Standard Filtered Back Projection Research Articles

Direct inversion of the Longitudinal ray transform for 2D residual elastic strain fields

We examine the problem of Bragg-edge elastic strain tomography from energy resolved neutron transmission imaging. A new approach is developed for two-dimensional plane-stress and plane-strain systems whereby elastic strain can be reconstructed from its Longitudinal ray transform (LRT) as two parts of a Helmholtz decomposition based on the concept of an Airy stress potential. The solenoidal component of this decomposition is reconstructed using an inversion formula based on a tensor filtered back projection (FBP) algorithm whereas the potential part can be recovered using either Hooke’s law or a finite element model of the elastic system. The technique is demonstrated for two-dimensional plane-stress systems in both simulation, and on real experimental data. We also demonstrate that application of the standard scalar FBP algorithm to the LRT in these systems recovers the trace of the solenoidal component of strain and we provide physical meaning for this quantity in the case of 2D plane-stress and plane-strain systems.

Directional TV algorithm for fast EPR imaging

Precise radiation guided by oxygen images has demonstrated superiority over the traditional radiation methods. Electron paramagnetic resonance (EPR) imaging has proven to be the most advanced oxygen imaging modality. However, the main drawback of EPR imaging is the long scan time. For each projection, we usually need to collect the projection many times and then average them to achieve high signal-to-noise ratio (SNR). One approach to fast scan is to reduce the repeating time for each projection. While the projections would be noisy and thus the traditional commonly-use filtered backprojection (FBP) algorithm would not be capable of accurately reconstructing images. Optimization-based iterative algorithms may accurately reconstruct images from noisy projections for they may incorporate prior information into optimization models. Based on the total variation (TV) algorithms for EPR imaging, in this work, we propose a directional TV (DTV) algorithm to further improve the reconstruction accuracy. We construct the DTV constrained, data divergence minimization (DTVcDM) model, derive its Chambolle–Pock (CP) solving algorithm, validate the correctness of the whole algorithm, and perform evaluations via simulated and real data. The experimental results show that the DTV algorithm outperforms the existing TV and FBP algorithms in fast EPR imaging. Compared to the standard FBP algorithm, the proposed algorithm may achieve 10 times of acceleration.

Comparison of Image Quality and Radiation Dose between Filtered Back Projection and Sinogram Affirmed Iterative Reconstruction in CT Head: A Cross-sectional Study

Introduction: Computed Tomography (CT) scans account for only 15% of total diagnostic procedures but for over half of the collective dose of radiation. The increasing awareness about the harmful effects of radiation has also created a need for developing techniques that decrease radiation exposure while at the same time providing a reasonably good image quality. The majority of the CT units today use Filtered Back Projection (FBP) as the means of image reconstruction. However, FBP leads to coupling between image noise and radiation exposure and limits the dose reduction possible while providing diagnostic quality images. Second-generation Iterative Reconstruction (IR) techniques have caught the attention of medical researchers because they provide a superior image quality than FBP at the same radiation dose showing potential for dose reduction. Aim: To compare the image quality and radiation dose in standard dose CT head reconstructed with FBP and low dose CT head reconstructed with Sinogram Affirmed Iterative Reconstruction (SAFIRE) and FBP both. Materials and Methods: A cross-sectional analytical study was conducted on 100 patients in the emergency department of Vardhman Mahavir Medical College and Safdarjung Hospital in India from November 2018 to April 2020. Patients referred for emergency Non-contrast Computed Tomography (NCCT) head for any indication excluding post-op cases and those with metallic artifacts were included. Fifty patients chosen using systematic random sampling underwent low-dose CT head using the Care Dose 4D Automatic Exposure Control (AEC) system with reconstruction using SAFIRE (Group-A) and FBP (Group-B). Another 50 patients who were within five years of the corresponding Group-A and B patients underwent standard dose head CT reconstruction using FBP (Group-C). CTDI vol, DLP, and effective dose were recorded in all patients. Image quality was assessed objectively using the unpaired t-test for Group-A and C and paired t-test for Group-A and B. Subjective image analysis between the groups was done using a 4-point Likert scale. Results: Image quality parameters were found to be better in Group-A compared to Group-C (p<0.05). The mean values in low dose SAFIRE group, low dose FBP group, and standard dose FBP group were as follows: Grey Matter (GM) Signal-to-Noise Ratio (SNR) (14±3.29, 8.96±2 and 9.24±1.96), White Matter (WM) SNR (14.6 ±3.73, 6.9±3.79 and 7.11±1.68), Cerebrospinal Fluid (CSF) SNR (1.65±1.12, 0.86±0.80 and 1.08±0.61) and Contrastto-Noise Ratio (CNR) (3.06±0.94, 1.81±0.69 and 1.74±0.69), respectively. Group-A had significantly improved image quality parameters than Group-B and Group-C. Radiation dose reduction of 42%, 39.34%, and 42.5% was achieved in CTDIvol, DLP, and effective dose respectively in low dose group. Conclusion: Low dose CT head reconstructed with SAFIRE were significantly better in image quality compared to standarddose CT head images reconstructed using FBP, while allowing for up to 42% reduction in dose.

Simulation-Assisted Augmentation of Missing Wedge and Region-of-Interest Computed Tomography Data.

This study reports a strategy to use sophisticated, realistic X-ray Computed Tomography (CT) simulations to reduce Missing Wedge (MW) and Region-of-Interest (RoI) artifacts in FBP (Filtered Back-Projection) reconstructions. A 3D model of the object is used to simulate the projections that include the missing information inside the MW and outside the RoI. Such information augments the experimental projections, thereby drastically improving the reconstruction results. An X-ray CT dataset of a selected object is modified to mimic various degrees of RoI and MW problems. The results are evaluated in comparison to a standard FBP reconstruction of the complete dataset. In all cases, the reconstruction quality is significantly improved. Small inclusions present in the scanned object are better localized and quantified. The proposed method has the potential to improve the results of any CT reconstruction algorithm.

Implementation of dual-energy material decomposition technique in stationary CT baggage scanner withπ-angle sparsity for enhancing threat detection

Two-dimensional X-ray inspection systems are widely used in aviation security applications; however, they have inherent limitations in recognizing the three-dimensional (3D) shapes of hidden objects. Therefore, there is a growing demand for the implementation of advanced 3D X-ray inspection systems at airports for more accurate detection of threats in luggage and personal belongings. In this study, we designed a new stationary computed tomography (CT) baggage scanner with π-angle sparsity (i.e., 20 pairs of X-ray sources and line detectors were placed within a scan angle of 180°) and compressed sensing (CS)-based reconstruction, and implemented a dual-energy material decomposition (DEMD) technique in the proposed system to separate soft and dense materials of an examined object to enhance threat detection. To validate the efficacy of the proposed approach (CS/180°/P20), we conducted a feasibility study using numerical simulation before its practical implementation. Polychromatic projections were emulated at X-ray tube voltages of 60 and 140 kVp, and DEMD was applied to the projections prior to CT reconstruction. Conventional and dual-energy CT images were reconstructed using both standard filtered-backprojection (FBP) and state-of-the-art CS-based algorithms to compare the image quality. According to our simulation results, the CS-reconstructed images were almost unaffected by the clearly evident streak artifacts on the FBP-reconstructed images because of the use of 20 extreme sparse-view projections, and the image quality of the dual-energy CT images obtained using the proposed CT configuration was similar to that obtained using the conventional CT configuration with 720 dense projections, indicating the efficacy of the proposed approach. Consequently, high-quality dual-energy CT images of soft and dense materials were successfully obtained using the proposed stationary CT configuration.

Filtered back projection vs. iterative reconstruction for CBCT: effects on image noise and processing time.

To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR). CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05. The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s. IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.

Physical characteristics of deep learning-based image processing software in computed tomography: a phantom study.

This study aimed to assess the image characteristics of deep-learning-based image processing software (DLIP; FCT PixelShine, FUJIFILM, Tokyo, Japan) and compare it with filtered back projection (FBP), model-based iterative reconstruction (MBIR), and deep-learning-based reconstruction (DLR). This phantom study assessed the object-specific spatial resolution (task-based transfer function [TTF]), noise characteristics (noise power spectrum [NPS]), and low-contrast detectability (low-contrast object-specific contrast-to-noise ratio [CNRLO]) at three different output doses (standard: 10mGy; low: 3.9mGy; ultralow: 2.0mGy). The processing strength of DLIPFBP with A1, A4, and A9 was compared with those of FBP, MBIR, and DLR. The standard dose with high-contrast TTFs of DLIPFBP exceeded that of FBP. Low-contrast TTFs were comparable to or lower than that of FBP. The NPS peak frequency (fP) of DLIPFBP shifts to low spatial frequencies of up to 8.6% at ultralow doses compared to the standard FBP dose. MBIR shifted the most fP compared to FBP-a marked shift of up to 49%. DLIPFBP showed a CNRLO equal to or greater than that of DLR in standard or low doses. In contrast, the CNRLO of the DLIPFBP was equal to or lower than that of the DLR in ultralow doses. DLIPFBP reduced image noise while maintaining a resolution similar to commercially available MBIR and DLR. The slight spatial frequency shift of fP in DLIPFBP contributed to the noise texture degradation suppression. The NPS suppression in the low spatial frequency range effectively improved the low-contrast detectability.

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques.

The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques. To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies. In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones. The real volume of each piece was measured using the water displacement method. In the in vivo study, 100 patients (84 men; mean age = 71.2 ± 8.7 years) underwent CAC scoring with a tube voltage of 120 kVp and image thickness of 3 mm. The image reconstruction was done with FBP, hybrid IR, and three levels of DLR including mild (DLRmild), standard (DLRstd), and strong (DLRstr). In the in vitro study, the calcium volume was equivalent (P = 0.949) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. In the in vivo study, the image noise was significantly lower in images that used DLRstr-based reconstruction, when compared images other reconstructions (P < 0.001). There were no significant differences in the calcium volume (P = 0.987) and Agatston score (P = 0.991) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. The highest overall agreement of Agatston scores was found in the DLR groups (98%) and hybrid IR (95%) when compared to standard FBP reconstruction. The DLRstr presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.

Impact of deep learning image reconstructions (DLIR) on coronary artery calcium quantification

BackgroundDeep learning image reconstructions (DLIR) have been recently introduced as an alternative to filtered back projection (FBP) and iterative reconstruction (IR) algorithms for computed tomography (CT) image reconstruction. The aim of this study was to evaluate the effect of DLIR on image quality and quantification of coronary artery calcium (CAC) in comparison to FBP.MethodsOne hundred patients were consecutively enrolled. Image quality–associated variables (noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) as well as CAC-derived parameters (Agatston score, mass, and volume) were calculated from images reconstructed by using FBP and three different strengths of DLIR (low (DLIR_L), medium (DLIR_M), and high (DLIR_H)). Patients were stratified into 4 risk categories according to the Coronary Artery Calcium - Data and Reporting System (CAC-DRS) classification: 0 Agatston score (very low risk), 1–99 Agatston score (mildly increased risk), Agatston 100–299 (moderately increased risk), and ≥ 300 Agatston score (moderately-to-severely increased risk).ResultsIn comparison to standard FBP, increasing strength of DLIR was associated with a significant and progressive decrease of image noise (p < 0.001) alongside a significant and progressive increase of both SNR and CNR (p < 0.001). The use of incremental levels of DLIR was associated with a significant decrease of Agatston CAC score and CAC volume (p < 0.001), while mass score remained unchanged when compared to FBP (p = 0.232). The underestimation of Agatston CAC led to a CAC-DRS misclassification rate of 8%.ConclusionDLIR systematically underestimates Agatston CAC score. Therefore, DLIR should be used cautiously for cardiovascular risk assessment.Key Points• In coronary artery calcium imaging, the implementation of deep learning image reconstructions improves image quality, by decreasing the level of image noise.• Deep learning image reconstructions systematically underestimate Agatston coronary artery calcium score.• Deep learning image reconstructions should be used cautiously in clinical routine to measure Agatston coronary artery calcium score for cardiovascular risk assessment.

A truth-based primal-dual learning approach to reconstruct CT images utilizing the virtual imaging trial platform.

Inherent to Computed tomography (CT) is image reconstruction, constructing 3D voxel values from noisy projection data. Modeling this inverse operation is not straightforward. Given the ill-posed nature of inverse problem in CT reconstruction, data-driven methods need regularization to enhance the accuracy of the reconstructed images. Besides, generalization of the results hinges upon the availability of large training datasets with access to ground truth. This paper offers a new strategy to reconstruct CT images with the advantage of ground truth accessible through a virtual imaging trial (VIT) platform. A learned primal-dual deep neural network (LPD-DNN) employed the forward model and its adjoint as a surrogate of the imaging's geometry and physics. VIT offered simulated CT projections paired with ground truth labels from anthropomorphic human models without image noise and resolution degradation. The models included a library of anthropomorphic, computational patient models (XCAT). The DukeSim simulator was utilized to form realistic projection data emulating the impact of the physics and geometry of a commercial-equivalent CT scanner. The resultant noisy sinogram data associated with each slice was thus generated for training. Corresponding linear attenuation coefficients of phantoms' materials at the effective energy of the x-ray spectrum were used as the ground truth labels. The LPD-DNN was deployed to learn the complex operators and hyper-parameters in the proximal primal-dual optimization. The obtained validation results showed a 12% normalized root mean square error with respect to the ground truth labels, a peak signal-to-noise ratio of 32 dB, a signal-to-noise ratio of 1.5, and a structural similarity index of 96%. These results were highly favorable compared to standard filtered-back projection reconstruction (65%, 17 dB, 1.0, 26%).

Deep learning enabled ultra-fast-pitch acquisition in clinical X-ray computed tomography.

In X-raycomputed tomography (CT), many important clinical applications may benefit from a fast acquisition speed. The helical scan is the most widely used acquisition mode in clinical CT, where a fast helical pitch can improve the acquisition speed. However, on a typical single-source helical CT (SSCT) system, the helical pitch p typically cannot exceed 1.5; otherwise, reconstruction artifacts will result from data insufficiency. The purpose of this work is to develop a deep convolutional neural network (CNN) to correct for artifacts caused by an ultra-fast pitch, which can enable faster acquisition speed than what is currently achievable. A customized CNN (denoted as ultra-fast-pitch network (UFP-net)) was developed to restore the underlying anatomical structure from the artifact-corrupted post-reconstruction data acquired from SSCT with ultra-fast pitch (i.e., p ≥ 2). UFP-net employed residual learning to capture the features of image artifacts. UFP-net further deployed in-house-customized functional blocks with spatial-domain local operators and frequency-domain non-local operators, to explore multi-scale feature representation. Images of contrast-enhanced patient exams (n=83) with routine pitch setting (i.e., p<1) were retrospectively collected, which were used as training and testing datasets. This patient cohort involved CT exams over different scan ranges of anatomy (chest, abdomen, and pelvis) and CT systems (Siemens Definition, Definition Flash, Definition AS+, Siemens Healthcare, Inc.), and the corresponding base CT scanning protocols used consistent settings of major scan parameters (e.g., collimation and pitch). Forward projection of the original images was calculated to synthesize helical CT scans with one regular pitch setting (p=1) and two ultra-fast-pitch setting (p=2 and 3). All patient images were reconstructed using the standard filtered-back-projection (FBP) algorithm. A customized multi-stage training scheme was developed to incrementally optimize the parameters of UFP-net, using ultra-fast-pitch images as network inputs and regular pitch images as labels. Visual inspection was conducted to evaluate image quality. Structural similarity index (SSIM) and relative root-mean-square error (rRMSE) were used as quantitative quality metrics. The UFP-net dramatically improved image quality over standard FBP at both ultra-fast-pitch settings. At p=2, UFP-net yielded higher mean SSIM (>0.98) with lower mean rRMSE (<2.9%), compared to FBP (mean SSIM<0.93; mean rRMSE>9.1%). Quantitative metrics at p=3: UFP-net-mean SSIM [0.86, 0.94] and mean rRMSE [5.0%, 8.2%]; FBP-mean SSIM [0.36, 0.61] and mean rRMSE [36.0%, 58.6%]. The proposed UFP-net has the potential to enable ultra-fast data acquisition in clinical CT without sacrificing image quality. This method has demonstrated reasonable generalizability over different body parts when the corresponding CT exams involved consistent base scan parameters.

Effect of Different Iterative Reconstruction Algorithms on Ultra-Low Dose CT of Inflammatory Bowel Disease in a Rabbit Model

Purpose: To evaluate the feasibility of ultra-low dose 80 kVp CT for the evaluation of inflammatory bowel disease (IBD) in a rabbit model and to investigate the effect of two different iterative reconstruction (IR) algorithms on 80 kVp CT in terms of image quality and diagnostic performance in comparison with same session conventional 120 kVp images.Materials and Methods: This study was approved by the Animal Care and Use Committee of our hospital. Twenty-eight New Zealand white rabbits were randomly divided into two groups: IBD group (n=18) and a control group (n=10). To create an acute IBD model, 3mL of a 5% w/v tri-nitrobenzene sulfonic acid solution was administered to the sigmoid colon of the rabbits. CT was performed at 80 kVp and 120 kVp and was reconstructed using filtered back projection (FBP), hybrid statistic-based IR, and full IR algorithms for 80 kVp and using FBP only for 120 kVp. Effective radiation dose, image noise, image quality, and diagnostic performance by two reviewers were recorded and compared using repeated measure analysis of variance, McNemar test, and receiver operating curve (ROC) analysis.Results: Mean effective radiation dose of 80 kVp CT (0.05 mSv) was significantly lower than that (0.285 mSv) of 120 kVp CT. Mean image noise was highest in the 80 kVp FBP setting (60.36) but significantly decreased with IR algorithms (47.02 with hybrid IR and 12.92 with full IR) (P&lt;0.0001). Mean overall image quality score was lowest in the 80 kVp FBP setting (1.57 and 1.46 for reviewers 1 and 2, respectively) but significantly improved with IR algorithms (2.43 and 2.25 for hybrid IR and 4.79 and 4.93 for full IR) (P&lt;0.0001). Sensitivity and area under the curve (AUC) for differentiating a normal bowel from IBD was lowest with 80 kVp FBP (61.1% and 83.3%; 0.883 and 0.967) but improved with IR algorithms (83.3%~100%; 0.992~1), to similar levels as the 120 kVp FBP setting (100% and 1). Differences in sensitivity and AUC between 80 kVp FBP and 80 kVp IR algorithms were statistically significant in reviewer 1.Conclusions: Ultra-low dose 80 kVp CT in a rabbit IBD model is not feasible using the standard FBP algorithm. However, with the application of IR algorithms, diagnostic performance of 80 kVp CT was acceptable and on par with that at conventional 120 kVp FBP.

Diagnostic Quality and Radiation Dose for Pulmonary Embolism Protocol Computed Tomography: ASIR-V vs. Standard Filtered Back Projection

Diagnostic Quality and Radiation Dose for Pulmonary Embolism Protocol Computed Tomography: ASIR-V vs. Standard Filtered Back Projection

Application of sigmoidal optimization to reconstruct nuclear medicine image: Comparison with filtered back projection and iterative reconstruction method

High levels for noise and a loss of true signal make the quantitative interpretation of nuclear medicine (NM) images difficult. An application of profile optimization using a sigmoidal function in this study was used to acquire the NM images with high quality. And the images were acquired by using three kinds of reconstruction method using each same sinogram: a standard filtered back-projection (FBP), an iterative reconstruction (IR) technique, and the sigmoidal function profile optimization (SFPO). Comparison of image according to reconstruction method was performed to show a superiority of the SFPO for imaging. The images reconstructed by using the SFPO showed an average of 1.49 times and of 1.17 times better in contrast than the results obtained using the standard FBP and the IR technique, respectively. Higher signal to noise ratios were obtained as an average of 12.30 times and of 3.77 times than results obtained using the standard FBP and the IR technique, respectively. This study confirms that reconstruction with SFPO (vs FBP and vs IR) can lead to better lesion detectability and characterization with noise reduction. It can be developed for future reconstruction technique for the NM imaging.

Phase Contrast CT Enabled Three-Material Decomposition in Spectral CT Imaging.

Three-material decomposition is crucial for material quantification when more than two elemental materials, including a K-edge material, are presented in an image object. In principle, three-material decomposition requires a triple energy scan which cannot be directly accomplished using a conventional dual energy CT system. In this work, a new scheme to enable three-material decomposition by employing phase contrast CT was presented. When a grating interferometer is added, a conventional absorption dual energy CT system can be upgraded to a phase contrast dual energy CT system which provides an additional phase signal related to the real part of the refractive index of an image object, along with the absorption signal under two different x-ray spectra. In this work, a three-material decomposition method was proposed for the aforementioned dual energy phase contrast CT system. Physical experimental studies were performed on a benchtop x-ray Talbot-Lau interferometer system to validate the proposed method. A physical phantom, containing calcium and iodine inserts of known concentrations, was used as the image object. A rotation-rotation dual energy phase contrast CT scan was performed under 40 and 80 kVp tube potentials. For each view angle, a phase stepping procedure with five phase steps was performed. After the phase retrieval procedure and image reconstruction using the standard filtered-back projection, the solutions were decomposed into the calcium, iodine and water bases based on the proposed decomposition method. For all the solutions, the relative quantification errors of the concentrations were within 10%.

Stochastic Variance-Reduced Gradient Based Medical Image Restoration

Image reconstruction is an increasingly complex field in CT. Iterative Reconstruction (IR) is at present an adjunct to standard Filtered Back Projection (FBP) reconstruction, but could become a replacement for it. Because of its potential for filtering at lower radiation dosages, IR has gotten a ton of consideration in the medicinal writing and all sellers offer business arrangements. Its utilization in cardiovascular CT has been driven to some extent because of worries about radiation portion and picture quality. In this paper proposes a novel reconstruction algorithm for different medical image modalities and bring out their performance and utilization in various medical diagnostics. The performance parameters like efficiency, utility, noise characteristics, diagnostic values etc., these measured parameter values are compared with various existing reconstruction algorithm. Our novel algorithm Stochastic Variance-Reduced Gradient is mainly designed to improve the quality of an image and to bring their practical utility to medical practitioners. Various simulation studies with benchmark medical images will be carried out to highlight the utility of the algorithm in diagnostic and medical practice.

Assessment of potential reduction in multidetector computed tomography doses using FBP and SAFIRE for detection and measurement of the position of the inferior alveolar canal.

The objective was to identify the lowest doses required to detect and measure the position of the inferior alveolar canal (IAC) on multidetector computed tomography (MDCT) images using filtered backprojection (FBP) and sinogram-affirmed iterative reconstructions (SAFIRE) 3 and SAFIRE 5. Four cadaveric mandibles were imaged using a reference protocol with standard dose and FBP and 3 ultra-low-dose protocols (LD1-LD3), using an MDCT scanner. All test examinations were reconstructed with FBP, SAFIRE 3, and SAFIRE 5. Subjective visibility of the IAC in the images and digital measurements of the height of the ridge above the IAC were recorded from test images and compared with those from the reference image using one-sample t tests, Bland-Altman plots, and linear regression. Subjective visibility comparable to the standard protocol was obtained with an 84.6% dose reduction using the LD2 protocol. No statistically significant difference was found between the height measurements from the reference protocol and any of the LD1 and LD2 protocols. The t tests indicated a significant difference between the measurements from the reference and all LD3 test protocols. SAFIRE did not have an advantage over FBP images. Significant dose reduction from the reference dose can allow adequate detection and measurements of the IAC.

Artifact Reduction in the Diagnosis of Vasospasm in Computed Tomographic Perfusion: Potential of Iterative Metal Artifact Reduction.

This study aimed to analyze the possibility of artifact reduction using a new iterative metal artifact reduction algorithm (iMAR) in the diagnosis of perfusion deficits due to vasospasms and to evaluate its clinical relevance. Sixty-one volume perfusion computed tomographies of 24 patients after coiling or aneurysm clipping were reconstructed using standard-filtered back-projection and iMAR retrospectively. The degree of artifacts was evaluated as well as the size of the nonevaluable area. Diagnostic performance was evaluated compared with digital subtraction angiography. Artifacts were present in 39 of 61 volume perfusion computed tomography examinations. Image quality (score, 1.0 vs 1.6; P < 0.01) was higher and the size of the signal loss was reduced significantly by iMAR (intracranial metal artifacts, 887 mm vs 359 mm [P < 0.01]; cranial bolt, 3008 mm vs 837 mm [P < 0.01]). Digital subtraction angiography confirmed vasospasms in 11 (92%) of 12 patients. The iMAR yields higher image quality by reducing artifacts compared with filtered back-projection.

Improved visualization of the coronary arteries using motion correction during vasodilator stress CT myocardial perfusion imaging

BackgroundVasodilator stress computed tomography perfusion (sCTP) imaging is complementary to coronary CT angiography (CCTA), used to determine the hemodynamic significance of coronary artery disease. However, it requires a separate image acquisition due to motion artifacts caused by higher heart rates during stress, resulting in increased iodine contrast dose and radiation. We sought to determine whether a novel motion correction algorithm applied to stress images would improve the visualization of the coronary arteries to potentially allow CCTA + sCTP evaluation in a single scan. Methods28 patients referred for clinically indicated CCTA (iCT, Philips) underwent sCTP imaging (retrospective-gating with dose modulation; 100 kVp and 250 mA; 5.2 ± 4.3 mSv) after regadenoson (0.4 mg, Astellas). Stress images were reconstructed using standard filtered back-projection (FBP) and also processed to generate interaction-free coronary motion-compensated back-projection reconstructions (MCR). Each coronary artery from standard FBP and MCR images was viewed side-by-side by a reader blinded to the reconstruction technique, who graded severity of motion artifact by segment (scale 0–5, with 3 as the threshold for diagnostic quality) and to measure signal-to-noise and contrast-to-noise ratios (SNR, CNR). ResultsVisualization scores were higher with MCR for all coronary segments, including 14/86 (16%) segments deemed as non-diagnostic on FBP images. SNR (7 ± 2) and CNR (15 ± 8) were unchanged by motion-correction (7 ± 3, p = 0.88 and 15 ± 5, p = 0.94, respectively). ConclusionsMCR improves the visualization of coronary anatomy on sCTP images without degrading image characteristics. This algorithm is an important step towards the combined assessment of coronary anatomy and myocardial perfusion in a single scan, which will reduce study time, radiation exposure and contrast dose.

Effect of Statistically Iterative Image Reconstruction on Vertebral Bone Strength Prediction Using Bone Mineral Density and Finite Element Modeling: A Preliminary Study.

Statistical iterative reconstruction (SIR) using multidetector computed tomography (MDCT) is a promising alternative to standard filtered back projection (FBP), because of lower noise generation while maintaining image quality. Hence, we investigated the feasibility of SIR in predicting MDCT-based bone mineral density (BMD) and vertebral bone strength from finite element (FE) analysis. The BMD and FE-predicted bone strength derived from MDCT images reconstructed using standard FBP (FFBP) and SIR with (FSIR) and without regularization (FSIRB0) were validated against experimental failure loads (Fexp). Statistical iterative reconstruction produced the best quality images with regard to noise, signal-to-noise ratio, and contrast-to-noise ratio. Fexp significantly correlated with FFBP, FSIR, and FSIRB0. FFBP had a significant correlation with FSIRB0 and FSIR. The BMD derived from FBP, SIRB0, and SIR were significantly correlated. Effects of regularization should be further investigated with FE and BMD analysis to allow for an optimal iterative reconstruction algorithm to be implemented in an in vivo scenario.