Back-projection Reconstruction Technique Research Articles

Image fusion for neutron tomography of nuclear fuel

Image fusion, the process of combining different images together, can be useful to create a more complete picture. In this work, image fusion is applied to neutron tomography of nuclear fuel with the goal of enhancing the information obtained about the fuel. Different reconstruction methods, such as Feldkamp, Davis and Kress filtered back projection and Simultaneous Reconstruction Technique, were combined to enhance image quality. This methodology was shown to reduce noise and ring artifacts without sacrificing sharp edges, allowing for a more accurate representation of sample geometry. Technique enhancements and future applications for the neutron imaging community are also discussed.

A novel QA phantom based on scintillating fiber ribbons with implementation of 2D dose tomography for small-field radiotherapy.

To develop a novel instrument for real-time quality assurance (QA) procedures in radiotherapy. The system implements a scintillation-based phantom and associated signal acquisition and processing modules and aims to monitor two-dimensional (2D) dose distributions of small fields. For the proposed phantom, we have designed and realized a prototype implementing six high-resolution tissue-equivalent scintillating fiber ribbons stacked with in-plane 30° rotated orientations from each other. Each ribbon output is coupled to a silicon photodiode linear array (with an element pitch of 400μm) to detect scintillating signal, which represents the projected irradiation profile perpendicular to the ribbon's orientation. For the system providing six acquired projected dose profiles at different orientations, we have developed a two-step signal processing method to perform 2D dose reconstruction. The first step is to determine irradiation field geometry parameters using a tomographic geometry approach, and the second one is to perform specific penumbra estimation. The QA system prototype has been tested on a Novalis TrueBeam STX with a 6-MV photon beam for small elliptic fields defined by 5- and 10-mm cone collimators and for 10×10- and 20×10-mm2 rectangular fields defined by the micro-multileaf collimator. Gamma index analysis using EBT3 films as reference has been carried out with tight 2%-dose-difference (DD)/700-μm-distance-to-agreement (DTA) as well as 1%-DD/1-mm-DTA criteria for evaluating the system performances. The testing also includes an evaluation of the proposed two-step field reconstruction method in comparison with two conventional methods: filtered back projection (FBP) and simultaneous iterative reconstruction technique (SIRT). The reconstructed 2D dose distributions have gamma index pass rates higher than 95% for all the tested configurations as compared with EBT3 film measurements with both 2%-DD/700-μm-DTA and 1%-DD/1-mm criteria. 2D global gamma analysis shows that the two-step and FBP radiation field reconstruction methods systematically outperform the SIRT approach. Moreover, higher gamma index success rates are obtained with the two-step method than with FBP in the case of the fields defined with the stereotactic cones. The proposed small-field QA system makes a use of six water-equivalent scintillating detectors (fiber ribbons) to acquire dose distribution. The developed two-step signal processing method performs tomographic 2D dose reconstruction. A system prototype has been built and tested using hospital facilities with small rectangular and elliptic fields. Testing results show 2D reconstructed dose distributions with high accuracy and resolution. Such a system could potentially be an alternative approach to film dosimetry for small-field QA, which is still widely used as reference in clinical practice.

Exterior computed tomography image reconstruction based on anisotropic relative total variation in polar coordinates.

In computed tomography (CT) image reconstruction problems, exterior CT is an important application in industrial non-destructive testing (NDT). Different from the limited-angle problem that misses part of the rotation angle, the rotation angle of the exterior problem is complete, but for each rotation angle, the projection data through the central region of the object cannot be collected, so that the exterior CT problem is ill-posed inverse problem. The results of traditional reconstruction methods like filtered back-projection (FBP) and simultaneous algebra reconstruction technique (SART) have artifacts along the radial direction edges for exterior CT reconstruction. In this study, we propose and test an anisotropic relative total variation in polar coordinates (P-ARTV) model for addressing the exterior CT problem. Since relative total variation (RTV) can effectively distinguish edges from noises, and P-ARTV with different weights in radial and tangential directions can effectively enhance radial edges, a two-step iteration algorithm was developed to solve the P-ARTV model in this study. The fidelity term and the regularization term are solved in Cartesian and polar coordinate systems, respectively. Numerical experiments show that our new model yields better performance than the existing state-of-the-art algorithms.

Anisotropic structure property based image reconstruction method for limited-angle computed tomography.

Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.

Deep learning-based image reconstruction for few-view computed tomography

Few-view computed tomography (CT) is able to provide the internal information of an object for diagnosis, while reducing radiation dose and decreasing side effects. In spite of these benefits, few-view CT images with conventional reconstruction algorithms are suffer from structural distortion and artifacts due to insufficient projections. In this study, a deep learning-based network was proposed for reconstructing few-view CT images and improving image quality. The proposed network consisted of fully connected, convolution and deconvolution layers, and the network training was implemented to directly transform few-view projections into reconstructed images without expert reconstruction knowledge. The quality of the output images obtained from the trained network was compared with those reconstructed by using the filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) algorithms. The results showed that the proposed network is able to improve the quantitative accuracy and noise property of reconstructed images, and the proposed network is more suitable for few-view CT reconstruction than the other algorithms. In conclusion, the proposed network can be potentially used to improve few-view CT image quality and considered as an alternative for few-view CT reconstruction. The source code is available on a GitHub repository at https://github.com/ YIMDOBIN/CT_reconstruction_via_DL.

A Novel Experimental Strategy for In situ SR-CT Mechanical Loading Test

In synchrotron radiation computed tomography (SR-CT) of mechanical loading process, accurate reconstruction of finite angle projection information shielded by mechanical loading devices is a bottleneck problem. For accurate characterization, an experimental strategy for in situ SR-CT mechanical loading test was proposed in this manuscript, which was called "finite angle CT reconstruction method based on tilted sampling information recovery" or TSIR-CT. Effectively recovering the information lost in finite angle CT is a fundamental solution. From the new perspective of the frequency domain, the tilt sampled projection information was utilized to recover the missing angle information. A series of numerical reconstruction experiments were carried out to verify the new method. It was indicated that the reconstruction results of TSIR-CT method have been improved quantitatively comparing with filtered back projection (FBP) and algebraic reconstruction technique (ART) method. For the 506th layer from top end of the 3D test model, the standardized covariance criterion for evaluating similarity was increased from 0.8400 of FBP and 0.9148 of ART to 0.9357, respectively. Finally, this new method was validated using actual SR-CT data and demonstrated effective improvement of reconstruction quality. This new method is suitable for in situ SR-CT mechanical loading test.

Mouse livers machine learning identification based on hyperspectral x-ray computed tomography reconstructed x-ray absorption spectra

X-ray computed tomography (X-CT) is often used to examine organs, but the reconstructed images can only be used for structural identification. Whether the organs are healthy or not requires a professional doctor to examine the reconstructed image and judge from his or her own experience. The purpose of this paper is to identify the cirrhotic mouse liver and normal mouse liver with hyperspectral x-ray CT (HXCT) and machine learning. HXCT is proposed to reconstruct the x-ray absorption spectrum (XAS) characteristics of a single pixel in the reconstructed mouse liver images. HXCT uses a cadmium telluride photon counter as the x-ray detector, which can improve the spectral resolution and separate spectral lines. Filtered back-projection and algebra reconstruction technique reconstruction algorithms are used for image and XAS reconstruction. In the machine learning model, principal component analysis is utilized to reduce the dimensionality of XAS. Besides, the neural network algorithm Artificial Neural Network (ANN) is used to train and identify the reconstructed XAS of two different kinds of livers. These two different mouse livers can be well recognized since the accuracy goes to almost 100% based on ANN. It is feasible to employ the machine learning algorithm to identify the XAS of different mouse livers.

Self-Guided Limited-Angle Computed Tomography Reconstruction Based on Anisotropic Relative Total Variation

In some computed tomography (CT) applications, limited-angle projections are used for image reconstruction, and traditional reconstruction methods, such as filtered back-projection (FBP) or simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images without prior knowledge assistance. For limited-angle CT reconstruction, total variation minimization (TVM) method is not conductive to recovering image structures. Image reconstruction methods based on anisotropic total variation (ATV) and reweighted anisotropic total variation (RwATV) can significantly reduce the shading artifacts using prior knowledge of the scanning angular range and image sparsity. However, using the knowledge of image sparsity does not further improve image quality in some applications. In this paper, we propose a new reconstruction method based on anisotropic relative total variation (ARTV) for limited-angle CT reconstruction. In ARTV, the windowed inherent variation (WIV) indicates the strength of structure information and WIV values are adaptively determined by local structure information. In limited-angle CT, particular scanning angular range urges us into exerting different strengths on different directions of ARTV. Experiments on FORBILD HEAD phantom, a thoracic image and real projections of a walnut are performed to test the new method. Experimental results show that the destroyed structures are recovered to some extent, and we acquire higher image quality compared to some existing limited-angle CT reconstruction methods.

Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniques

BackgroundThe influence of computed tomography (CT) reconstruction algorithms on the performance of machine-learning-based CT-derived fractional flow reserve (CT-FFRML) has not been investigated. CT-FFRML values and processing time of two reconstruction algorithms were compared using an on-site workstation. MethodsCT-FFRML was computed on 40 coronary CT angiography (CCTA) datasets that were reconstructed with both iterative reconstruction in image space (IRIS) and filtered back-projection (FBP) algorithms. CT-FFRML was computed on a per-vessel and per-segment basis as well as distal to lesions with ≥50% stenosis on CCTA. Processing times were recorded. Significant flow-limiting stenosis was defined as invasive FFR and CT-FFRML values ≤ 0.80. Pearson's correlation, Wilcoxon, and McNemar statistical testing were used for data analysis. ResultsPer-vessel analysis of IRIS and FBP reconstructions demonstrated significantly different CT-FFRML values (p ≤ 0.05). Correlation of CT-FFRML values between algorithms was high for the left main (r = 0.74), left anterior descending (r = 0.76), and right coronary (r = 0.70) arteries. Proximal and middle segments showed a high correlation of CT-FFRML values (r = 0.73 and r = 0.67, p ≤ 0.001, respectively), despite having significantly different averages (p ≤ 0.05). No difference in diagnostic accuracy was observed (both 81.8%, p = 1.000). Of the 40 patients, 10 had invasive FFR results. Per-lesion correlation with invasive FFR values was moderate for IRIS (r = 0.53, p = 0.117) and FBP (r = 0.49, p = 0.142). Processing time was significantly shorter using IRIS (15.9 vs. 19.8 min, p ≤ 0.05). ConclusionCT reconstruction algorithms influence CT-FFRML analysis, potentially affecting patient management. Additionally, iterative reconstruction improves CT-FFRML post-processing speed.

Two-dimensional photoacoustic imaging of femtosecond filament in water

We report a first-of-its-kind optoacoustic tomography of a femtosecond filament in water. Using a broadband (~100 MHz) piezoelectric transducer and a back-projection reconstruction technique, a single filament profile was retrieved. Obtained pressure distribution induced by the femtosecond filament allowed us to identify the size of the core and the energy reservoir with spatial resolution better than 10 µm. The photoacoustic imaging provides direct measurements of the energy deposition into the medium under filamentation of ultrashort laser pulses that cannot be obtained by existing techniques. In combination with a relative simplicity and high accuracy, photoacoustic imaging can be considered as a breakthrough instrument for filamentation investigation.

Optimization of Three-Dimensional (3D) Chemical Imaging by Soft X-Ray Spectro-Tomography Using a Compressed Sensing Algorithm.

Soft X-ray spectro-tomography provides three-dimensional (3D) chemical mapping based on natural X-ray absorption properties. Since radiation damage is intrinsic to X-ray absorption, it is important to find ways to maximize signal within a given dose. For tomography, using the smallest number of tilt series images that gives a faithful reconstruction is one such method. Compressed sensing (CS) methods have relatively recently been applied to tomographic reconstruction algorithms, providing faithful 3D reconstructions with a much smaller number of projection images than when conventional reconstruction methods are used. Here, CS is applied in the context of scanning transmission X-ray microscopy tomography. Reconstructions by weighted back-projection, the simultaneous iterative reconstruction technique, and CS are compared. The effects of varying tilt angle increment and angular range for the tomographic reconstructions are examined. Optimization of the regularization parameter in the CS reconstruction is explored and discussed. The comparisons show that CS can provide improved reconstruction fidelity relative to weighted back-projection and simultaneous iterative reconstruction techniques, with increasingly pronounced advantages as the angular sampling is reduced. In particular, missing wedge artifacts are significantly reduced and there is enhanced recovery of sharp edges. Examples of using CS for low-dose scanning transmission X-ray microscopy spectroscopic tomography are presented.

Transmission tomography of forward-scattering structures.

A method of multiple-shot scatter correction in transmission tomography systems is considered. It is assumed that the scattering is confined within a cone of relatively small angles, and the propagation is governed by a standard parabolic wave equation. An approximate directed-wave propagator may then be obtained, which relates a set of diffraction patterns measured for a multitude of point sources, to a linogram of the object. Introducing a specially designed coordinate transformation, we rewrite this propagator in a separable form as a paired Fresnel transform. Further, a corresponding inverse operator is applied to extract emulated projections (constituting jointly a sinogram) of the object from the measured data. Apart from the original algorithm, which suffers essentially from truncation errors, its improved, considerably more robust version is also constructed. The estimated sinograms may be inverted by any of the existing methods including a filtered back-projection or algebraic reconstruction techniques. The results of simulations show an essential reduction of diffraction/scattering artifacts and improved resolution of reconstructed images. At the same time it is found that scatter correction may cause object-dependent ring artifacts observed in the tomograms.

Stent Lumen Visibility in Single-energy CT Angiography: Does Tube Potential Matter?

There has been a trend toward lowering tube potential in computed tomography angiography (CTA) examinations to reduce radiation dose or contrast medium dose. The aim of this study was to evaluate the influence of tube potential on peripheral artery in-stent lumen visibility in CTA examinations. Nine different peripheral artery stents were placed in a vessel phantom (inner diameter: 5 mm, surrounded by water) and scanned consecutively using a 128-row CT scanner with 70, 80, 100, 120, and 140 kV and two different concentrations of contrast medium to simulate contrast-enhanced blood. Medium-smooth and ultra-sharp reconstruction kernels with filtered back projection (B30f, B46f) and iterative reconstruction technique (I30f, I46f) were used. Visible in-stent lumen diameter and artifact width were evaluated using a semiautomatic software tool. All stents were scanned with digital angiography, which was regarded as the reference standard. Averaged over all stents, visible in-stent lumen diameter ranged from 1.30 ± 0.21 mm (CM2/70 kV/I30f) to 3.13 ± 0.32 mm (CM1/120 kV/I46f). In-stent lumen diameters were significantly higher for 120 and 140 kV compared to 70 kV (2.39 ± 0.73 and 2.39 ± 0.66 mm vs 1.99 ± 0.69 mm; P = 0.01 and P = 0.005). Ultra-sharp reconstruction kernels lead to significantly better in-stent lumen visibility than smooth reconstruction kernels (B46f: 2.74 ± 0.34 mm vs B30f: 1.57 ± 0.36 mm; P < 0.001, respectively). Furthermore, in-stent lumen visibility was improved for iterative reconstructions compared to filtered back projection (I46f: 2.93 ± 0.30 mm vs B46f: 2.74 ± 0.34 mm; P < 0.001). Contrast medium concentration did not influence in-stent lumen visibility. Despite all known benefits of low kV CTA protocols, the use of a very low tube potential may hamper in-stent lumen visibility. A sharp kernel may be of value when evaluating the inner lumen of vascular stents.

Sub-milliSievert ultralow-dose CT colonography with iterative model reconstruction technique

Purpose. The purpose of this study was to evaluate the technical and diagnostic performance of sub-milliSievert ultralow-dose (ULD) CT colonograpy (CTC) in the detection of colonic and extracolonic lesions.Materials and Methods. CTC with standard dose (SD) and ULD acquisitions of 64 matched patients, half of them with colonic findings, were reconstructed with filtered back projection (FBP), hybrid (HIR) and iterative model reconstruction techniques (IMR). Image noise in six colonic segments, in the left psoas muscle and aorta were measured. Image quality of the left adrenal gland and of the colon in the endoscopic and 2D view was rated on a five point Likert scale by two observers, who also completed the reading of CTC for colonic and extracolonic findings.Results. The mean radiation dose estimate was 4.1 ± 1.4 mSv for SD and 0.86 ± 0.17 mSv for ULD for both positions (p < 0.0001). In ULD-IMR, SD-IMR and SD-HIR, the endoluminal noise was decreased in all colonic segments compared to SD-FBP (p < 0.001). There were 27 small (6–9 mm) and 17 large (≥10 mm) colonic lesions that were classified as sessile polyps (n = 38), flat lesions (n = 3), or as a mass (n = 3). Per patient sensitivity and specificity were 0.82 and 0.93 for ULD-FBP, 0.97 and 0.97 for ULD-HIR, 0.97 and 1.0 for ULD-IMR. Per polyp sensitivity was 0.84 for ULD-FBP, 0.98 for ULD-HIR, 0.98 for ULD-IMR. Significantly less extracolonic findings were detected in ULD-FBP and ULD-HIR, but in the E4 category by C-RADS (potentially important findings), the detection was similar.Conclusion. Both HIR and IMR are suitable for sub-milliSievert ULD CTC without sacrificing diagnostic performance of the study.

Comparison of iterative model, hybrid iterative, and filtered back projection reconstruction techniques in low-dose brain CT: impact of thin-slice imaging

The purpose of this study was to evaluate the utility of iterative model reconstruction (IMR) in brain CT especially with thin-slice images. This prospective study received institutional review board approval, and prior informed consent to participate was obtained from all patients. We enrolled 34 patients who underwent brain CT and reconstructed axial images with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and IMR with 1 and 5mm slice thicknesses. The CT number, image noise, contrast, and contrast noise ratio (CNR) between the thalamus and internal capsule, and the rate of increase of image noise in 1 and 5mm thickness images between the reconstruction methods, were assessed. Two independent radiologists assessed image contrast, image noise, image sharpness, and overall image quality on a 4-point scale. The CNRs in 1 and 5mm slice thickness were significantly higher with IMR (1.2 ± 0.6 and 2.2 ± 0.8, respectively) than with FBP (0.4 ± 0.3 and 1.0 ± 0.4, respectively) and HIR (0.5 ± 0.3 and 1.2 ± 0.4, respectively) (p < 0.01). The mean rate of increasing noise from 5 to 1mm thickness images was significantly lower with IMR (1.7 ± 0.3) than with FBP (2.3 ± 0.3) and HIR (2.3 ± 0.4) (p < 0.01). There were no significant differences in qualitative analysis of unfamiliar image texture between the reconstruction techniques. IMR offers significant noise reduction and higher contrast and CNR in brain CT, especially for thin-slice images, when compared to FBP and HIR.

Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium

BackgroundAdaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium. ObjectiveTo investigate the influence of ASIR on calcium quantification in comparison to FBP. MethodsIn 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques. ResultsImage noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P < 0.001). Use of ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size. ConclusionIn comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes.

Impact of Dose-Modified Protocols on Radiation Doses in Patients Undergoing CT Examinations following Image-Guided Catheter Placement

PurposeTo investigate the impact of dose-modified (DM) scan protocols on decreasing radiation exposure from computed tomography (CT) scans obtained following image-guided catheter procedures. Materials and MethodsIn this retrospective analysis, between December 2012 and June 2014, 192 patients (mean age, 60.7 y; 102 men) who underwent abdomen/pelvis CT examinations for catheter placement follow-up were included. The standard-dose (SD) baseline CT parameters included tube potential of 120 kVp, tube current of 75–550 mA, and noise index (NI) of 18–22. Weight-based scan parameters applied for follow-up CT were based on two reconstruction algorithms: filtered back projection (FBP; 120 kVp, 75–350 mA, NI = 30) and iterative reconstruction technique (IRT; 100/120 kVp, 75–250/350 mA, NI = 35). Two readers reviewed image quality (IQ) of follow-up and baseline CT examinations for 22 randomly sampled patients. Radiation doses were retrieved by dose monitoring software. ResultsCompared with baseline, DM follow-up CT protocols enabled substantial (62.4%) dose reductions (mean CT dose indexes: 4.1 mGy at follow-up, 10.9 mGy at baseline; P < .0001). Doses were significantly lower for IRT follow-up CT examinations compared with FBP (mean CT dose indexes: IRT, 3.6 mGy; FBP, 4.6 mGy; P < .05). In 47 patients with more than one follow-up CT examination (mean, 3.1 examinations per patient; range, 2–6), the observed cumulative radiation dose (CRD) was 42.1% lower than the expected CRD (observed, 1,437.9 mGy·cm; expected, 2,483.6 mGy·cm; P < .0001). Subjective IQ scores were acceptable for follow-up CT examinations (follow-up, 3.6; baseline, 4; P < .05). ConclusionsDM CT examinations enable substantial dose reduction (62.4%) for each follow-up examination compared with SD baseline scans, without any IQ concerns. Use of IRT decreases dose by an additional 22%. The CRD is lowered by 42% in patients undergoing multiple DM follow-up CT examinations.

Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography.

Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ. The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) - a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis-Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts.

Compensation of Missing Wedge Effects with Sequential Statistical Reconstruction in Electron Tomography

Electron tomography (ET) of biological samples is used to study the organization and the structure of the whole cell and subcellular complexes in great detail. However, projections cannot be acquired over full tilt angle range with biological samples in electron microscopy. ET image reconstruction can be considered an ill-posed problem because of this missing information. This results in artifacts, seen as the loss of three-dimensional (3D) resolution in the reconstructed images. The goal of this study was to achieve isotropic resolution with a statistical reconstruction method, sequential maximum a posteriori expectation maximization (sMAP-EM), using no prior morphological knowledge about the specimen. The missing wedge effects on sMAP-EM were examined with a synthetic cell phantom to assess the effects of noise. An experimental dataset of a multivesicular body was evaluated with a number of gold particles. An ellipsoid fitting based method was developed to realize the quantitative measures elongation and contrast in an automated, objective, and reliable way. The method statistically evaluates the sub-volumes containing gold particles randomly located in various parts of the whole volume, thus giving information about the robustness of the volume reconstruction. The quantitative results were also compared with reconstructions made with widely-used weighted backprojection and simultaneous iterative reconstruction technique methods. The results showed that the proposed sMAP-EM method significantly suppresses the effects of the missing information producing isotropic resolution. Furthermore, this method improves the contrast ratio, enhancing the applicability of further automatic and semi-automatic analysis. These improvements in ET reconstruction by sMAP-EM enable analysis of subcellular structures with higher three-dimensional resolution and contrast than conventional methods.

Submillisievert chest CT with filtered back projection and iterative reconstruction techniques.

The purpose of this study was to compare submillisievert chest CT images reconstructed with filtered back projection (FBP), SafeCT, adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR) with standard of care FBP images. Fifty patients (33 men and 17 women; mean age [± SD], 62 ± 10 years) undergoing routine chest CT gave written informed consent for acquisition of an additional submillisievert chest CT series with reduced tube current but identical scanning length as standard of care chest CT. Sinogram data of the submillisievert series were reconstructed with FBP, SafeCT, ASIR, and MBIR and compared with FBP images at standard-dose chest CT (n = 8 × 50 = 400 series). Two thoracic radiologists performed independent comparison for visualization of lesion margin, visibility of small structures, and diagnostic acceptability. Objective noise measurements and noise spectral density were obtained. Of 287 detected lesions, 162 were less than 1-cm noncalcified nodules. Lesion margins were well seen on all submillisievert reconstruction images except MBIR, on which they were poorly visualized. Likewise, only submillisievert MBIR images were suboptimal for visibility of normal structures, such as pulmonary vessels in the outer 2 cm of the lung, interlobular fissures, and subsegmental bronchial walls. MBIR had the lowest image noise compared with other techniques. FBP, SafeCT, ASIR, and MBIR can enable optimal lesion evaluation on chest CT acquired at a volume CT dose index of 2 mGy. However, all submillisievert reconstruction techniques were suboptimal for visualization of mediastinal structures. Submillisievert MBIR images were suboptimal for visibility of normal lung structures despite showing lower image noise.