Radiation dose reduction in time-resolved CT angiography using highly constrained back projection reconstruction

Basis and current state of computed tomography perfusion imaging: a review

Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

[Low-dose cerebral perfusion CT image restoration using prior image constrained diffusion tensor]

OBJECTIVE

We propose an efficient method to reduce the noise in low-dose cerebral perfusion CT images using prior image constrained diffusion tensor to reduce the radiation dose in brain CT examination.

METHODS

By utilizing the redundant information in cerebral perfusion CT images, we embedded the complementary structure information in prior images into lowdose cerebral perfusion CT image restoration process to suppress the image noise and artifacts.We first calculated the diffusion tensor for the low-dose cerebral perfusion CT image and prior image separately and then constructed a prior image constrained diffusion tensor (PICDT) to incorporate the structure information from the prior image into low-dose image restoration process.

RESULTS

In experiments with the Shepp-Logan phantom, the SSIM value of CBF map obtained by the proposed algorithm was increased by 63% as compared with that of the FBP algorithm.In analysis of the clinical dataset, the SSIM value of CBF map obtained by the proposed algorithm was increased by 45% as compared with that of FBP algorithm.

CONCLUSION

The proposed method can effectively reduce noises and artifacts of low-dose cerebral perfusion CT images while maintaining the structural details to obtain accurate cerebral hemodynamic maps.

Iterative reconstruction for low-dose cerebral perfusion computed tomography using prior image induced diffusion tensor

Cerebral perfusion computed tomography (CPCT) can depict the functional status of cerebral circulation at the tissue level; hence, it has been increasingly used to diagnose patients with cerebrovascular disease. However, there is a significant concern that CPCT scanning protocol could expose patients to excessive radiation doses. Although reducing the x-ray tube current when acquiring CPCT projection data is an effective method for reducing radiation dose, this technique usually results in degraded image quality. To enhance the image quality of low-dose CPCT, we present a prior image induced diffusion tensor (PIDT) for statistical iterative reconstruction, based on the penalized weighted least-squares (PWLS) criterion, which we referred to as PWLS-PIDT, for simplicity. Specifically, PIDT utilizes the geometric features of pre-contrast scanned high-quality CT image as a structure prior for PWLS reconstruction; therefore, the low-dose CPCT images are enhanced while preserving important features in the target image. An effective alternating minimization algorithm is developed to solve the associated objective function in the PWLS-PIDT reconstruction. We conduct qualitative and quantitative studies to evaluate the PWLS-PIDT reconstruction with a digital brain perfusion phantom and patient data. With this method, the noise in the reconstructed CPCT images is more substantially reduced than that of other competing methods, without sacrificing structural details significantly. Furthermore, the CPCT sequential images reconstructed via the PWLS-PIDT method can derive more accurate hemodynamic parameter maps than those of other competing methods.

Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging

We investigated a projection interpolation method for reconstructing dynamic contrast-enhanced (DCE) heart images from undersampled x-ray projections with filtered backprojecton (FBP). This method may facilitate the application of sparse-view dynamic acquisition for ultralow-dose quantitative computed tomography (CT) myocardial perfusion (MP) imaging. We conducted CT perfusion studies on 5 pigs with a standard full-view acquisition protocol (984 projections). We reconstructed DCE heart images with FBP from all and a quarter of the measured projections evenly distributed over 360°. We interpolated the sparse-view (quarter) projections to a full-view setting using a cubic-spline interpolation method before applying FBP to reconstruct the DCE heart images (synthesized full-view). To generate MP maps, we used 3 sets of DCE heart images, and compared mean MP values and biases among the 3 protocols. Compared with synthesized full-view DCE images, sparse-view DCE images were more affected by streak artifacts arising from projection undersampling. Relative to the full-view protocol, mean bias in MP measurement associated with the sparse-view protocol was 10.0 mL/min/100 g (95%CI: −8.9 to 28.9), which was >3 times higher than that associated with the synthesized full-view protocol (3.3 mL/min/100 g, 95% CI: −6.7 to 13.2). The cubic-spline-view interpolation method improved MP measurement from DCE heart images reconstructed from only a quarter of the full projection set. This method can be used with the industry-standard FBP algorithm to reconstruct DCE images of the heart, and it can reduce the radiation dose of a whole-heart quantitative CT MP study to <2 mSv (at 8-cm coverage).

VVBP-Tensor in the FBP Algorithm: Its Properties and Application in Low-Dose CT Reconstruction

For decades, commercial X-ray computed tomography (CT) scanners have been using the filtered backprojection (FBP) algorithm for image reconstruction. However, the desire for lower radiation doses has pushed the FBP algorithm to its limit. Previous studies have made significant efforts to improve the results of FBP through preprocessing the sinogram, modifying the ramp filter, or postprocessing the reconstructed images. In this paper, we focus on analyzing and processing the stacked view-by-view backprojections (named VVBP-Tensor) in the FBP algorithm. A key challenge for our analysis lies in the radial structures in each backprojection slice. To overcome this difficulty, a sorting operation was introduced to the VVBP-Tensor in its z direction (the direction of the projection views). The results show that, after sorting, the tensor contains structures that are similar to those of the object, and structures in different slices of the tensor are correlated. We then analyzed the properties of the VVBP-Tensor, including structural self-similarity, tensor sparsity, and noise statistics. Considering these properties, we have developed an algorithm using the tensor singular value decomposition (named VVBP-tSVD) to denoise the VVBP-Tensor for low-mAs CT imaging. Experiments were conducted using a physical phantom and clinical patient data with different mAs levels. The results demonstrate that the VVBP-tSVD is superior to all competing methods under different reconstruction schemes, including sinogram preprocessing, image postprocessing, and iterative reconstruction. We conclude that the VVBP-Tensor is a suitable processing target for improving the quality of FBP reconstruction, and the proposed VVBP-tSVD is an effective algorithm for noise reduction in low-mAs CT imaging. This preliminary work might provide a heuristic perspective for reviewing and rethinking the FBP algorithm.

Radiation Dose Reduction in CT Myocardial Perfusion Imaging Using SMART-RECON

In this paper, a newly developed statistical model-based image reconstruction [referred to as Simultaneous Multiple Artifacts Reduction in Tomographic RECONstruction (SMART-RECON)] is applied to low dose computer tomography (CT) myocardial perfusion imaging (CT-MPI). This method uses the nuclear norm of the spatial-temporal image matrix of the CT-MPI images as a regularizer, rather than a conventional spatial regularizer that incorporates image smoothness, edge preservation, or spatial sparsity into the reconstruction. In addition to providing the needed noise reduction for low-dose CT-MPI, SMART-RECON provides images with spatial resolution and noise power spectrum (NPS) properties, which are independent of contrast and dose levels. Both numerical simulations and in vivo animal studies were performed to validate the proposed method. In these studies, it was found that: 1) quantitative accuracy of perfusion maps in CT-MPI was well maintained for radiation dose level as low as 10 mAs per image frame, compared with the reference standard of 200 mAs for conventional filtered backprojection; 2) flow-occluded myocardium in the porcine heart was well delineated by SMART-RECON at 10 mAs per frame when compared with model-based image reconstruction using spatial total variation (TV) as the regularizer (referred to as TV-SIR) or spatial-temporal TV (ST-TV-SIR); the CT-MPI results were confirmed with positron-emission tomography imaging; 3) image sharpness in SMART-RECON images was nearly independent of image contrast level and radiation dose level, in stark contrast to TV-SIR and ST-TV-SIR, which displayed a strong dependence on both image contrast and radiation dose levels; and 4) the structure of the dose-normalized NPS for the SMART-RECON method did not depend on dose, while the TV-SIR and ST-TV-SIR NPS structure was dose-dependent.

Low-Dose Dynamic Cerebral Perfusion Computed Tomography Reconstruction via Kronecker-Basis-Representation Tensor Sparsity Regularization

Dynamic cerebral perfusion computed tomography (DCPCT) has the ability to evaluate the hemodynamic information throughout the brain. However, due to multiple 3-D image volume acquisitions protocol, DCPCT scanning imposes high radiation dose on the patients with growing concerns. To address this issue, in this paper, based on the robust principal component analysis (RPCA, or equivalently the low-rank and sparsity decomposition) model and the DCPCT imaging procedure, we propose a new DCPCT image reconstruction algorithm to improve low-dose DCPCT and perfusion maps quality via using a powerful measure, called Kronecker-basis-representation tensor sparsity regularization, for measuring low-rankness extent of a tensor. For simplicity, the first proposed model is termed tensor-based RPCA (T-RPCA). Specifically, the T-RPCA model views the DCPCT sequential images as a mixture of low-rank, sparse, and noise components to describe the maximum temporal coherence of spatial structure among phases in a tensor framework intrinsically. Moreover, the low-rank component corresponds to the "background" part with spatial-temporal correlations, e.g., static anatomical contribution, which is stationary over time about structure, and the sparse component represents the time-varying component with spatial-temporal continuity, e.g., dynamic perfusion enhanced information, which is approximately sparse over time. Furthermore, an improved nonlocal patch-based T-RPCA (NL-T-RPCA) model which describes the 3-D block groups of the "background" in a tensor is also proposed. The NL-T-RPCA model utilizes the intrinsic characteristics underlying the DCPCT images, i.e., nonlocal self-similarity and global correlation. Two efficient algorithms using alternating direction method of multipliers are developed to solve the proposed T-RPCA and NL-T-RPCA models, respectively. Extensive experiments with a digital brain perfusion phantom, preclinical monkey data, and clinical patient data clearly demonstrate that the two proposed models can achieve more gains than the existing popular algorithms in terms of both quantitative and visual quality evaluations from low-dose acquisitions, especially as low as 20 mAs.

Low-dose CT perfusion with projection view sharing

PURPOSE

CT Perfusion (CTP) is a widely used clinical imaging modality. However, CTP typically involves the use of substantial radiation dose (CTDI_vol ≥~200 mGy). The purpose of this study is to present a low-dose CTP technique using a projection view-sharing reconstruction algorithm originally developed for dynamic MRI - "K-space Weighted Image Contrast" (KWIC).

METHODS

The KWIC reconstruction is based on an angle-bisection scheme. In KWIC, a Fourier transform was performed along each projection to form a "k-space"-like CT data space, based on the central-slice theorem. As a projection view-sharing technique, KWIC preserves the spatiotemporal resolution of undersampled CTP data by progressively increasing the number of projection views shared for more distant regions of "k-space". KWIC reconstruction was evaluated on a digital FORBILD head phantom with numerically simulated time-varying objects. The numerically simulated scans were undersampled using the angle-bisection scheme to achieve 50%, 25%, and 12.5% of the original dose (288, 144, and 72 projections, respectively). The area-under-the-curve (AUC), time-to-peak (TTP), and full width half maximum (FWHM) were measured in KWIC recons and compared to fully sampled filtered back projection (FBP) reconstructions. KWIC reconstruction and dose reduction was also implemented for three clinical CTP cases (45 s, 1156 projections per turn, 1 s/turn, CTDI_vol 217 mGy). Quantitative perfusion metrics were computed and compared between KWIC reconstructed CTP data and those of standard FBP reconstruction.

RESULTS

The AUC, TTP, and FWHM in the numerical simzulations were unaffected by the undersampling/dose reduction (down to 12.5% dose) with KWIC reconstruction compared to the fully sampled FBP reconstruction. The normalized root-mean-square-error (NRMSE) of the AUC in the FORBILD head phantom is 0.04, 0.05, and 0.07 for 50%, 25%, and 12.5% KWIC, respectively, as compared to FBP reconstruction. The cerebral blood flow (CBF) and cerebral blood volume had no significant difference between FBP and 50%, 25%, and 12.5% KWIC reconstructions (P > 0.05).

CONCLUSIONS

This study demonstrates that KWIC preserves perfusion metrics for CTP with substantially reduced dose. Clinical implementation will require further investigation into methods of rapid switching of a CT x-ray source.

Optimal dilution of contrast medium for quantitating parenchymal blood volume using a flat-panel detector

Objective Similar to perfusion studies after acute ischemic stroke, measuring cerebral blood volume (CBV) via C-arm computed tomography before and after therapeutic interventions may help gauge subsequent revascularization. We tested serial dilutions of intra-arterial injectable contrast medium (CM) to determine the optimal CM concentration for quantifying parenchymal blood volume by flat-panel detector imaging (FD-PBV). Methods CM was diluted via saline power injector, instituting time delays for FD-PBV studies. A red/green/blue (RGB) color scale was employed to quantify/compare FD-PBV and magnetic resonance-derived CBV (MRCBV). Results Contrast values of right and left common carotid arteries did not differ significantly at CM dilutions of ≥20%. RGB analysis of FD-PBV imaging (relative to MR-CVB), showed CM dilution altered the colors (by 16%), increasing red and decreasing blue ratios. Conclusion Diluting CM to 20% resulted in no laterality differential of FD-PBV imaging, with left/right quantitative ratios approaching 1.1 (optimal for clinical use).

[Sinogram restoration for low-dose cerebral perfusion CT images]

In clinical cerebral perfusion CT examination, repeated scanning the region of interest in the cine mode increases the radiation dose of the patients, while decreasing the radiation dose by lowering the scanning current results in poor image quality and affects the clinical diagnosis. We propose a penalized weighted least-square (PWLS) method for recovering the projection data to improve the quality of low-dose cerebral perfusion CT imaged. This method incorporates the statistical distribution characteristics of brain perfusion CT projection data and uses the statistical properties of the projection data for modeling. The PWLS method was used to recover the data, and the Gauss-Seidel (GS) method was employed for iterative solving. Adaptive weighting is introduced between the original projection data and the projection data after PWLS restoration. The experimental results on the clinical data demonstrated that the PWLS-based sinogram restoration method improved noise reduction and artifact suppression as compared with the conventional noise reduction methods, and better retained the edges and details to generate better cerebral perfusion maps.

Robust low-dose dynamic cerebral perfusion CT image restoration via coupled dictionary learning scheme

Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition. Specifically, in the present CDL scheme, the 2D background information from the average of the baseline time frames of low-dose unenhanced CT images and the 3D enhancement information from normal-dose sequential cerebral PCT images are exploited to train the dictionary atoms respectively. After getting the two trained dictionaries, we couple them to represent the desired PCT images as spatio-temporal prior in objective function construction. Finally, the low-dose dynamic cerebral PCT images are restored by using a general DL image processing. To get a robust solution, the objective function is solved by using a modified dictionary learning based image restoration algorithm. The experimental results on clinical data show that the present method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the state-of-the-art methods.

TENDER: Tensor non-local deconvolution enabled radiation reduction in CT perfusion

Stroke is the leading cause of long-term disability and the second leading cause of mortality in the world, and exerts an enormous burden on the public health. Computed Tomography (CT) remains one of the most widely used imaging modality for acute stroke diagnosis. However when coupled with CT perfusion, the excessive radiation exposure in repetitive imaging to assess treatment response and prognosis has raised significant public concerns regarding its potential hazards to both short- and long-term health outcomes. Tensor total variation has been proposed to reduce the necessary radiation dose in CT perfusion without comprising the image quality by fusing the information of the local anatomical structure with the temporal blood flow model. However the local search in the TTV framework fails to leverage the non-local information in the spatio-temporal data. In this paper, we propose TENDER, an efficient framework of non-local tensor deconvolution to maintain the accuracy of the hemodynamic parameters and the diagnostic reliability in low radiation dose CT perfusion. The tensor total variation is extended using non-local spatio-temporal cubics for regularization, and an efficient algorithm is proposed to reduce the time complexity with speedy similarity computation. Evaluations on clinical data of patients subjects with cerebrovascular disease and normal subjects demonstrate the advantage of non-local tensor deconvolution for reducing radiation dose in CT perfusion.

Mixed Confidence Estimation for Iterative CT Reconstruction

Dynamic (4D) CT imaging is used in a variety of applications, but the two major drawbacks of the technique are its increased radiation dose and longer reconstruction time. Here we present a statistical analysis of our previously proposed Mixed Confidence Estimation (MCE) method that addresses both these issues. This method, where framed iterative reconstruction is only performed on the dynamic regions of each frame while static regions are fixed across frames to a composite image, was proposed to reduce computation time. In this work, we generalize the previous method to describe any application where a portion of the image is known with higher confidence (static, composite, lower-frequency content, etc.) and a portion of the image is known with lower confidence (dynamic, targeted, etc). We show that by splitting the image space into higher and lower confidence components, MCE can lower the estimator variance in both regions compared to conventional reconstruction. We present a theoretical argument for this reduction in estimator variance and verify this argument with proof-of-principle simulations. We also propose a fast approximation of the variance of images reconstructed with MCE and confirm that this approximation is accurate compared to analytic calculations of and multi-realization image variance. This MCE method requires less computation time and provides reduced image variance for imaging scenarios where portions of the image are known with more certainty than others allowing for potentially reduced radiation dose and/or improved dynamic imaging.

Prospective evaluation of prior image constrained compressed sensing (PICCS) algorithm in abdominal CT: a comparison of reduced dose with standard dose imaging

PURPOSE

To prospectively study CT dose reduction using the "prior image constrained compressed sensing" (PICCS) reconstruction technique.

METHODS

Immediately following routine standard dose (SD) abdominal MDCT, 50 patients (mean age, 57.7 years; mean BMI, 28.8) underwent a second reduced dose (RD) scan (targeted dose reduction, 70%-90%). DLP, CTDIvol, and SSDE were compared. Several reconstruction algorithms (FBP, ASIR, and PICCS) were applied to the RD series. SD images with FBP served as reference standard. Two blinded readers evaluated each series for subjective image quality and focal lesion detection.

RESULTS

Mean DLP, CTDIvol, and SSDE for RD series were 140.3 mGy cm (median 79.4), 3.7 mGy (median 1.8), and 4.2 mGy (median 2.3) compared with 493.7 mGy cm (median 345.8), 12.9 mGy (median 7.9 mGy), and 14.6 mGy (median 10.1) for SD series, respectively. Mean effective patient diameter was 30.1 cm (median 30), which translates to a mean SSDE reduction of 72% (P < 0.001). RD-PICCS image quality score was 2.8 ± 0.5, improved over the RD-FBP (1.7 ± 0.7) and RD-ASIR (1.9 ± 0.8) (P < 0.001), but lower than SD (3.5 ± 0.5) (P < 0.001). Readers detected 81% (184/228) of focal lesions on RD-PICCS series, vs. 67% (153/228) and 65% (149/228) for RD-FBP and RD-ASIR, respectively. Mean image noise was significantly reduced on RD-PICCS series (13.9 HU) compared with RD-FBP (57.2) and RD-ASIR (44.1) (P < 0.001).

CONCLUSION

PICCS allows for marked dose reduction at abdominal CT with improved image quality and diagnostic performance over reduced dose FBP and ASIR. Further study is needed to determine indication-specific dose reduction levels that preserve acceptable diagnostic accuracy relative to higher dose protocols.

Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. "Time is brain" is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.

Tensor total-variation regularized deconvolution kegularlzea ueconvolution for efficient low-dose CT perfusion

Acute brain diseases such as acute stroke and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. 'Time is brain' is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation will lead to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. We propose a novel efficient framework using tensor total-variation (TTV) regularization to achieve both high efficiency and accuracy in deconvolution for low-dose CTP. The method reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with estimation error reduced by 40%. It also corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), at both normal and reduced sampling rate. An efficient computational algorithm is proposed to find the solution with fast convergence.

Pushing CT and MR imaging to the molecular level for studying the "omics": current challenges and advancements

During the past decade, medical imaging has made the transition from anatomical imaging to functional and even molecular imaging. Such transition provides a great opportunity to begin the integration of imaging data and various levels of biological data. In particular, the integration of imaging data and multiomics data such as genomics, metabolomics, proteomics, and pharmacogenomics may open new avenues for predictive, preventive, and personalized medicine. However, to promote imaging-omics integration, the practical challenge of imaging techniques should be addressed. In this paper, we describe key challenges in two imaging techniques: computed tomography (CT) and magnetic resonance imaging (MRI) and then review existing technological advancements. Despite the fact that CT and MRI have different principles of image formation, both imaging techniques can provide high-resolution anatomical images while playing a more and more important role in providing molecular information. Such imaging techniques that enable single modality to image both the detailed anatomy and function of tissues and organs of the body will be beneficial in the imaging-omics field.

Mixed Confidence Estimation for Iterative CT Reconstruction

We present a statistical analysis of our previously proposed Constrain-Static Target-Kinetic algorithm for 4D CT reconstruction. This method, where framed iterative reconstruction is only performed on the dynamic regions of each frame, while static regions are fixed across frames to a composite image, was proposed to reduce computation time. In this work, we generalize the previous method to describe any application where a portion of the image is known with higher confidence (static, composite, lower-frequency content, etc.) and a portion of the image is known with lower confidence (dynamic, targeted, etc). We show that by splitting the image space into higher and lower confidence components, CSTK can lower the estimator variance in both regions compared to conventional reconstruction. We present a theoretical argument for this reduction in estimator variance and verify this argument with proof-of-principle simulations. This method allows for reduced computation time and improved image quality for imaging scenarios where portions of the image are known with more certainty than others.

Reduction of image noise in low tube current dynamic CT myocardial perfusion imaging using HYPR processing: a time-attenuation curve analysis

PURPOSE

This study describes a HighlY constrained backPRojection (HYPR) image processing method for the reduction of image noise in low tube current time-resolved CT myocardial perfusion scans. The effect of this method on myocardial time-attenuation curve noise and fidelity is evaluated in an animal model, using varying levels of tube current.

METHODS

CT perfusion scans of four healthy pigs (42-59 kg) were acquired at 500, 250, 100, 50, 25, and 10 mA on a 64-slice scanner (4 cm axial coverage, 120 kV, 0.4 s∕rotation, 50 s scan duration). For each scan a sequence of ECG-gated images centered on 75% R-R was reconstructed using short-scan filtered back projection (FBP). HYPR processing was applied to the scans acquired at less than 500 mA using parameters designed to maintain the voxel noise level in the 500-mA FBP images. The processing method generates a series of composite images by averaging over a sliding time window and then multiplies the composite images by weighting images to restore temporal fidelity to the image sequence. HYPR voxel noise relative to FBP noise was measured in AHA myocardial segment numbers 1, 5, 6, and 7 at each mA. To quantify the agreement between HYPR and FBP time-attenuation curves (TACs), Bland-Altman analysis was performed on TACs measured in full myocardial segments. The relative degree of TAC fluctuation in smaller subvolumes was quantified by calculating the root mean square deviation of a TAC about the gamma variate curve fit to the TAC data.

RESULTS

HYPR image sequences were produced using 2, 7, and 20 beat composite windows for the 250, 100, and 50 mA scans, respectively. At 25 and 10 mA, all available beats were used in the composite (41-60; average 50). A 7-voxel-wide 3D cubic filter kernel was used to form weighting images. The average ratio of HYPR voxel noise to 500-mA FBP voxel noise was 1.06, 1.10, 0.97, 1.11, and 2.15 for HYPR scans at 250, 100, 50, 25, and 10 mA. The average limits-of-agreement between HYPR and FBP TAC values measured 0.02+∕-0.91, 0.04+∕-1.92, 0.19+∕-1.59, 1.13+∕-4.22, and 1.07+∕-6.37 HU (mean difference +∕-1.96 SD). The HYPR image subvolume that yielded a fixed level of TAC fluctuations was smaller, on average, than the FBP subvolume determined at the same mA.

CONCLUSIONS

HYPR processing is a feasible method for generating low noise myocardial perfusion data from a low-mA time-resolved CT myocardial perfusion scan. The method is applicable to current clinical scanners and uses conventional image reconstructions as input data.

C-arm CT measurement of cerebral blood volume and cerebral blood flow using a novel high-speed acquisition and a single intravenous contrast injection

BACKGROUND AND PURPOSE

Assessment of perfusion parameters is important in the selection of patients who are most likely to benefit from revascularization after an acute ischemic stroke. The aim of this study was to evaluate the feasibility of measuring cerebral perfusion parameters with the use of a novel high-speed C-arm CT acquisition in conjunction with a single intravenous injection of contrast.

MATERIALS AND METHODS

Seven canines had experimentally induced focal ischemic regions confirmed by CT perfusion imaging. Four hours after ischemic injury creation, each subject underwent cerebral perfusion measurements with the use of standard perfusion CT, immediately followed by the use of C-arm CT. Cerebral blood flow and cerebral blood volume maps measured by C-arm CT were quantitatively and qualitatively compared with those measured by perfusion CT for 6 of the 7 canine subjects.

RESULTS

Results from independent observer evaluations of perfusion CT and C-arm perfusion maps show strong agreement between observers for identification of ischemic lesion location. Significant percentage agreement between observers for lesion detection and identification of perfusion mismatch between CBV and CBF maps indicate that the maps for both perfusion CT and C-arm are easy to interpret. Quantitative region of interest-based evaluation showed a strong correlation between the perfusion CT and C-arm CBV and CBF maps (R(2) = 0.68 and 0.85). C-arm measurements for both CBV and CBF were consistently overestimated when compared with perfusion CT.

CONCLUSIONS

Qualitative and quantitative measurements of CBF and CBV with the use of a C-arm CT acquisition and a single intravenous injection of contrast agent are feasible. Future improvements in flat detector technology and software algorithms probably will enable more accurate quantitative perfusion measurements with the use of C-arm CT.

Projection reconstruction magnetic particle imaging

We acquire the first experimental 3-D tomographic images with magnetic particle imaging (MPI) using projection reconstruction methodology, which is similar to algorithms employed in X-ray computed tomography. The primary advantage of projection reconstruction methods is an order of magnitude increase in signal-to-noise ratio (SNR) due to averaging. We first derive the point spread function, resolution, number of projections required, and the SNR gain in projection reconstruction MPI. We then design and construct the first scanner capable of gathering the necessary data for nonaliased projection reconstruction and experimentally verify our mathematical predictions. We demonstrate that filtered backprojection in MPI is experimentally feasible and illustrate the SNR and resolution improvements with projection reconstruction. Finally, we show that MPI is capable of producing three dimensional imaging volumes in both phantoms and postmortem mice.

Characterization of statistical prior image constrained compressed sensing. I. Applications to time-resolved contrast-enhanced CT

PURPOSE

Prior image constrained compressed sensing (PICCS) is an image reconstruction framework that takes advantage of a prior image to improve the image quality of CT reconstructions. An interesting question that remains to be investigated is whether or not the introduction of a statistical model of the photon detection in the PICCS reconstruction framework can improve the performance of the algorithm when dealing with high noise projection datasets. The goal of the research presented in this paper is to characterize the noise properties of images reconstructed using PICCS with and without statistical modeling. This paper investigates these properties in the clinical context of time-resolved contrast-enhanced CT.

METHODS

Both numerical phantom studies and an Institutional Review Board approved human subject study were used in this research. The conventional filtered backprojection (FBP), and PICCS with and without the statistical model were applied to each dataset. The prior image used in PICCS was generated by averaging over FBP reconstructions from different time frames of the time-resolved CT exam, thus reducing the noise level. Numerical studies were used to evaluate if the noise characteristics are altered for varying levels of noise, as well as for different object shapes. The dataset acquired in vivo was used to verify that the conclusions reached from numerical studies translate adequately to a clinical case. The results were analyzed using a variety of qualitative and quantitative metrics such as the universal image quality index, spatial maps of the noise standard deviations, the noise uniformity, the noise power spectrum, and the model-observer detectability.

RESULTS

The noise characteristics of PICCS were shown to depend on the noise level contained in the data, the level of eccentricity of the object, and whether or not the statistical model was applied. Most differences in the characteristics were observed in the regime of low incident x-ray fluence. No substantial difference was observed between PICCS with and without statistics in the high fluence domain. Objects with a semi-major axis ratio below 0.85 were more accurately reconstructed with lower noise using the statistical implementation. Above that range, for mostly circular objects, the PICCS implementation without the statistical model yielded more accurate images and a lower noise level. At all levels of eccentricity, the noise spatial distribution was more uniform and the model-observer detectability was greater for PICCS with the statistical model. The human subject study was consistent with the results obtained using numerical simulations.

CONCLUSIONS

For mildly eccentric objects in the low noise regime, PICCS without the noise model yielded equal or better noise level and image quality than the statistical formulation. However, in a vast majority of cases, images reconstructed using statistical PICCS have a noise power spectrum that facilitated the detection of model lesions. The inclusion of a statistical model in the PICCS framework does not always result in improved noise characteristics.

Noise spatial nonuniformity and the impact of statistical image reconstruction in CT myocardial perfusion imaging

PURPOSE

To achieve high temporal resolution in CT myocardial perfusion imaging (MPI), images are often reconstructed using filtered backprojection (FBP) algorithms from data acquired within a short-scan angular range. However, the variation in the central angle from one time frame to the next in gated short scans has been shown to create detrimental partial scan artifacts when performing quantitative MPI measurements. This study has two main purposes. (1) To demonstrate the existence of a distinct detrimental effect in short-scan FBP, i.e., the introduction of a nonuniform spatial image noise distribution; this nonuniformity can lead to unexpectedly high image noise and streaking artifacts, which may affect CT MPI quantification. (2) To demonstrate that statistical image reconstruction (SIR) algorithms can be a potential solution to address the nonuniform spatial noise distribution problem and can also lead to radiation dose reduction in the context of CT MPI.

METHODS

Projection datasets from a numerically simulated perfusion phantom and an in vivo animal myocardial perfusion CT scan were used in this study. In the numerical phantom, multiple realizations of Poisson noise were added to projection data at each time frame to investigate the spatial distribution of noise. Images from all datasets were reconstructed using both FBP and SIR reconstruction algorithms. To quantify the spatial distribution of noise, the mean and standard deviation were measured in several regions of interest (ROIs) and analyzed across time frames. In the in vivo study, two low-dose scans at tube currents of 25 and 50 mA were reconstructed using FBP and SIR. Quantitative perfusion metrics, namely, the normalized upslope (NUS), myocardial blood volume (MBV), and first moment transit time (FMT), were measured for two ROIs and compared to reference values obtained from a high-dose scan performed at 500 mA.

RESULTS

Images reconstructed using FBP showed a highly nonuniform spatial distribution of noise. This spatial nonuniformity led to large fluctuations in the temporal direction. In the numerical phantom study, the level of noise was shown to vary by as much as 87% within a given image, and as much as 110% between different time frames for a ROI far from isocenter. The spatially nonuniform noise pattern was shown to correlate with the source trajectory and the object structure. In contrast, images reconstructed using SIR showed a highly uniform spatial distribution of noise, leading to smaller unexpected noise fluctuations in the temporal direction when a short scan angular range was used. In the numerical phantom study, the noise varied by less than 37% within a given image, and by less than 20% between different time frames. Also, the noise standard deviation in SIR images was on average half of that of FBP images. In the in vivo studies, the deviation observed between quantitative perfusion metrics measured from low-dose scans and high-dose scans was mitigated when SIR was used instead of FBP to reconstruct images.

CONCLUSIONS

(1) Images reconstructed using FBP suffered from nonuniform spatial noise levels. This nonuniformity is another manifestation of the detrimental effects caused by short-scan reconstruction in CT MPI. (2) Images reconstructed using SIR had a much lower and more uniform noise level and thus can be used as a potential solution to address the FBP nonuniformity. (3) Given the improvement in the accuracy of the perfusion metrics when using SIR, it may be desirable to use a statistical reconstruction framework to perform low-dose dynamic CT MPI.

[Dynamic contrast-enhanced computed tomography. Tracer kinetics and radiation hygienic principles]

Technical innovations in multislice computed tomography (CT) allow for larger volume coverage in ever shorter scan times. This progress has stimulated the clinical application of dynamic contrast-enhanced (DCE) CT techniques, which offer the possibility to noninvasively characterize tissue microcirculation in terms of well-defined physiological quantities. This educational review imparts to radiologists the essential physiological terms and definitions as well as the basic tracer kinetic concepts required for the analysis of DCE-CT data. In particular, four different approaches are presented and exemplified by the analysis of representative DCE data: the steepest-gradient method, model-free algebraic deconvolution in combination with the indicator-dilution theory, two-compartment modelling and the so-called adiabatic approximation to the homogeneity model. Even though DCE-CT offers substantial methodological and practical advantages as compared to DCE-MRI (magnetic resonance imaging), there are also two serious and interconnected shortcomings: the low contrast enhancement in relation to the noise level and the high exposure of patients to ionizing radiation. These limiting aspects are considered in detail from a radiation hygienic point of view, emphasizing the basic principles of justification and optimization. Clinically established as well as potential future applications of DCE-CT will be presented in a subsequent paper.

Simulation tools for two-dimensional experiments in x-ray computed tomography using the FORBILD head phantom

Mathematical phantoms are essential for the development and early stage evaluation of image reconstruction algorithms in x-ray computed tomography (CT). This note offers tools for computer simulations using a two-dimensional (2D) phantom that models the central axial slice through the FORBILD head phantom. Introduced in 1999, in response to a need for a more robust test, the FORBILD head phantom is now seen by many as the gold standard. However, the simple Shepp-Logan phantom is still heavily used by researchers working on 2D image reconstruction. Universal acceptance of the FORBILD head phantom may have been prevented by its significantly higher complexity: software that allows computer simulations with the Shepp-Logan phantom is not readily applicable to the FORBILD head phantom. The tools offered here address this problem. They are designed for use with Matlab®, as well as open-source variants, such as FreeMat and Octave, which are all widely used in both academia and industry. To get started, the interested user can simply copy and paste the codes from this PDF document into Matlab® M-files.

Noise reduction in spectral CT: reducing dose and breaking the trade-off between image noise and energy bin selection

PURPOSE

Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins.

METHODS

Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i) numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system.

RESULTS

The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented by the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images.

CONCLUSIONS

Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.

Newer CT applications and their alternatives: what is appropriate in children?

Innovations in image acquisition and reconstruction technologies have greatly expanded the range of CT applications available in the routine clinical setting. CT images of sub-millimeter resolution can now be acquired of entire body regions in a few seconds or even sub-second time, allowing depiction of fine anatomical detail uncompromised by motion artifact. With sophisticated visualization software, image data can be processed into multiplanar, volume-rendered, cine and other formats to better display anatomical abnormalities and facilitate newer applications such as CT angiography, enterography, urography, tracheobronchography and cardiac CT. Newer applications including dual-energy material decomposition CT are furthering the transition of CT from a purely morphological to a combined anatomical, functional and metabolic imaging technique. These newer applications have largely been pioneered in adult populations, and heightened concern of the risk of carcinogenesis from ionizing radiation tempers dissemination of their use in children. Similar information can often be gleaned from alternative imaging modalities without ionizing radiation exposure, such as MRI and US, and what is most appropriate in children will depend on relative diagnostic efficacy, cost, availability and local expertise.

Nonconvex prior image constrained compressed sensing (NCPICCS): theory and simulations on perfusion CT

PURPOSE

To present and evaluate a new image reconstruction method for dynamic CT based on a nonconvex prior image constrained compressed sensing (NCPICCS) algorithm. The authors systematically compared the undersampling potential, functional information recovery, and solution convergence speed of four compressed sensing (CS) based image reconstruction methods using perfusion CT data: Standard l1-based CS, nonconvex CS (NCCS), and l1-based and nonconvex CS, including an additional constraint based on a prior image (PICCS and NCPICCS, respectively).

METHODS

The Shepp-Logan phantom was modified such that its uppermost ellipses changed attenuation through time, simulating both an arterial input function (AIF) and a homogeneous tissue perfusion region. Data were simulated with and without Poisson noise added to the projection data and subsequently reconstructed with all four CS-based methods at four levels of undersampling: 20, 12, 6, and 4 projections. Root mean squared (RMS) error of reconstructed images and recovered time attenuation curves (TACs) were assessed as well as convergence speed. The performance of both PICCS and NCPICCS methods were also evaluated using a kidney perfusion animal experiment data set.

RESULTS

All four CS-based methods were able to reconstruct the phantoms with 20 projections, with similar results on the RMS error of the recovered TACs. NCCS allowed accurate reconstructions with as few as 12 projections, PICCS with as few as six projections, and NCPICCS with as few as four projections. These results were consistent for noise-free and noisy data. NCPICCS required the fewest iterations to converge across all simulation conditions, followed by PICCS, NCCS, and then CS. On animal data, at the lowest level of undersampling tested (16 projections), the image quality of NCPICCS was better than PICCS with fewer streaking artifacts, while the TAC accuracy on the selected region of interest was comparable.

CONCLUSIONS

The authors have presented a novel method for image reconstruction using highly undersampled dynamic CT data. The NCPICCS method takes advantage of the information provided by a prior image, as in PICCS, but employs a more general nonconvex sparsity measure [such as the l(p)-norm (0 < p < or = 1)] rather than the conventional convex l1-norm. Despite the lack of guarantees of a globally optimal solution, the proposed nonconvex extension of PICCS consistently allowed for image reconstruction from fewer samples than the analogous l1-based PICCS method. Both nonconvex sparsity measures as well as prior image information (when available) significantly reduced the number of iterations required for convergence, potentially providing computational advantages for practical implementation of CS-based image reconstruction techniques.

Reduced image noise at low-dose multidetector CT of the abdomen with prior image constrained compressed sensing algorithm

PURPOSE

To assess the effect of prior image constrained compressed sensing (PICCS) on noise reduction and image quality at low-dose computed tomography (CT).

MATERIALS AND METHODS

This HIPAA-compliant institutional review board-approved retrospective study was performed by using DICOM CT colonography data sets obtained in 20 adult patients. Informed consent was waived. Low-dose CT colonography was performed with 64-detector CT by using the standard protocol with mean effective dose per series of 3.06 mSv (range, 1.4-7.7 mSv). PICCS was applied to standard filtered back-projection (FBP) series. For FBP and PICCS series, mean and standard deviation (SD) of attenuation were obtained with 100-mm(2) circular region of interest (ROI) at six sites (240 soft-tissue, colonic gas, and subcutaneous fat measurements). Two abdominal radiologists reviewed two- and three-dimensional CT colonography displays and graded image quality with a five-point scale. Phantom studies were performed to compare spatial resolution and image quality between FBP and PICCS. Mean image noise and image quality scores were calculated and compared for clinical and phantom data sets. Bland-Altman, generalized estimating equation regression model, and Student t tests were used to obtain limits of agreement and to compare noise ratios and subjective image quality.

RESULTS

Mean SD of attenuation (image noise) for ROIs was 38.0 for FBP and 12.2 for PICCS, corresponding to a noise-reduction factor of 3.1 (P < .001). Average noise reduction was 3.3 for soft tissue, 2.8 for air, and 3.0 for fat attenuation. Attenuation did not substantially change between FBP and PICCS images. Average two-dimensional image quality was 2.45 for FBP and 3.4 for PICCS (P < .001). Average three-dimensional image quality at three sites in the colon was 3.5 for FBP and 3.7 for PICCS (P = .34). Phantom data sets revealed no loss of spatial resolution in a line phantom and reduced noise in a liver tumor phantom when PICCS was compared with FBP.

CONCLUSION

Application of PICCS to standard FBP low-dose multidetector CT abdominal images results in substantial noise reduction and improved image quality.

Noise reduction and image quality improvement of low dose and ultra low dose brain perfusion CT by HYPR-LR processing

PURPOSE

To evaluate image quality and signal characteristics of brain perfusion CT (BPCT) obtained by low-dose (LD) and ultra-low-dose (ULD) protocols with and without post-processing by highly constrained back-projection (HYPR)-local reconstruction (LR) technique.

METHODS AND MATERIALS

Simultaneous BPCTs were acquired in 8 patients on a dual-source-CT by applying LD (80 kV, 200 mAs, 14×1.2 mm) on tube A and ULD (80 kV, 30 mAs, 14×1.2 mm) on tube B. Image data from both tubes was reconstructed with identical parameters and post-processed using the HYPR-LR. Correlation coefficients between mean and maximum (MAX) attenuation values within corresponding ROIs, area under attenuation curve (AUC), and signal to noise ratio (SNR) of brain parenchyma were assessed. Subjective image quality was assessed on a 5-point scale by two blinded observers (1: excellent, 5: non-diagnostic).

RESULTS

Radiation dose of ULD was more than six times lower compared to LD. SNR was improved by HYPR: ULD vs. ULD+HYPR: 1.9±0.3 vs. 8.4±1.7, LD vs. LD+HYPR: 5.0±0.7 vs. 13.4±2.4 (both p<0.0001). There was a good correlation between the original datasets and the HYPR-LR post-processed datasets: r = 0.848 for ULD and ULD+HYPR and r = 0.933 for LD and LD+HYPR (p<0.0001 for both). The mean values of the HYPR-LR post-processed ULD dataset correlated better with the standard LD dataset (r = 0.672) than unprocessed ULD (r = 0.542), but both correlations were significant (p<0.0001). There was no significant difference in AUC or MAX. Image quality was rated excellent (1.3) in LD+HYPR and non-diagnostic (5.0) in ULD. LD and ULD+HYPR images had moderate image quality (3.3 and 2.7).

CONCLUSION

SNR and image quality of ULD-BPCT can be improved to a level similar to LD-BPCT when using HYPR-LR without distorting attenuation measurements. This can be used to substantially reduce radiation dose. Alternatively, LD images can be improved by HYPR-LR to higher diagnostic quality.

Fast whole-brain 4D contrast-enhanced MR angiography with velocity encoding using undersampled radial acquisition and highly constrained projection reconstruction: image-quality assessment in volunteer subjects

We report on the image quality obtained by using fast contrast-enhanced whole-brain 4D radial MRA with 0.75-second temporal resolution, isotropic submillimeter spatial resolution, and velocity encoding (HYPRFlow). Images generated by HYPR-LR by using the velocity-encoded data as the constraining image were of diagnostic quality. In addition, we demonstrate that measurements of shear stress within the middle cerebral artery can be derived from the high-resolution 3D velocity data.

Synchrotron radiation computed laminography for polymer composite failure studies

Synchrotron radiation computed laminography is applied to the three-dimensional micro-imaging of damage in large polymer composite plates with high spatial resolution. The influence of different experimental conditions is studied with respect to measurement time optimization, dose minimization and reduction of artefacts in the reconstructed images. Failures like delaminations, transverse ply cracks and splits are observed under in situ loads. The propagation of up to 2 mm-long cracks is non-destructively followed in situ and investigated in detail. By phase retrieval using a single detector distance, the failures can be easily visualized in three dimensions.

Radiation dose reduction in computed tomography: techniques and future perspective

Despite universal consensus that computed tomography (CT) overwhelmingly benefits patients when used for appropriate indications, concerns have been raised regarding the potential risk of cancer induction from CT due to the exponentially increased use of CT in medicine. Keeping radiation dose as low as reasonably achievable, consistent with the diagnostic task, remains the most important strategy for decreasing this potential risk. This article summarizes the general technical strategies that are commonly used for radiation dose management in CT. Dose-management strategies for pediatric CT, cardiac CT, dual-energy CT, CT perfusion and interventional CT are specifically discussed, and future perspectives on CT dose reduction are presented.