advanced at a dramatic rate. Algebraic iterative reconstruction techniques have shown promising and desirable image characteristics, but are seldom used due to their high computational cost for complete reconstruction of large volumetric datasets. In many cases, however, interest in high resolution reconstructions is restricted to smaller regions of interest within the complete volume. In this paper we present an implementation of a simple and practical method to produce iterative reconstructions of reduced-sized ROI from 3D helical tomographic data. We use the observation that the conventional filtered back-projection reconstruction is generally of high quality throughout the entire volume to predict the contributions to ROI-related projections arising from volumes outside the ROI. These predictions are then used to pre-correct the data to produce a tomographic inversion problem of substantially reduced size and memory demands. Our work expands on those of other researchers who have observed similar potential computational gains by exploiting FBP results. We demonstrate our approach using cardiac CT cone-beam imaging, illustrating our results with both ex vivo and in vivo multi-cycle EKG-gated examples.

A new deconvolution approach to perfusion imaging exploiting spatial correlation

The parts of the human body affected by a disease do not only undergo structural changes but also demonstrate significant physiological (functional) abnormalities. An important parameter that reveals the functional state of tissue is the flow of blood per unit tissue volume or perfusion, which can be obtained using dynamic imaging methods. One mathematical approach widely used for estimating perfusion from dynamic imaging data is based on a convolutional tissue-flow model. In these approaches, deconvolution of the observed data is necessary to obtain the important physiological parameters within a voxel. Although several alternatives have been proposed for deconvolution, all of them treat neighboring voxels independently and do not exploit the spatial correlation between voxels or the temporal correlation within a voxel over time. These simplistic approaches result in a noisy perfusion map with poorly defined region boundaries. In this paper, we propose a novel perfusion estimation method which incorporates spatial as well as temporal correlation into the deconvolution process. Performance of our method is compared to standard methods using independent voxel processing. Both simulated and real data experiments illustrate the potential of our method.