System Optimization and Iterative Image Reconstruction in Photoacoustic Computed Tomography for Breast Imaging

This paper is about 3D image reconstruction from a limited set of computed tomography (CT) projections. We focus on configurations with very limited angle of view and on applications in which the image to be reconstructed is composed of one or several localized objects laying in a known background. We propose an original method based on the detection of the localized object voxels and on a sparse modeling of the image. Reconstruction is done by computing the maximum a posteriori estimator of the image parameters. To implement image reconstruction, we adopt a multigrid strategy in which coarse-to-fine resoluted images are successively reconstructed. This strategy provides detection of localized object voxels as well as accurate initial solutions at each resolution level. Each optimization stage is carried out by using an iterative deterministic descent algorithm. We propose a convergent single site update algorithm which consists of successive constrained optimizations with respect to one voxel at a time. We show the performance of the multigrid method on simulated data corresponding to a set of limited angle cone-beam projections of a synthetic image. The results are accurate, while both memory storage and numerical time of computation are dramatically reduced compared with the monogrid method.

In recent several years, motivated by the need to reduce radiation doses in CT exams, all of the major CT manufacturers have commercialized different iterative image reconstruction techniques and these innovative techniques were used in clinical routines with increasing popularity. However, due to the intrinsic nonlinearity of these new techniques, the well accepted quantitative image quality assessment metrics such as spatial resolution and contrast to noise ratio are not sufficient to provide the needed quantitative metrics for assessing image quality and for guiding the CT scan protocol optimization. This symposium aims at providing a thorough update to AAPM community on what we have understood in the past for linear CT imaging system, what are the new challenges and opportunities offered by the nonlinear iterative image reconstruction, and what would be the future directions in quantitative image quality assessment that the AAPM community can work together to address the challenges and to adapt the nonlinear image reconstruction methods to routine clinical practice to improve patient care. Three invited speakers will lead the discussions in this symposium.Dr. Ke Li from the University of Wisconsin‐Madison will be presenting the new challenges introduced in model‐based iterative reconstruction (MBIR) method with a focus on how the nonlinear nature of the MBIR reconstruction poses challenges in quantitative image quality metrics such as spatial resolution, noise power spectrum, and the limitations of CNR in protocol optimization which eventually leads to the need of task‐based detectability index as the metric.Dr. Shuai Leng from Mayo Clinic will then present how the CNR metric is insufficient for nonlinear reconstruction algorithms, and how task based model observers can be used to more accurately quantify image quality, and to optimize imaging system and scanning protocols for nonlinear iterative image reconstruction algorithms based on specific imaging task. Practical considerations regarding the application of task based image quality metrics will also be discussed.Dr. Ehsan Samei from Duke University will start the symposium by summarizing a to‐be‐released AAPM Task Group report (TG 233: Performance Evaluation of Computed Tomography Systems). He will cover what has been understood and accepted for linear CT imaging system, what are the new challenges in CT image quality assessment introduced by the nonlinear iterative reconstruction (IR) techniques, and summarize both taskneutral and task‐based metrologies developed in TG 233 to partially address these challenges, and foresee additional challenges in the future. Learning Objectives: Understand the challenges in quantitative image quality assessment introduced by nonlinear iterative reconstruction techniques Understand the challenges and methods in noise performance assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in spatial resolution assessment for nonlinear iterative reconstruction techniques Understand the challenges and methods in in task‐based observer performance assessment for nonlinear iterative reconstruction techniquesFunding support received from NIH and DOD; Funding support received from GE Healthcare; Funding support received from Siemens AX; Patent royalties received from GE Healthcare; S. Leng, R01 EB071095; U01 EB017185; E. Samei, Research grant, Siemens; Research grant, GE; K. Li, Funding from NIH, DOD and AAPM

Objective To investigate the effect of split-bolus spectral computed tomography(CT) on the portal venography and radiation dose. Methods The prospective study was adopted. The clinical data of 119 patients who underwent spectral CT at China-Japan Union Hopital from September 2014 to March 2015 were collected. Patients were randomly divided into the portal venography with split-bolus spectral CT single-phase enhanced scan group and portal vein multi-phase scan group by random sequence method. In the portal venography with split-bolus spectral CT single-phase enhanced scan group, the spectral CT was used with the method of split-bolus single phase imaging, and in the portal vein multi-phase scan group, standard spiral CT was used to perform three-phase scan. Two observers evaluated CT portal venography subjectively and objectively, measured CT values, contrast to noise ratio (CNR), signal noise ratio (SNR), and calculated radiation dose. Observed indices included (1) choice of optimal monochromatic images. (2) CT values of portal veins, measurement of CNR and SNR. (3) Subjective scoring of portal venography quality. (4) Comparison of radiation dose. Measurement data with normal distribution were presented as±s, comparison between groups was analyzed by independent sample t test. Comparison of count data was analyzed by chi-square test. Results The 113 patients were screened for eligibility, including 59 in the portal venography with split-bolus spectral CT single-phase enhanced scan group and 54 in the portal vein multi-phase scan group. (1) Choice of optimal monochromatic images: optimal monochromatic images were abstracted at 60 keV from spectral CT portal venography. (2) CT values of portal veins and measurement of CNR and SNR: the CT values of intrahepatic portal vein, extrahepatic portal vein and branches of portal vein were (319±44)HU, (328±53)HU, (294±45)HU in the reconstructed images at the energy level of 60 keV in the portal venography with split-bolus spectral CT single-phase enhanced scan group and (213±41)HU, (228±49)HU, (210±41)HU in the portal vein multi-phase scan group, with significant differences between the 2 groups(t=8.04, 6.34, 6.82, P 0.05). The SNR of intrahepatic portal vein, extrahepatic portal vein and branches of portal vein were 31±6, 29±6, 27±6 in the portal venography with split-bolus spectral CT single-phase enhanced scan group and 29±7, 28±9, 26±6 in the portal vein multi-phase scan group, respectively, with no significant differences (t=-0.688, 0.615, 0.600, P>0.05). (3) Subjective scoring of portal venography quality: the subjective score of image quality of portal venography was 14.3±1.0 in the portal venography with split-bolus spectral CT single-phase enhanced scan group and 12.5±1.8 in the portal vein multi-phase scan group, with a significant difference (t=12.43, P<0.05). (4) Comparison of radiation dose: the radiation dose was (8.1±1.1)mSv of patients in the portal venography with split-bolus spectral CT single-phase enhanced scan group and (17.4±7.5)mSv in the portal vein multi-phase scan group, with a significant difference (t=24.14, P<0.05). Conclusion Spectral CT portal vein imaging combined with split-bolus protocol can achieve better manifestations of portal vein and its branches, and reduce radiation dose in the scanning process. Key words: Liver diseases; Portal vein; Spilt-bolus; Tomography, X-ray computed; Spectral imaging; Radiation dose

With the development of computing technology, there is an increasing interest in iterative CT image reconstruction. To study iterative reconstruction algorithms, we have developed a software simulator IterCT in VC++ on PC and made it publicly available on the Internet. In the latest version of the simulator, we have implemented four representative iterative image reconstruction algorithms, which are expectation maximization (EM) method and its ordered-subset version (OSEM), the simultaneous algebraic reconstruction technique (SART) and its ordered-subset version (OSSART). The filtered backprojection method is included as the benchmark.

Iterative image reconstruction for positron emission tomography (PET) can improve image quality by using spatial regularization that penalizes image intensity difference between neighboring pixels. Themost commonly used quadratic penalty often over-smoothes edges and small objects in reconstructed images. Non-quadratic penalties can preserve edges but may introduce piece-wise constant blocky artifacts. The results are also sensitive to the hyper-parameter that controls the shape of the penalty function. This paper presents a robust regularization for iterative image reconstruction by using neighborhood patches instead of individual pixels in formulating the non-quadratic penalties. An optimization transfer algorithm is developed for the corresponding optimization problem. Computer simulations show that the proposed patch-based regularization can achieve better contrast recovery for small objects compared with quadratic regularization, and is more robust to the hyper-parameter than the conventional pixel-based non-quadratic regularization.

This work is aimed at evaluating the image reconstruction techniques used in single photon emission computed tomography (SPECT/CT) imaging. The reconstruction techniques considered in this work are: the ordered subset expectation maximization (OSEM) and the Filtered Back Projection (FBP). An anthropomorphic torso phantom with lung and liver inserts was injected with 99mTc. The liver and the background were given 0.2 MBq/ml and 0.07 MBq/ml respectively. The lungs inserts were filled with polystyrene beads for humanization. A mimicked 30 ml tumor (27 mm) in the liver insert was given 2 MBq/ml sequential SPECT/CT (step and shot, 10 mins over 180° per head) and CT (120 kVp, 80 mAs) projections were acquired using dual head SPECT/16 slice CT system. The projections were reconstructed using OSEM (10 subsets for 2–10 numbers of iterations, Butterworth post filtering, 10 orders and 0.48 frequencies) and FBP (using Butterworth post filtering at 10 order and cutoff frequencies of 0.38–0.58, 0.05 gaps). Image J software was used for the image analysis. Statistical t-test tested the mean gray values of the tumor and its background. The OSEM and FBP reconstructed images were evaluated from their contrast-to-noise ratio (CNR). The optimal parameter was selected based on 3–5 CNR detection range set by the Rose criterion. The t-test showed the background and the tumors mean values to be statistically significant (P ≪ 0.05). The CNR values for FBP images were all below the detection range, maximum value at the cutoff frequency 0.48 (1.69). In OSEM images at cutoff frequency 0.48, the CNR values were within the detection range for 2–8 numbers of iteration with maximum value (4.89) at 4 numbers of iteration. OSEM was found to be the best for tumor detection and administered activity can be reduced at 4 OSEM’s of iterations (40 MLEM iterations) for patient safety. Keywords: Anthropomorphic torso phantom, 99mTc, SPECT, iterative reconstruction, CNR, P-Value, liver Cite this Article F. Usman, R. Zainon, A. Saidu, A. Bala. Evaluation of image reconstruction techniques in Single photon emission computed tomography (SPECT/CT) imaging. Research & Reviews: Journal of Physics. 2016; 5(2): 13–22p.

Objective To explore the application value of automatic tube current modulation combined with iterative reconstruction algorithm (idose4) in low-dose computed tomography pulmonary angiography (CTPA). Methods A total of 80 patients with CTPA collected continuously were randomly divided into control group (n=40, 80 kV, 180 mAs) and experimental group (n=40, 80 kV, automatic tube current modulation). The data sets of control group were reconstructed with filtered-back-projection algorithm (group A), and the data sets of experimental group were reconstructed with filtered-back-projection algorithm (group B) and iterative reconstruction algorithm (group C). Subjective score, mean CT values of pulmonary artery, image noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were recorded. Effective dose (E) was compared. Results The E of experimental group was decreased by 36.8% compared to control group (t=-3.998, P 0.05). There was statistically significant difference for the SNR, CNR and image noise of 3 groups (F=10.411, 7.630, 13.021, P 0.05). Conclusions Using automatic tube current modulation combined with iterative reconstruction algorithm in low-dose CTPA could meet the requirements of image quality, and reduce the radiation dose to a certain extent. Key words: Computed tomography pulmonary angiography; Radiation dose; Iterative reconstruction; Automatic tube current modulation

Turn down the noise—a blinded evaluation of iterative image reconstruction in radiation therapy computed tomography simulation

BackgroundHybrid imaging combines nuclear medicine imaging such as single photon emission computed tomography (SPECT) or positron emission tomography (PET) with computed tomography (CT). Through this hybrid design, scanned patients accumulate radiation exposure from both applications. Imaging modalities have been the subject of long-term optimization efforts, focusing on diagnostic applications. It was the aim of this study to investigate the influence of an iterative CT image reconstruction algorithm (ASIR) on the image quality of the low-dose CT images.Methodology/Principal FindingsExaminations were performed with a SPECT-CT scanner with standardized CT and SPECT-phantom geometries and CT protocols with systematically reduced X-ray tube currents. Analyses included image quality with respect to photon flux. Results were compared to the standard FBP reconstructed images. The general impact of the CT-based attenuation maps used during SPECT reconstruction was examined for two SPECT phantoms. Using ASIR for image reconstructions, image noise was reduced compared to FBP reconstructions for the same X-ray tube current. The Hounsfield unit (HU) values reconstructed by ASIR were correlated to the FBP HU values(R2 ≥ 0.88) and the contrast-to-noise ratio (CNR) was improved by ASIR. However, for a phantom with increased attenuation, the HU values shifted for low X-ray tube currents I ≤ 60 mA (p ≤ 0.04). In addition, the shift of the HU values was observed within the attenuation corrected SPECT images for very low X-ray tube currents (I ≤ 20 mA, p ≤ 0.001).Conclusion/SignificanceIn general, the decrease in X-ray tube current up to 30 mA in combination with ASIR led to a reduction of CT-related radiation exposure without a significant decrease in image quality.

BackgroundIterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).PurposeTo examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.Material and MethodsThe LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.ResultsThe image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).ConclusionAn ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

Since the birth of charge coupled devices (CCD) and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors, pixel pitch of digital image sensors has been continuously shrinking to meet the resolution and size requirements of the cameras. However, shrinking pixels reduces the maximum number of photons a sensor can hold, a phenomenon broadly known as the full-well capacity limit. The drop in full-well capacity causes drop in signal-to-noise ratio and dynamic range.The Quanta Image Sensor (QIS) is a class of solid-state image sensors proposed by Eric Fossum in 2005 as a potential solution for the limited full-well capacity problem. QIS is envisioned to be the next generation image sensor after CCD and CMOS since it enables sub-diffraction-limit pixels without the inherited problems of pixel shrinking. Equipped with a massive number of detectors that have single-photon sensitivity, the sensor counts the incoming photons and triggers a binary response “1” if the photon count exceeds a threshold, or “0” otherwise. To acquire an image, the sensor oversamples the space and time to generate a sequence of binary bit maps. Because of this binary sensing mechanism, the full-well capacity, signal-to-noise ratio and the dynamic range can all be improved using an appropriate image reconstruction algorithm. The contribution of this thesis is to address three image processing problems in QIS: 1) Image reconstruction, 2) Threshold design and 3) Color filter array design.Part 1 of the thesis focuses on reconstructing the latent grayscale image from the QIS binary measurements. Image reconstruction is a necessary step for QIS because the raw binary measurements are not images. Previous methods in the literature use iterative algorithms which are computationally expensive. By modeling the QIS binary measurements as quantized Poisson random variables, a new non-iterative image reconstruction method based on the Transform-Denoise framework is proposed. Experimental results show that the new method produces better quality images while requiring less computing time.Part 2 of the thesis considers the threshold design problem of a QIS. A spatially-varying threshold can significantly improve the reconstruction quality and the dynamic range. However, no known method of how to achieve this can be found in the literature. The theoretical analysis of this part shows that the optimal threshold should match with the underlying pixel intensity. In addition, the analysis proves the existence of a set of thresholds around the optimal threshold that give asymptotically unbiased reconstructions. The asymptotic unbiasedness has a phase transition behavior. A new threshold update scheme based on this idea is proposed. Experimentally, the new method can provide good estimates of the thresholds with less computing budget compared to existing methods.Part 3 of the thesis extends QIS capabilities to color imaging by studying how a color filter array should be designed. Because of the small pixel pitch of QIS, crosstalk between neighboring pixels is inevitable and should be considered when designing the color filter arrays. However, optimizing the light efficiency while suppressing aliasing and crosstalk in a color filter array are conflicting tasks. A new optimization framework is proposed to solve the problem. The new framework unifies several mainstream design criteria while offering generality and flexibility. Extensive experimental comparisons demonstrate the effectiveness of the framework.

An iterative image reconstruction is expected as a powerful dose reduction method in clinical CT examinations. Although many studies have been made on the iterative reconstruction, its clinical use in CT routine examinations is still challenging. An iterative reconstruction is capable of denoising images in low dose CT scans. However, its long time processing prevents clinical work flows in routine examinations. The one of the solutions is a few iterations with high-performance computing.

In electrical impedance tomography, currents are applied to the body through electrodes that are attached to the surface and the corresponding surface voltages are measured. Based on these boundary measurements, the internal admittivity distribution of the body can be reconstructed. In order to improve the image quality it is necessary and useful to apply physiologically meaningful prior information into the image reconstruction. Such prior information usually can be obtained from other sources. For example, information on the object's boundary shape and internal structure can be obtained from computed tomography and magnetic resonance imaging scan. However, this type of prior information may change from time to time and from person to person. As these changes are limited anatomically and physiologically, the prior information including the possible changes can be presented in a number of variational forms. The aim of this paper is to find which form of prior information is more compatible for a specific imaged object at the time of imaging. This paper proposes a new method for selecting the most appropriate form of prior information, through the procedure of iterative image reconstruction by using the information obtained from boundary measurements. The method is based on the principle that incompatible prior information causes errors which are able to affect the image reconstruction's convergence behavior. In this method, according to the various forms of prior information available, several image reconstruction configurations are designed. Then, through monitoring the convergence behavior in an iterative image reconstruction, the configuration with compatible prior information can be found among those different configurations. As an example, the prior information regarding the imaged object's boundary shape and internal structure was studied by computer simulation. Results were shown and discussed.

Objective To study the application of the intelligent optimum tube voltage (Care kV) combined with automatic tube current (Care Dose 4D) technique in dual source CT scanning for the abdomen. Methods 180 adult patients underwent whole abdominal CT examination from February 2016 to March 2016 of were selected as the retrospective research objects. On the basis of reference mAs of the image quality the patients were divided into three groups using completely randomized grouping method with 60 patients in each group, such as 250 mAs group, 200 mAs group, and 150 mAs group. The other scanning parameters and image reconstruction parameters were the same. The liver parenchyma CT values and image noise(SD) at the second hepatic portal level were measured. The signal-to-noise ratios (SNR) in the three groups were compared as well as contrast to noise ratio (CNR), noise (SD), the average volume CT dose index (CTDIvol), dose length product (DLP) and effective dose(E). Results The kV and mAs values in the plain scan, arterial phase scan and portal phase scan in the three groups of patients had statistically significant differences(F=35.25, P 0.05). In 250 mAs group and 150 mAs group, the SNR and the noise of plain scan and arterial phase images had no statistically significant difference (F=1.98, P>0.05), and SNR of portal phase images and CNR of three phases had statistically significant difference (F=27.64, P 0.05), and the SNR and CNR of portal phase images had significant difference (F=19.63, P< 0.05). Conclusions For the combined use of Care kV and Care dose 4D, it is feasible to select appropriate reference mAs in reducing radiation dose without sacrificing imaging quality. Key words: Tomography, X-ray computed; Intelligent optimum tube voltage technique(Care kV); Radiation dose

Electrical brain imaging (EBI) refers to a set of techniques that exploit either the spontaneous electrical activity of the central nervous system, as in electroencephalographic (EEG) source reconstruction, or make use of external current injections, as in electrical impedance tomography (EIT) , to image the structure or function of the brain. When compared to other brain imaging methods used in research or in the clinical setting, such as computed tomography (CT), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), EIT and EEG source localization instrumentation offer the advantages of portability, low cost, high temporal resolution [ms] and quick setup. The downsides are a low spatial resolution [cm], high computational cost of the image reconstruction process and high sensitivity to imperfections of the electrical model of the head.&#13;\nIn this work, a new special purpose reconstruction algorithm for EIT is presented and validated wth experimental measurements performed on a cylindrical phantom and on a simulated human head. The algorithm focuses on the quick detection of compact conductivity contrasts in imperfectly known in 3D domains. The performance of the proposed algorithm is then compared to the one of a benchmark reconstruction method in the EIT field, Tikhonov regularized reconstruction, with stroke detection and classification as a case study. Moreover, the possible application of EIT imaging to the detection of epileptic foci with intracranial deep electrodes (stereoelectroencephalography or SEEG) is explored.&#13;\nFinally, EEG source reconstruction algorithms are implemented on a heterogeneous multi-CPU and multi-GPU computing system to significantly reduce the reconstruction time.