Advanced Iterative Reconstruction Research Articles

Accelerated 3D multi-echo spin-echo sequence with a subspace constrained reconstruction for whole mouse brain mapping.

To accelerate whole-brain quantitative mapping in preclinical imaging setting. A three-dimensional (3D) multi-echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high-quality raw images. Different subspaces, generated using exponential or extended-phase graph (EPG) simulations or from low-resolution calibration images, were compared. The subspace dimension was investigated in terms of precision. The method was validated on a phantom containing a wide range of and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality and estimation were assessed for 3 acceleration factors (6/8/10). The EPG-based dictionary gave robust estimations of a large range of . A subspace dimension of 6 was the best compromise between precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole-brain maps, the detection and the monitoring of metastasis growth of less than 500 . Subspace-based reconstruction is suitable for 3D mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156 m on the mouse brain, used here for monitoring metastases growth.

Music curriculum integration and reconstruction model based on advanced music waveform iterative reconstruction algorithm

To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms.

The Impact of Novel Reconstruction Algorithms on Calcium Scoring: Results on a Dedicated Cardiac CT Scanner

Contemporary reconstruction algorithms yield the potential of reducing radiation exposure by denoising coronary computed tomography angiography (CCTA) datasets. We aimed to assess the reliability of coronary artery calcium score (CACS) measurements with an advanced adaptive statistical iterative reconstruction (ASIR-CV) and model-based adaptive filter (MBAF2) designed for a dedicated cardiac CT scanner by comparing them to the gold-standard filtered back projection (FBP) calculations. We analyzed non-contrast coronary CT images of 404 consecutive patients undergoing clinically indicated CCTA. CACS and total calcium volume were quantified and compared on three reconstructions (FBP, ASIR-CV, and MBAF2+ASIR-CV). Patients were classified into risk categories based on CACS and the rate of reclassification was assessed. Patients were categorized into the following groups based on FBP reconstructions: 172 zero CACS, 38 minimal (1-10), 87 mild (11-100), 57 moderate (101-400), and 50 severe (400<). Overall, 19/404 (4.7%) patients were reclassified into a lower-risk group with MBAF2+ASIR-CV, while 8 additional patients (27/404, 6.7%) shifted downward when applying stand-alone ASIR-CV. The total calcium volume with FBP was 7.0 (0.0-133.25) mm3, 4.0 (0.0-103.5) mm3 using ASIR-CV, and 5.0 (0.0-118.5) mm3 with MBAF2+ASIR-CV (all comparisons p < 0.001). The concomitant use of ASIR-CV and MBAF2 may allow the reduction of noise levels while maintaining similar CACS values as FBP measurements.

Observer Sensitivity for Detection of Pulmonary Nodules in Ultra-Low Dose Computed Tomography Protocols Using a Third-Generation Dual-Source CT with Ultra-High Pitch—A Phantom Study

This study evaluates ultra-low-dose computed tomography (ULDCT) protocols concerning the detectability of pulmonary nodules. The influence of tube current settings, kernels, strength levels of third-generation iterative reconstruction algorithms, and pitch was investigated. A chest phantom with artificial spherical nodules of different densities and diameters was examined with a third-generation dual-source CT. Scanning and post-processing protocols, tube current levels, and ultra-high and non-high pitch modes were applied. Images were reconstructed with filtered back-projection (FBP) or advanced model-based iterative reconstruction (ADMIRE) algorithms. Sharp (Bl57) or medium-soft (Br36) convolution kernels were applied. The reading was performed by an experienced and an inexperienced reader. The highest observer sensitivity was found using a non-high pitch protocol at tube currents of 120 mAs and 90 mAs with the sharp kernel and iterative reconstruction level of 5. Non-high pitch protocols showed better detectability of solid nodules. Combinations with the medium-soft kernel achieved slightly higher observer sensitivity than with the sharp kernel. False positives (FP) occurred more often for subsolid nodules, at a tube current level of 120 mAs, and with the sharp kernel. A tube current level of 90 mAs combined with the highest iterative reconstruction level achieved the highest accuracy in lung nodule detection regardless of size, density, and reader experience.

Accuracy of Virtual Non-contrast Image Reconstruction Using Material Decomposition for Fast kV-switching Dual-energy CT

Dual-energy computed tomography (DECT) system can generate virtual non-contrast (VNC) images. Although several reconstruction algorithms are defined, there are not many researches using deep learning image reconstruction (DLIR) algorithm. In this study, we evaluated the accuracy of the VNC image reconstruction under various conditions using DLIR algorithm. At first, each iodine insert with variable concentrations (2.0, 5.0, 10.0, 15.0 mg/ml) or diameters (2.0, 5.0, 10.0, 28.5 mm), or mixed insert including blood-mimicking material with iodine (iodine concentrations: 2.0, 4.0 mg/ml) was put in the center of the multi-energy CT phantom (Gammex, USA). This phantom was placed in the isocenter of DECT, and it scanned and reconstructed the VNC images. In addition, the VNC images were reconstructed with various display field of view (DFOV) sizes (240, 350 mm) or reconstruction algorithms (filtered back projection, advanced statistical iterative reconstruction, deep learning image reconstruction) for each iodine diameter. Attenuation values of these images (CTVNC) were measured and assessed by placing a circular region of interest (ROI) on each insert. CTVNC form iodine inserts increased with iodine concentration became lower, whereas CTVNC form blood plus iodine inserts were stable regardless of low iodine concentration. As iodine diameter became smaller, CTVNC increased remarkably. CTVNC remained steady even though reconstruction parameters were varied. In our study, the VNC image reconstruction using DLIR algorithm was affected by various conditions such as iodine concentration and size. In particular, its accuracy was reduced by the size of target.

Iterative Reconstruction: State-of-the-Art and Future Perspectives.

Image reconstruction processing in computed tomography (CT) has evolved tremendously since its creation, succeeding at optimizing radiation dose while maintaining adequate image quality. Computed tomography vendors have developed and implemented various technical advances, such as automatic noise reduction filters, automatic exposure control, and refined imaging reconstruction algorithms.Focusing on imaging reconstruction, filtered back-projection has represented the standard reconstruction algorithm for over 3 decades, obtaining adequate image quality at standard radiation dose exposures. To overcome filtered back-projection reconstruction flaws in low-dose CT data sets, advanced iterative reconstruction algorithms consisting of either backward projection or both backward and forward projections have been developed, with the goal to enable low-dose CT acquisitions with high image quality. Iterative reconstruction techniques play a key role in routine workflow implementation (eg, screening protocols, vascular and pediatric applications), in quantitative CT imaging applications, and in dose exposure limitation in oncologic patients.Therefore, this review aims to provide an overview of the technical principles and the main clinical application of iterative reconstruction algorithms, focusing on the strengths and weaknesses, in addition to integrating future perspectives in the new era of artificial intelligence.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound

BackgroundAdvances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. MethodsMatched cross-sections (n ​= ​7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 ​mm and 0.50 ​mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. ResultsSegment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 ​mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p ​< ​0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p ​< ​0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64–0.67; p ​= ​0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p ​< ​0.001 and plaque burden ICC: 0.45, RC: 32.0, p ​< ​0.001). ConclusionCCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. Clinicaltrials.gov (NCT03556644)

MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography Imaging

This work extends our previous research entitled &#x201C;MemXCT: Memory-centric X-ray CT Reconstruction with Massive Parallelization&#x201D; that was originally published at SC19 conference (Hidayeto&#x011F;lu <i>et al.</i>, 2019) with reproducibility of the computational imaging performance. X-ray computed tomography (XCT) is regularly used at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be in the order of terabytes. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use an exception rather than the rule. We propose a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a memory-centric iterative reconstruction system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures involving KNL and GPU. MemXCT can reconstruct a large (11K&#x00D7;11K) mouse brain tomogram in 10 seconds using 4096 KNL nodes (256K cores). The results presented in our original article at the SC19 were based on large-scale supercomputing resources. The MemXCT application was selected for the Student Cluster Competition (SCC) Reproducibility Challenge and evaluated on a variety of cloud computing resources by universities around the world in the SC20 conference. We summarize the results of the top-ranked SCC Reproducibility Challenge teams and identify the most pertinent measures for ensuring the reproducibility of our experiments in this article.

A Music Curriculum Integration and Reconstruction Model Based on Advanced Iterative Reconstruction Algorithm

Music curriculum fusion and super-resolution reconstruction based on musical elements have gradually attracted the attention of researchers. The traditional music element fusion and reconstruction algorithm is based on the fusion and reconstruction of all the pixel information of the source music element, which has the problem of high time and space complexity. Based on the advanced iterative reconstruction theory, this paper uses the measurement matrix to measure the dimensionality of the music signal, compresses the music element data while acquiring the music elements, reduces the sampling frequency, reduces the sampling amount of the music element data, and greatly reduces the data. It solves the problem of spatial resolution reduction caused by degradation and the problem of music element feature extraction, classification, and identification and can obtain more music element features and detailed parameters. Aiming at the back-projection algorithm of musical symbol filtering, the advanced iterative reconstruction algorithm of musical symbol filtering of a triangular line array is simulated. The experimental results show that the feasibility analysis factor of the scheme reaches 0.917, and the running time of the reconstruction algorithm is reduced to 0.131 s, which promotes a large amount of data in music curriculum element fusion and super-resolution reconstruction.

A novel simulation-driven reconstruction approach for x-ray computed tomography.

Radiation dose reduction is critical to the success of x-ray computed tomography (CT). Many advanced reconstruction techniques have been developed over the years to combat noise resulting from the low-dose CT scans. These algorithms rely on accurate local estimation of the image noise to determine reconstruction parameters or to select inferencing models. Because of the difficulties in the noise estimation for heterogeneous objects, the performance of many algorithms is inconsistent and suboptimal. Here, we propose a novel approach to overcome such shortcoming. By injecting appropriate amount of noise in the CT raw data, a computer simulation approach is capable of accurately estimating the local statistics of the raw data and the local noise in the reconstructed images. This information is then used to guide the noise-reduction process during the reconstruction. As an initial implementation, a scaling map is generated based on the noise predicted from the simulation and the noise estimated from existing reconstruction algorithms. Images generated with existing algorithms are subsequently modified based on the scaling map. In this study, both iterative reconstruction (IR) and deep learning image reconstruction (DLIR) algorithms are evaluated. Phantom experiments were conducted to evaluate the performance of the simulation-based noise estimation in terms of the standard deviation and noise power spectrum. Quantitative results have demonstrated that the noise measured from the original image matches well with the noise estimated from the simulation. Clinical datasets were utilized to further confirm the accuracy of the proposed approach under more challenging conditions. To validate the performance of the proposed reconstruction approach, clinical scans were used. Performance comparison was carried out qualitatively and quantitatively. Two existing advanced reconstruction techniques, IR and DLIR, were evaluated against the proposed approach. Results have shown that the proposed approach outperforms existing IR and DLIR algorithms in terms of noise suppression and, equally importantly, noise uniformity across the entire imaging volume. Visual assessment of the images also reveals that the proposed approach does not endure noise texture issues faced by some of the existing reconstruction algorithms today. Phantom and clinical results have demonstrated superior performance of the proposed approach with regard to noise reduction as well as noise homogeneity. Visual inspection of the noise texture further confirms the clinical utility of the proposed approach. Future enhancements on the current implementation are explored regarding image quality and computational efficiency. Because of the limited scope of this paper, detailed investigation on these enhancement features will be covered in a separate report.

Comparison of acquisition and iterative reconstruction parameters in abdominal computed tomography-guided procedures: a phantom study

Many computed tomography (CT) navigation systems have been developed to help radiologists improve the accuracy and safety of the procedure. We evaluated the accuracy of one CT computer-assisted guided procedure with different reduction dose protocols. A total of 128 punctures were randomly made by two operators on two different anthropomorphic phantoms. The tube voltage was fixed to 100 kVp. Tube currents (mAs) were defined to obtain 4 dose levels: 180 mAs (D1.00), 90 mAs (D0.50), 45 mAs (D0.25) and 15 mAs (D0.10) with respective volume CT dose index (CTDIvol) of 7.02, 3.52, 1.75 and 0.59 mGy. The raw data were reconstructed using level 2 of advanced model-based iterative reconstruction (ADMIRE) (A2) for D1.00, A3 for D0.50, A4 for D0.25 and A5 for D0.10. Two 12-mm targets per phantom were selected. The mean Euclidean distance (EuD) between the tip of the needle and the isocenter of the target was measured for each puncture. The different measures were compared by paired Student's t-tests. The mean EuD was 7.0±3.1 mm for the 128 punctures performed. Regardless of which phantom was considered, no significant difference in accuracy occurred between the 4 dose levels, which were 7.1±3.5 mm for D1.00; 7.1±3.1 mm for D0.50; 7.2±3.0 mm for D0.25 and 6.6±2.6 mm for D0.10. Abdominal CT-guided procedures, using computer-assisted navigation and iterative reconstruction algorithms, allow precise punctures on anthropomorphic phantoms with a dose reduction of -92% compared to a standard protocol.

CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques.

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ©RSNA, 2021.

Standardisation of conventional and advanced iterative reconstruction methods for Gallium-68 multi-centre PET-CT trials

PurposeTo assess the applicability of the Fluorine-18 performance specifications defined by EANM Research Ltd (EARL), in Gallium-68 multi-centre PET-CT trials using conventional (ordered subset expectation maximisation, OSEM) and advanced iterative reconstructions which include the systems’ point spread function (PSF) and a Bayesian penalised likelihood algorithm (BPL) commercially known as Q.CLEAR. The possibility of standardising the two advanced reconstruction methods was examined.MethodsThe NEMA image quality phantom was filled with Gallium-68 and scanned on a GE PET-CT system. PSF and BPL with varying post-reconstruction Gaussian filter width (2–6.4 mm) and penalisation factor (200–1200), respectively, were applied. The average peak-to-valley ratio from six profiles across each sphere was estimated to inspect any edge artefacts. Image noise was assessed using background variability and image roughness. Six GE and Siemens PET-CT scanners provided Gallium-68 images of the NEMA phantom using both conventional and advanced reconstructions from which the maximum, mean and peak recoveries were drawn. Fourteen patients underwent 68Ga-PSMA PET-CT imaging. BPL (200-1200) reconstructions of the data were compared against PSF smoothed with a 6.4-mm Gaussian filter.ResultsA Gaussian filter width of approximately 6 mm for PSF and a penalisation factor of 800 for BPL were needed to suppress the edge artefacts. In addition, those reconstructions provided the closest agreement between the two advanced iterative reconstructions and low noise levels with the background variability and the image roughness being lower than 7.5% and 11.5%, respectively. The recoveries for all methods generally performed at the lower limits of the EARL specifications, especially for the 13- and 10-mm spheres for which up to 27% (conventional) and 41% (advanced reconstructions) lower limits are suggested. The lesion standardised uptake values from the clinical data were significantly different between BPL and PSF smoothed with a Gaussian filter of 6.4 mm wide for all penalisation factors except for 800 and 1000.ConclusionIt is possible to standardise the advanced reconstruction methods with the reconstruction parameters being also sufficient for minimising the edge artefacts and noise in the images. For both conventional and advanced reconstructions, Gallium-68 specific recovery coefficient limits were required, especially for the smallest phantom spheres.

Deep learning-based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study.

(1) To compare low-contrast detectability of a deep learning-based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIRE, IMR, and FBP). Using abdominal CT images of 100 patients reconstructed via ADMIRE and FBP, we trained DLA by feeding FBP images as input and ADMIRE images as the ground truth. To measure the low-contrast detectability, the randomized repeat scans of Catphan® phantom were performed under various conditions of radiation exposures. Twelve radiologists evaluated the presence/absence of a target on a five-point confidence scale. The multi-reader multi-case area under the receiver operating characteristic curve (AUC) was calculated, and non-inferiority tests were performed. Using American College of Radiology CT accreditation phantom, contrast-to-noise ratio, target transfer function, noise magnitude, and detectability index (d') of DLA, ADMIRE, IMR, and FBPs were computed. The AUC of DLA in low-contrast detectability was non-inferior to that of ADMIRE (p < .001) and superior to that of FBP (p < .001). DLA improved the image quality in terms of all physical measurements compared to FBPs from both CT vendors and showed profiles of physical measurements similar to those of ADMIRE. The low-contrast detectability of the proposed deep learning-based denoising algorithm was non-inferior to that of ADMIRE and superior to that of FBP. The DLA could successfully improve image quality compared with FBP while showing the similar physical profiles of ADMIRE. • Low-contrast detectability in the images denoised using the deep learning algorithm was non-inferior to that in the images reconstructed using standard algorithms. • The proposed deep learning algorithm showed similar profiles of physical measurements to advanced iterative reconstruction algorithm (ADMIRE).

Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction.

OBJECTIVE. Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying characterization results of a deep learning approach to decisions about clinical CT protocols. MATERIALS AND METHODS. A proprietary deep learning image reconstruction (DLIR) method was characterized against an existing advanced adaptive statistical iterative reconstruction method (ASIR-V) and FBP from the same vendor. The metrics used were contrast-to-noise ratio, spatial resolution as a function of contrast level, noise texture (i.e., noise power spectra [NPS]), noise scaling as a function of slice thickness, and CT number consistency. The American College of Radiology accreditation phantom and a uniform water phantom were used at a range of doses and slice thicknesses for both axial and helical acquisition modes. RESULTS. ASIR-V and DLIR were associated with improved contrast-to-noise ratio over FBP for all doses and slice thicknesses. No dose or contrast dependencies of spatial resolution were observed for ASIR-V or DLIR. NPS results showed DLIR maintained an FBP-like noise texture whereas ASIR-V shifted the NPS to lower frequencies. Noise changed with dose and slice thickness in the same manner for ASIR-V and FBP. DLIR slice thickness noise scaling differed from FBP, exhibiting less noise penalty with decreasing slice thickness. No clinically significant changes were observed in CT numbers for any measurement condition. CONCLUSION. In a phantom model, DLIR does not suffer from the concerns over reduction in spatial resolution and introduction of poor noise texture associated with previous methods.

Impact of dose reduction and the use of an advanced model-based iterative reconstruction algorithm on spectral performance of a dual-source CT system: A task-based image quality assessment

PurposeTo assess the impact of dose reduction and the use of an advanced modeled iterative reconstruction algorithm (ADMIRE) on image quality in low-energy monochromatic images from a dual-source dual energy computed tomography CT (DSCT) platform. Materials and methodsAcquisitions on an image-quality phantom were performed using DSCT equipment with 100/Sn150 kVp for four dose levels (CTDIvol: 20/11/8/5mGy). Raw data were reconstructed for six energy levels (40/50/60/70/80/100 keV) using filtered back projection and two levels of ADMIRE (A3/A5). Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated on virtual monoenergetic images (VMIs). Detectability index (d′) was computed to model the detection task of two enhanced iodine lesions as function of keV. ResultsNoise-magnitude was significantly reduced between 40 to 70 keV by −56±0% (SD) (range: −56%–−55%) with FBP; −56±0% (SD) (−56%–−56%) with A3; and −57±1% (SD) (range: −57%–−56%) with A5. The average spatial frequency of the NPS peaked at 70 keV and decreased as ADMIRE level increased. TTF values at 50% were greatest at 40 keV and shifted towards lower frequencies as the keV increased. The detectability of both lesions increased with increasing dose level and ADMIRE level. For the simulated lesion with iodine at 2mg/mL, d’ values peaked at 70 keV for all reconstruction types, except for A3 at 20 mGy and A5 at 11 and 20 mGy, where d’ peaked at 60 keV. For the other simulated lesion, d’ values were highest at 40 keV and decreased beyond. ConclusionAt low keV on VMIs, this study confirms that iterative reconstruction reduces the noise magnitude, improves the spatial resolution and increases the detectability of enhanced iodine lesions.

Impact of increasing levels of adaptive statistical iterative reconstruction on image quality in oil-based postmortem CT angiography in coronary arteries

IntroductionPostmortem multi-detector computed tomography (PMCT) has become an important part in forensic imaging. Modern reconstruction techniques such as iterative reconstruction (IR) are frequently used in postmortem CT angiography (PMCTA). The image quality of PMCTA depends on the strength of IR. For this purpose, we aimed to investigate the impact of different advanced IR levels on the objective and subjective PMCTA image quality.Material and methodsWe retrospectively analyzed the coronary arteries of 27 human cadavers undergoing whole-body postmortem CT angiography between July 2017 and March 2018 in a single center. Iterative reconstructions of the coronary arteries were processed in five different level settings (0%; 30%; 50%; 70%; 100%) by using an adaptive statistical IR method. We evaluated the objective (contrast-to-noise ratio (CNR)) and subjective image quality in several anatomical locations.ResultsOur results demonstrate that the increasing levels of an IR technique have relevant impact on the image quality in PMCTA scans in forensic postmortem examinations. Higher levels of IR have led to a significant reduction of image noise and therefore to a significant improvement of objective image quality (+ 70%). However, subjective image quality is inferior at higher levels of IR due to plasticized image appearance.ConclusionObjective image quality in PMCTA progressively improves with increasing level of IR with the best CNR at the highest IR level. However, subjective image quality is best at low to medium levels of IR. To obtain a “classic” image appearance with optimal image quality, PMCTAs should be reconstructed at medium levels of IR.

Dual-source computed tomography of the lung with spectral shaping and advanced iterative reconstruction: potential for maximum radiation dose reduction

BackgroundRadiation dose at CT should be as low as possible without compromising diagnostic quality.ObjectiveTo assess the potential for maximum dose reduction of pediatric lung dual-source CT with spectral shaping and advanced iterative reconstruction (ADMIRE).Materials and methodsWe retrospectively analyzed dual-source CT acquisitions in a full-dose group (FD: 100 kV, 64 reference mAs) and in three groups with spectral shaping and differing reference mAs values (Sn: 100 kV, 96/64/32 reference mAs), each group consisting of 16 patients (age mean 11.5 years, standard deviation 4.8 years, median 12.8 years, range 1.3–18 years). Advanced iterative reconstruction of images was performed with different strengths (FD: ADMIRE Level 2; Sn: ADMIRE Levels 2, 3 and 4). We analyzed dose parameters and measured noise. Diagnostic confidence and detectability of lung lesions as well as anatomical structures were assessed using a Likert scale (from 1 [unacceptable] to 4 [fully acceptable]).ResultsCompared to full dose, effective dose was reduced to 16.7% in the Sn 96 group, 11.1% in Sn64, and 5.5% in Sn32 (P<0.001). Noise values of Sn64ADM4 did not statistically differ from those in FDADM2 (45.7 vs. 38.9 Hounsfield units [HU]; P=0.132), whereas noise was significantly higher in Sn32ADM4 compared to Sn64ADM4 (61.5 HU; P<0.001). A Likert score >3 was reached in Sn64ADM4 regarding diagnostic confidence (3.2) and detectability of lung lesions (3.3). For detectability of most anatomical structures, no significant differences were found between FDAM2 and Sn64ADM4 (P≥0.05).ConclusionIn pediatric lung dual-source CT, spectral shaping together with ADMIRE 4 enable radiation dose reduction to about 10% of a full-dose protocol while maintaining an acceptable diagnostic quality.

Characterization of a prototype rapid kilovoltage x-ray image guidance system designed for a ring shape radiation therapy unit.

This study aims to characterize the performance of a prototype rapid kilovoltage (kV) x-ray image guidance system onboard the newly released Halcyon 2.0 linear accelerator (Varian Medical Systems, Palo Alto, CA) by use of conventional and innovatively designed testing procedures. Basic imaging system performance tests and radiation dose measurements were performed for all eleven kV-cone beam computed tomography (CBCT) imaging protocols available on a preclinical Halcyon 2.0 LINAC. Both conventional CBCT reconstruction using the Feldkamp-Davis-Kress (FDK) algorithm and a novel, advanced iterative reconstruction (iCBCT) available on this platform were evaluated. Standard image quality metrics, including slice thickness accuracy, high-contrast resolution, low-contrast resolution, regional uniformity and noise, and CT Hounsfield unit (HU) number accuracy and linearity were evaluated using a manufacturer-supplied QUART phantom (GmbH, Zorneding, Germany) and an independent image quality phantom (Catphan 500, The Phantom Laboratory, New York, NY). Due to the simplified design of the QUART phantom, we developed surrogate and clinically feasible strategies for measuring slice thickness and high- and low-contrast resolution. Imaging dose delivered by these eleven protocols was measured using a computed tomography dose index phantom and pencil chamber with commonly accepted methods and procedures. A subset of measurements were repeated on a conventional C-arm LINAC (TrueBeam and Trilogy, Varian Medical System) for comparison. Clinical patient images of pelvic and abdominal regions are also presented for qualitative assessment as part of a feasibility study for clinical implementation. Image acquisition time was 17-42s on the Halcyon system compared with 60s on the C-arm LINAC systems. The kV imager projection offset, imaging and treatment isocenter coincidence and the couch three-dimensional match movement all achieved less than1mm mechanical accuracy. All major image quality metrics were within either the national guideline or vendor-recommended tolerances. The designed surrogate approach with the QUART phantom showed a range of 0.24-0.35cycles/mm for spatial resolution, a contrast-to-noise ratio (CNR) of 2-20 for FDK reconstruction and a tolerance of 0.5mm for slice thickness. Other metrics derived from the Catphan images obtained on the Halcyon and C-arm LINACs showed comparable values for the FDK reconstruction. The iterative reconstruction tended to reduce noise, as evidenced by a higher CNR ratio. The fast scan pelvis protocols for Halcyon resulted in 50% lower dose compared to the standard scans, and the thorax fast protocol similarly delivered 10% lower dose than the standard thoracic scan. Preliminary patient images indicated that rapid kV CBCT with breath-hold is feasible, with improved imaging quality compared to free-breathing scans. Independent and comprehensive characterization of the kV imaging guidance system on the Halcyon 2.0 system demonstrated acceptable image quality for clinical use. The imaging unit onboard the Halcyon meets vendor specifications and satisfies requirements for routine clinical use. The fast kV imaging system enables the potential for volumetric CBCT acquisition during a single breath-hold and the iterative reconstruction tends to reduce the noise therefore has the potential to improve the CNR for normal size patient.

Impact of advanced detector technology and iterative reconstruction on low-dose quantitative assessment of lung computed tomography density in a biological lung model.

Quantitative computed tomography (QCT)-derived measures of lung density are valued methods for objectively characterizing lung parenchymal and peripheral airways disease and are being used in a growing number of lung disease focused trials. Detector and reconstruction improvements in CT technology have allowed for significant radiation dose reduction in image acquisition with comparable qualitative image quality. We report the impact of detector type and reconstruction type on QCT lung density measures in relation to decreasing dose indices. Two sets of studies were completed in an in vivo pig model with a SOMATOM Definition Flash CT system: (a) prior to system upgrade with conventional detectors (UFC) and filtered back projection (FBP), and (b) post system upgrade with integrated electronic detectors (STELLAR) and iterative reconstruction (SAFIRE). CT data were acquired across estimated CT volume dose indices (CTDIvol ) ranging from 0.75 to 15 mGy at both inspiratory and expiratory breath holds. Semiautomated lung segmentations allowed calculation of histogram median, kurtosis, and 15th percentile. Percentage of voxels below -910 HU and -950 HU (inspiratory), and -856 HU (expiratory) were also examined. The changes in these QCT metrics from dose reduction (15 mGy down to 0.75 mGy) were calculated relative to paired reference values (15 mGy). Results were compared based on detector and reconstruction type. In this study, STELLAR detectors improved concordance with 15 mGy values down to 3 mGy for inspiratory scans and 6 mGy for expiratory scans. The addition of SAFIRE reconstruction in all acquired measurements resulted in minimal deviation from reference values at 0.75 mGy. The use of STELLAR integrated electronic detectors and SAFIRE iterative reconstruction may allow for comparable lung density measures with CT dose indices down to 0.75 mGy.