Homer H. Pien

Abdominal CT: Comparison of Adaptive Statistical Iterative and Filtered Back Projection Reconstruction Techniques

PURPOSEnTo compare image quality and lesion conspicuity on abdominal computed tomographic (CT) images acquired with different x-ray tube current-time products (50-200 mAs) and reconstructed with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) techniques.nnnMATERIALS AND METHODSnTwenty-two patients (mean age, 60.1 years ± 7.3 [standard deviation]; age range, 52.8-67.4 years; mean weight, 78.9 kg ± 18.3; 12 men, 10 women) gave informed consent for this prospective institutional review board-approved and HIPAA-compliant study, which involved the acquisition of four additional image series at multidetector CT. Images were acquired at different tube current-time products (200, 150, 100, and 50 mAs) and encompassed an abdominal lesion over a 10-cm scan length. Images were reconstructed separately with FBP and with three levels of ASIR-FBP blending. Two radiologists reviewed FBP and ASIR images for image quality in a blinded and randomized manner. Volume CT dose index (CTDI(vol)), dose-length product, patient weight, objective noise, and CT numbers were recorded. Data were analyzed by using analysis of variance and the Wilcoxon signed rank test.nnnRESULTSnCTDI(vol) values were 16.8, 12.6, 8.4, and 4.2 mGy for 200, 150, 100, and 50 mAs, respectively (P < .001). Subjective noise was graded as below average at 150 mAs and average at 100 and 50 mAs for ASIR images, as compared with FBP images, on which noise was graded as average at 150 mAs, above average at 100 mAs, and unacceptable at 50 mAs. A substantial blotchy image appearance was noted in four of 22 image series acquired at 4.2 mGy with 70% ASIR. Lesion conspicuity was significantly better at 4.2 mGy on ASIR than on FBP images (observed P < .044), and overall diagnostic confidence changed from unacceptable on FBP to acceptable on ASIR images.nnnCONCLUSIONnASIR lowers noise and improves diagnostic confidence in and conspicuity of subtle abdominal lesions at 8.4 mGy when images are reconstructed with 30% ASIR blending and at 4.2 mGy in patients weighing 90 kg or less when images are reconstructed with 50% or 70% ASIR blending.

Adaptive Statistical Iterative Reconstruction Technique for Radiation Dose Reduction in Chest CT: A Pilot Study

PURPOSEnTo compare lesion detection and image quality of chest computed tomographic (CT) images acquired at various tube current-time products (40-150 mAs) and reconstructed with adaptive statistical iterative reconstruction (ASIR) or filtered back projection (FBP).nnnMATERIALS AND METHODSnIn this Institutional Review Board-approved HIPAA-compliant study, CT data from 23 patients (mean age, 63 years ± 7.3 [standard deviation]; 10 men, 13 women) were acquired at varying tube current-time products (40, 75, 110, and 150 mAs) on a 64-row multidetector CT scanner with 10-cm scan length. All patients gave informed consent. Data sets were reconstructed at 30%, 50%, and 70% ASIR-FBP blending. Two thoracic radiologists assessed image noise, visibility of small structures, lesion conspicuity, and diagnostic confidence. Objective noise and CT number were measured in the thoracic aorta. CT dose index volume, dose-length product, weight, and transverse diameter were recorded. Data were analyzed by using analysis of variance and the Wilcoxon signed rank test.nnnRESULTSnFBP had unacceptable noise at 40 and 75 mAs in 17 and five patients, respectively, whereas ASIR had acceptable noise at 40-150 mAs. Objective noise with 30%, 50%, and 70% ASIR blending (11.8 ± 3.8, 9.6 ± 3.1, and 7.5 ± 2.6, respectively) was lower than that with FBP (15.8 ± 4.8) (P < .0001). No lesions were missed on FBP or ASIR images. Lesion conspicuity was graded as well seen on both FBP and ASIR images (P < .05). Mild pixilated blotchy texture was noticed with 70% blended ASIR images.nnnCONCLUSIONnAcceptable image quality can be obtained for chest CT images acquired at 40 mAs by using ASIR without any substantial artifacts affecting diagnostic confidence.nnnSUPPLEMENTAL MATERIALnhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11101450/-/DC1.

Multi GPU implementation of iterative tomographic reconstruction algorithms

Although iterative reconstruction techniques (IRTs) have been shown to produce images of superior quality over conventional filtered back projection (FBP) based algorithms, the use of IRT in a clinical setting has been hampered by the significant computational demands of these algorithms. In this paper we present results of our efforts to overcome this hurdle by exploiting the combined computational power of multiple graphical processing units (GPUs). We have implemented forward and backward projection steps of reconstruction on an NVIDIA Tesla S870 hardware using CUDA. We have been able to accelerate forward projection by 71x and backward projection by 137x. We generate these results with no perceptible difference in image quality between the GPU and serial CPU implementations. This work illustrates the power of using commercial off-the-shelf relatively low-cost GPUs, potentially allowing IRT tomographic image reconstruction to be run in near real time, lowering the barrier to entry of IRT, and enabling deployment in the clinic.

High-resolution biosensor spectral peak shift estimation

In this paper, we present a maximum likelihood (ML) approach to high-resolution estimation of the shifts of a spectral signal. This spectral signal arises in application of optically based resonant biosensors, where high resolution in the estimation of signal shift is synonymous with high sensitivity to biological interactions. For the particular sensor of interest, the underlying signal is nonuniformly sampled and exhibits Poisson amplitude statistics. Shift estimation accuracies orders of magnitude finer than the sample spacing are sought. The new ML-based formulation leads to a solution approach different from typical resonance shift estimation methods based on polynomial fitting and peak (or null) estimation and tracking.

A decomposition-based CT reconstruction formulation for reducing blooming artifacts

Cardiac computed tomography represents an important advancement in the ability to assess coronary vessels. The accuracy of these non-invasive imaging studies is limited, however, by the presence of calcium, since calcium blooming artifacts lead to an over-estimation of the degree of luminal narrowing. To address this problem, we have developed a unified decomposition-based iterative reconstruction formulation, where different penalty functions are imposed on dense objects (i.e. calcium) and soft tissue. The result is a quantifiable reduction in blooming artifacts without the introduction of new distortions away from the blooming observed in other methods. Results are shown for simulations, phantoms, ex vivo, and in vivo studies.

Accurate model-based high resolution cardiac image reconstruction in dual source CT

Cardiac imaging represents one of the most challenging imaging problems, requiring high spatial and temporal resolutions along with good tissue contrast. One of the newest clinical cardiac CT scanners incorporates two source-detector pairs in order to improve the temporal resolution by two-fold. To achieve the highest spatial resolution, reconstructions using iterative techniques may be desired. Yet the complexity of the dual-source geometry makes accurate system modeling a challenge. In this paper, we present a model-based iterative reconstruction algorithm for the dual-source CT. We demonstrate, using a total variation formulation, the results of our reconstructions. To accelerate the processing and enhance the quality of the result, we also incorporate a simplified detector response function in the forward projector. A segment of heavily-calcified coronary artery is used to demonstrate the spatial and temporal resolution of this approach with the dual-source system.

Low-dose X-ray CT reconstruction based on joint sinogram smoothing and learned dictionary-based representation

In this paper we propose two novel image reconstruction methods for low-dose X-ray CT data. Both approaches are based on anisotropic sinogram smoothing coupled with sparse local image representation with respect to a learned over complete dictionary. The redundant dictionary is learned from normal-dose CT training images and encodes artifact-free image behavior. The methods differ in the details of how the redundant dictionary information is included. Efficient solution approaches to the new formulations are provided. Comparative results on simulated low-dose imagery are given. Our approach is new in how it applies learning-based dictionary techniques to low-dose CT reconstruction, in its use of high quality training data in dictionary generation, and in its incorporation of anisotropic sinogram constraints together with the dictionary-based representation.

A learning-based approach to explosives detection using Multi-Energy X-Ray Computed Tomography

In this paper we consider the task of classifying materials into explosives and non-explosives according to features obtainable from Multi-Energy X-ray Computed Tomography (MECT) measurements. The discriminative ability of MECT derives from its sensitivity to the attenuation versus energy curves of materials. Thus we focus on the fundamental information available in these curves and features extracted from them. We study the dimensionality and span of these curves for a set of explosive and non-explosive compounds and show that their space is larger than two-dimensional, as is typically assumed. In addition, we build support vector machine classifiers with different feature sets and find superior classification performance when using more than two features and when using features different than the standard photoelectric and Compton coefficients. These results suggest the potential for improved detection performance relative to conventional dual-energy X-ray systems.

Joint cardiac and respiratory motion correction and super-resolution reconstruction in coronary PET/CT

Coronary artery disease is marked by the development of chronic inflammation in the vascular arteries that is associated with coronary plaques. Positron emission tomography (PET) is capable of detecting inflammation through activated macrophage uptake of FDG. Unfortunately, in conventional cardiac PET, respiratory and cardiac motion during acquisition leads to severe blurring of the resulting images and an effective spatial resolution inadequate for plaque detection and localization. In this paper, we extend our previous image-domain approach to a fully integrated, data-domain method that starts from the observed projection data and performs a model-based inversion and motion correction of all the data to create a high-resolution focused cardiac image. We term the new approach Data-domain Cardiac Shape Tracking and Adjustment for Respiration or D-CSTAR. In contrast to existing image domain methods the image reconstruction and motion correction steps are not separated. Unlike current data domain methods both cardiac and respiratory motions are compensated for. In D-CSTAR, cardiac motion parameters are estimated from X-ray CT images acquired in a breath-hold state. This cardiac motion information is incorporated in a unified PET reconstruction functional which jointly estimates and corrects for respiratory motion, compensates for phase aligned cardiac motion, and super-resolves the image. The technique is presented and applied to simulated cardiac PET/CT data corresponding to the XCAT phantom with both cardiac and respiratory cycles. The results show a marked qualitative and quantitative improvement when compared to conventional and existing PET methods.

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain

This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems.