Reconstruction from truncated projections using mixed extrapolations of exponential and quadratic functions

Truncation artifacts arise when the object being imaged extends past the scanned field of view (SFOV). The line integrals which lie beyond the SFOV are unmeasured, and reconstruction with traditional filtered backprojection (FBP) produces bright signal artifacts at the edge of the SFOV and little useful information outside the SFOV. A variety of techniques have been proposed to correct for truncation artifacts by estimating the unmeasured rays. We explore an alternative, iterative correction technique that reduces the artifacts and recovers the support (or outline) of the object that is consistent with the measured rays. We assume that the support is filled uniformly with tissue of a given CT number (for example, water-equivalent soft tissue) and segment the region outside the SFOV in a dichotomous fashion into tissue and air. In general, any choice for the object support will not be consistent with the measured rays in that a forward projection of the image containing the proposed support will not match the measured rays. The proposed algorithm reduces this inconsistency by deforming the object support to better match the measured rays. We initialize the reconstruction using the water cylinder extrapolation algorithm, an existing truncation artifact correction technique, but other starting algorithms can be used. The estimate of the object support is then iteratively deformed to reduce the inconsistency with the measured rays. After several iterations, forward projection is used to estimate the missing rays. Preliminary results indicate that this iterative, support recovery technique is able to produce superior reconstructions in the case of significant truncation compared to water cylinder extrapolation.

BackgroundPositron Emission Tomography (PET), Computed Tomography (CT), PET/CT and Single Photon Emission Tomography (SPECT) are non-invasive imaging tools used for creating two dimensional (2D) cross section images of three dimensional (3D) objects. PET and SPECT have the potential of providing functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules, whereas CT visualizes X-ray density in tissues in the body. PET/CT provides fused images representing both functional and anatomical information with better precision in localization than PET alone.Images generated by these types of techniques are generally noisy, thereby impairing the imaging potential and affecting the precision in quantitative values derived from the images. It is crucial to explore and understand the properties of noise in these imaging techniques. Here we used autocorrelation function (ACF) specifically to describe noise correlation and its non-isotropic behaviour in experimentally generated images of PET, CT, PET/CT and SPECT.MethodsExperiments were performed using phantoms with different shapes. In PET and PET/CT studies, data were acquired in 2D acquisition mode and reconstructed by both analytical filter back projection (FBP) and iterative, ordered subsets expectation maximisation (OSEM) methods. In the PET/CT studies, different magnitudes of X-ray dose in the transmission were employed by using different mA settings for the X-ray tube. In the CT studies, data were acquired using different slice thickness with and without applied dose reduction function and the images were reconstructed by FBP. SPECT studies were performed in 2D, reconstructed using FBP and OSEM, using post 3D filtering. ACF images were generated from the primary images, and profiles across the ACF images were used to describe the noise correlation in different directions. The variance of noise across the images was visualised as images and with profiles across these images.ResultsThe most important finding was that the pattern of noise correlation is rotation symmetric or isotropic, independent of object shape in PET and PET/CT images reconstructed using the iterative method. This is, however, not the case in FBP images when the shape of phantom is not circular. Also CT images reconstructed using FBP show the same non-isotropic pattern independent of slice thickness and utilization of care dose function. SPECT images show an isotropic correlation of the noise independent of object shape or applied reconstruction algorithm. Noise in PET/CT images was identical independent of the applied X-ray dose in the transmission part (CT), indicating that the noise from transmission with the applied doses does not propagate into the PET images showing that the noise from the emission part is dominant. The results indicate that in human studies it is possible to utilize a low dose in transmission part while maintaining the noise behaviour and the quality of the images.ConclusionThe combined effect of noise correlation for asymmetric objects and a varying noise variance across the image field significantly complicates the interpretation of the images when statistical methods are used, such as with statistical estimates of precision in average values, use of statistical parametric mapping methods and principal component analysis. Hence it is recommended that iterative reconstruction methods are used for such applications. However, it is possible to calculate the noise analytically in images reconstructed by FBP, while it is not possible to do the same calculation in images reconstructed by iterative methods. Therefore for performing statistical methods of analysis which depend on knowing the noise, FBP would be preferred.

Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.

The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application. Therefore, we propose a simple and efficient unsupervised method based on the Cycle-GAN framework. Introducing an implicit disentanglement strategy, we aim to separate truncation artifacts from content information. The separated artifact features serve as complementary constraints and the source of generating simulated paired data to enhance the training of the sub-network dedicated to removing truncation artifacts. Additionally, we incorporate polar transformation and an innovative constraint tailored specifically for truncation artifact features, further contributing to the effectiveness of our approach. Experiments conducted on multiple datasets demonstrate that our unsupervised network outperforms the traditional Cycle-GAN model significantly. When compared to state-of-the-art supervised models trained on paired datasets, our model achieves comparable visual results and closely aligns with quantitative evaluation metrics.

The objective of this study was to evaluate the radiation dose reduction potential of a novel image-based denoising technique in pediatric abdominopelvic and chest CT examinations and compare it with a commercial iterative reconstruction method. Data were retrospectively collected from 50 (25 abdominopelvic and 25 chest) clinically indicated pediatric CT examinations. For each examination, a validated noise-insertion tool was used to simulate half-dose data, which were reconstructed using filtered back-projection (FBP) and sinogram-affirmed iterative reconstruction (SAFIRE) methods. A newly developed denoising technique, adaptive nonlocal means (aNLM), was also applied. For each of the 50 patients, three pediatric radiologists evaluated four datasets: full dose plus FBP, half dose plus FBP, half dose plus SAFIRE, and half dose plus aNLM. For each examination, the order of preference for the four datasets was ranked. The organ-specific diagnosis and diagnostic confidence for five primary organs were recorded. The mean (± SD) volume CT dose index for the full-dose scan was 5.3 ± 2.1 mGy for abdominopelvic examinations and 2.4 ± 1.1 mGy for chest examinations. For abdominopelvic examinations, there was no statistically significant difference between the half dose plus aNLM dataset and the full dose plus FBP dataset (3.6 ± 1.0 vs 3.6 ± 0.9, respectively; p = 0.52), and aNLM performed better than SAFIRE. For chest examinations, there was no statistically significant difference between the half dose plus SAFIRE and the full dose plus FBP (4.1 ± 0.6 vs 4.2 ± 0.6, respectively; p = 0.67), and SAFIRE performed better than aNLM. For all organs, there was more than 85% agreement in organ-specific diagnosis among the three half-dose configurations and the full dose plus FBP configuration. Although a novel image-based denoising technique performed better than a commercial iterative reconstruction method in pediatric abdominopelvic CT examinations, it performed worse in pediatric chest CT examinations. A 50% dose reduction can be achieved while maintaining diagnostic quality.

Volumetric measurement of artificial pure ground-glass nodules at low-dose CT: Comparisons between hybrid iterative reconstruction and filtered back projection

Discrepancy between fields of view (FOVs) in a PET/CT scanner causes a truncation artifact when imaging extends beyond the CT FOV. The purposes of this study were to evaluate the impact of this artifact on measurements of 18F-FDG activity concentrations and to assess a truncation correction algorithm. Two phantoms and five patients were used in this study. In the first phantom, three inserts (water, air, bone equivalent) were placed in a water-filled cylinder containing 18F-FDG. In the second phantom study, a chest phantom and a 2-L bottle fitted with a bone insert were used to simulate a patient's torso and arm. Both phantoms were imaged while positioned centrally (baseline) and at the edge of the CT FOV to induce truncation. PET images were reconstructed using attenuation maps from truncated and truncation-corrected CT images. Regions of interest (ROIs) drawn on the inserts, simulated arm, and background water of the baseline truncated and truncation-corrected PET images were compared. In addition, extremity malignancies of five patients truncated on CT images were reconstructed with and without correction and the maximum standard uptake values (SUVs) of the malignancies were compared. Truncation artifact manifests as a rim of high activity concentration at the edge of the truncated CT image with an adjacent low-concentration region peripherally. The correction algorithm minimizes these effects. Phantom studies showed a maximum variation of -5.4% in the truncation-corrected background water image compared with the baseline image. Activity concentration in the water insert was 6.3% higher while that of air and bone inserts was similar to baseline. Extremity malignancies showed a consistent increase in the maximum SUV after truncation correction. Truncation affects measurements of 18F-FDG activity concentrations in PET/CT. A truncation-correction algorithm corrects truncation artifacts with small residual error.

The purpose of this study was to survey the radiation dose used in CT urography (CTU) in routine clinical practice, both before and after implementation of a scanning protocol that uses iterative reconstruction (Adaptive Iterative Dose Reduction 3D [AIDR 3D]). We retrospectively surveyed dose reports from consecutive CTU examinations performed in 2011 with the use of 64- and 320-MDCT scanners that were reconstructed with filtered back projection (FBP) and from CTU examinations performed from May 2012 through November 2013 that were reconstructed with the use of AIDR 3D. Findings from these dose reports were then correlated with such patient characteristics as weight and body mass index (BMI; weight in kilograms divided by the square of height in meters). Only dose reports from single-bolus three-phase CTU examinations were included in the study. The volume CT dose index, dose-length product (DLP), and effective dose were surveyed both per examination and per phase by use of published effective dose DLP conversion factors. Image quality was evaluated subjectively for a subset of patients. The two study cohorts included 82 patients (median patient weight, 75.0 kg; median BMI, 25.3) who underwent CTU with FBP and 85 patients (median patient weight, 78.0 kg; median BMI, 24.5) who underwent CTU with AIDR 3D. The median total DLP and median effective dose were 924 mGy · cm and 13.0 mSv, respectively, in the CTU with the FBP cohort and 433 mGy · cm and 6.1 mSv, respectively, in the CTU with the AIDR 3D cohort. The median DLP in the unenhanced, nephrogenic, and excretory phases was 218, 300, and 441 mGy · cm, respectively, in patients undergoing CTU with FBP and 114, 121, and 190 mGy · cm, respectively, in patients undergoing CTU with AIDR 3D. Image quality was diagnostic in both groups, with relatively fewer artifacts noted on scans obtained using CTU with AIDR 3D. Our study presents detailed dose data from three-phase CTU examinations performed both before and after implementation of iterative reconstruction. Implementation of a CTU protocol using iterative reconstruction resulted in a mean effective dose of 6.1 mSv with preservation of clinical diagnostic image quality.

Dose and image quality are two important parameters in CT Scan examination. The radiation dose produced at the CT scan is far greater than the radiation dose of the conventional radiological examination, so the utilization must be as good as possible. On Siemens CT Scan there is an automatic exposure control software, with expectations that the radiation dose received by the patient can be reduced because the software is an auto mA that is adjusted to the thickness of the object, but the impact of using the software causes a decrease in image quality, so it needs to be done image improvement using a back projection filter (BPF). Experimental research by applying a back projection filter to the Scan Head image on axial sections. Image assessment using 2 radiologists with data analysis using statistical processing and image post processing. CT Scan image information Head axial pieces experience differences in image information between before and after the back projection, with the mean value in each group experiencing an increase in image quality in the group after a back projection filter. Where the image after the back projection filter by applying automatic exposure control with kV 110 is the most optimal kV.

Purpose To determine if model-based iterative reconstruction (MBIR) can improve visualization of the Adamkiewicz artery on multi–detector row computed tomographic (CT) images compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). Materials and Methods This retrospective study was approved by the institutional review board, and written informed consent for the CT examination was obtained. Thirty-three patients underwent contrast material–enhanced 64-section multi–detector row CT for assessment of aortic aneurysm or dissection. Helical data were reconstructed by using FBP, ASIR, and MBIR. The signal-to-noise ratio of the aorta and contrast-to-noise ratio of the anterior spinal artery relative to the spinal cord were measured on multiplanar reformatted images. Visualization of the Adamkiewicz artery and its continuity with the intercostal or lumbar artery were evaluated by using a four-point scale. All image analyses were performed by two blinded, independent observers. The one-way analysis of variance and the Wilcoxon signed-rank test were used for statistical analysis. Results MBIR showed significantly better signal-to-noise and contrast-to-noise ratios than did ASIR and FBP (P < .05 for all comparisons) with good interobserver agreement (intraclass correlation coefficient of 0.93 for signal-to-noise ratio and 0.75 for contrast-to-noise ratio). The visualization score of the Adamkiewicz artery was also significantly better when MBIR was used (3.4 ± 0.8 and 3.6 ± 0.7 for observers A and B, respectively) than when ASIR (2.7 ± 1.1 and 3.0 ± 1.0, respectively) or FBP (2.5 ± 1.2 and 3.1 ± 0.9, respectively) was used. Conclusion Use of the MBIR algorithm led to improved multi–detector row CT visualization of the Adamkiewicz artery when compared with the use of ASIR and FBP. © RSNA, 2013

Coronary artery calcium (CAC) scoring with computed tomography (CT) is an established tool for quantifying calcified atherosclerotic plaque burden. Despite the widespread use of novel image reconstruction techniques in CT, the effect of iterative model reconstruction on CAC score remains unclear. We sought to assess the impact of iterative model based reconstruction (IMR) on coronary artery calcium quantification as compared to the standard filtered back projection (FBP) algorithm and hybrid iterative reconstruction (HIR). In addition, we aimed to simulate the impact of iterative reconstruction techniques on calcium scoring based risk stratification of a larger asymptomatic population. We studied 63 individuals who underwent CAC scoring. Images were reconstructed with FBP, HIR and IMR and CAC scores were measured. We estimated the cardiovascular risk reclassification rate of IMR versus HIR and FBP in a larger asymptomatic population (n = 504). The median CAC scores were 147.7 (IQR 9.6-582.9), 107.0 (IQR 5.9-526.6) and 115.1 (IQR 9.3-508.3) for FBP, HIR and IMR, respectively. The HIR and IMR resulted in lower CAC scores as compared to FBP (both p < 0.001), however there was no difference between HIR and IMR (p = 0.855). The CAC score decreased by 7.2 % in HIR and 7.3 % in IMR as compared to FBP, resulting in a risk reclassification rate of 2.4 % for both HIR and IMR. The utilization of IMR for CAC scoring reduces the measured calcium quantity. However, the CAC score based risk stratification demonstrated modest reclassification in IMR and HIR versus FBP.

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.

Objective To study the effect of the reconstruction method on the semi-quantitative distribution of dopamine transporter (DAT) in the brain, 2β-carbomethoxy-3β-(4-fluorophe-nyl)-(N-11C-methyl)tropane (11C-CFT) PET images of healthy subjects were reconstructed by different PET reconstruction methods. Methods From March 2014 to June 2015, the 11C-CFT PET images of 41 healthy subjects were reconstructed by filtering back projection(FBP) method and ordered subsets expectation maximization (OSEM) iterative method. The brain regions of interest (ROI), namely, caudate nucleus, anterior putamen, and posterior shell nucleus, were automatically sketched with the parietal and occipital cortex lacking of DAT distribution as reference regions. The semi-quantitative value of DAT distribution was calculated using the following formula: radioactivity count of ROI/radioactivity count of parietal and occipital cortex-1. Paired t-test was used to compare the semi-quantitative values of DAT distribution. Correlation analysis was performed using the Pearson correlation analysis. Results The values of DAT distribution based on OSEM were as follows: caudate nucleus (1.77-2.15), anterior putamen (2.17-2.39), and posterior putamen (1.71-2.06). The values of DAT distribution based on FBP were as follows: caudate nucleus (1.68-2.10), anterior putamen (2.07-2.37), and posterior putamen (1.62-1.96). In the bilateral caudate nucleus and anterior and posterior putamen, the 11C-CFT of DAT distribution by OSEM was significantly higher than that by FBP(t=9.658-15.859, all P=0.000). The Pearson correlation analysis showed that the semi-quantitative values of DAT distribution by FBP and OSEM were positively correlated with each other in the bilateral caudate nucleus and anterior and posterior putamen (R2=0.907-0.951, all P=0.000). The DAT distribution by OSEM and FBP decreased with aging in the caudate nucleus and anterior and posterior putamen. Conclusions A significant difference was found in the semi-quantitative value of the DAT distribution in the brain of healthy subjects by different PET reconstruction methods. Therefore, a consistent PET image reconstruction method should be used in a multicenter or longitudinal study. Key words: Dopamine plasma membrane transport proteins; Positron-emission tomography; Filtered backprojection; Iterative

The purpose of this article is to study the added value of model-based iterative reconstruction (MBIR) on metal artifact reduction on CT compared with standard filtered back projection (FBP). Ex vivo imaging was performed on several metal implants. Datasets were reconstructed with standard FBP and MBIR algorithms. The sizes of the artifacts surrounding the metal implant were recorded and compared. In vivo imaging was performed on 62 patients with metal implants. Each dataset was reconstructed with FBP and MBIR algorithms. Objective image quality was assessed by measuring the size of the artifact generated by the metal implant. Subjective image quality was graded on a 3-point scale, taking into account the visibility of the bone-metal interface, as well as the visibility of the neighboring soft tissues. Ex vivo analysis yielded a reduction of 82% in the size of the artifact when using the MBIR algorithm, compared with the FBP algorithm. The mean (SD) size of the artifacts was 1.4 ± 0.8 and 0.25 ± 0.06 cm(2) with FBP and MBIR, respectively. In vivo, the mean size of the artifacts decreased from 7.3 ± 1.5 cm(2) to 4.0 ± 0.9 cm(2) for FBP and MBIR, respectively (p = 0.012). The subjective image quality analysis showed an equal or better bone-metal interface of MBIR algorithm in 85% of cases. Visibility of the soft tissue surrounding the metal implant was determined to be equal or better in 97% of cases in which MBIR was used. This study shows that the MBIR algorithm allows a clear reduction of metal artifacts on CT images and, hence, a better analysis of the soft tissue surrounding the metal implant compared with FBP.

OA158] Noise equivalent number of quanta (NEQ): A tool for choosing the optimal reconstruction algorithm in computed tomography (CT)