Statistical Iterative Reconstruction Research Articles

Accuracy of computer-aided design models of the jaws produced using ultra-low MDCT doses and ASIR and MBIR

PurposeTo compare the surface of computer-aided design (CAD) models of the maxilla produced using ultra-low MDCT doses combined with filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) reconstruction techniques with that produced from a standard dose/FBP protocol.MethodsA cadaveric completely edentulous maxilla was imaged using a standard dose protocol (CTDIvol: 29.4 mGy) and FBP, in addition to 5 low dose test protocols (LD1-5) (CTDIvol: 4.19, 2.64, 0.99, 0.53, and 0.29 mGy) reconstructed with FBP, ASIR 50, ASIR 100, and MBIR. A CAD model from each test protocol was superimposed onto the reference model using the ‘Best Fit Alignment’ function. Differences between the test and reference models were analyzed as maximum and mean deviations, and root-mean-square of the deviations, and color-coded models were obtained which demonstrated the location, magnitude and direction of the deviations.ResultsBased upon the magnitude, size, and distribution of areas of deviations, CAD models from the following protocols were comparable to the reference model: FBP/LD1; ASIR 50/LD1 and LD2; ASIR 100/LD1, LD2, and LD3; MBIR/LD1. The following protocols demonstrated deviations mostly between 1–2 mm or under 1 mm but over large areas, and so their effect on surgical guide accuracy is questionable: FBP/LD2; MBIR/LD2, LD3, LD4, and LD5. The following protocols demonstrated large deviations over large areas and therefore were not comparable to the reference model: FBP/LD3, LD4, and LD5; ASIR 50/LD3, LD4, and LD5; ASIR 100/LD4, and LD5.ConclusionsWhen MDCT is used for CAD models of the jaws, dose reductions of 86% may be possible with FBP, 91% with ASIR 50, and 97% with ASIR 100. Analysis of the stability and accuracy of CAD/CAM surgical guides as directly related to the jaws is needed to confirm the results.

Statistical Iterative CBCT Reconstruction Based on Neural Network.

Cone-beam computed tomography (CBCT) plays an important role in radiation therapy. Statistical iterative reconstruction (SIR) algorithms with specially designed penalty terms provide good performance for low-dose CBCT imaging. Among others, the total variation (TV) penalty is the current state-of-the-art in removing noises and preserving edges, but one of its well-known limitations is its staircase effect. Recently, various penalty terms with higher order differential operators were proposed to replace the TV penalty to avoid the staircase effect, at the cost of slightly blurring object edges. We developed a novel SIR algorithm using a neural network for CBCT reconstruction. We used a data-driven method to learn the "potential regularization term" rather than design a penalty term manually. This approach converts the problem of designing a penalty term in the traditional statistical iterative framework to designing and training a suitable neural network for CBCT reconstruction. We proposed using transfer learning to overcome the data deficiency problem and an iterative deblurring approach specially designed for the CBCT iterative reconstruction process during which the noise level and resolution of the reconstructed images may change. Through experiments conducted on two physical phantoms, two simulation digital phantoms, and patient data, we demonstrated the excellent performance of the proposed network-based SIR for CBCT reconstruction, both visually and quantitatively. Our proposed method can overcome the staircase effect, preserve both edges and regions with smooth intensity transition, and provide reconstruction results at high resolution and low noise level.

Promote quantitative ischemia imaging via myocardial perfusion CT iterative reconstruction with tensor total generalized variation regularization

Myocardial perfusion computed tomography (MPCT) imaging is commonly used to detect myocardial ischemia quantitatively. A limitation in MPCT is that an additional radiation dose is required compared to unenhanced CT due to its repeated dynamic data acquisition. Meanwhile, noise and streak artifacts in low-dose cases are the main factors that degrade the accuracy of quantifying myocardial ischemia and hamper the diagnostic utility of the filtered backprojection reconstructed MPCT images. Moreover, it is noted that the MPCT images are composed of a series of 2/3D images, which can be naturally regarded as a 3/4-order tensor, and the MPCT images are globally correlated along time and are sparse across space. To obtain higher fidelity ischemia from low-dose MPCT acquisitions quantitatively, we propose a robust statistical iterative MPCT image reconstruction algorithm by incorporating tensor total generalized variation (TTGV) regularization into a penalized weighted least-squares framework. Specifically, the TTGV regularization fuses the spatial correlation of the myocardial structure and the temporal continuation of the contrast agent intake during the perfusion. Then, an efficient iterative strategy is developed for the objective function optimization. Comprehensive evaluations have been conducted on a digital XCAT phantom and a preclinical porcine dataset regarding the accuracy of the reconstructed MPCT images, the quantitative differentiation of ischemia and the algorithm’s robustness and efficiency.

Low-dose CT image reconstruction using gain intervention-based dictionary learning

Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research has focused on the radiation exposure distributed to patients through CT investigations. Therefore, low-dose CT has become a significant research area. Many researchers have proposed different low-dose CT reconstruction techniques. But, these techniques suffer from various issues such as over smoothing, artifacts, noise, etc. Therefore, in this paper, we have proposed a novel integrated low-dose CT reconstruction technique. The proposed technique utilizes global dictionary-based statistical iterative reconstruction (GDSIR) and adaptive dictionary-based statistical iterative reconstruction (ADSIR)-based reconstruction techniques. In case the dictionary (D) is predetermined, then GDSIR can be used and if D is adaptively defined then ADSIR is appropriate choice. The gain intervention-based filter is also used as a post-processing technique for removing the artifacts from low-dose CT reconstructed images. Experiments have been done by considering the proposed and other low-dose CT reconstruction techniques on well-known benchmark CT images. Extensive experiments have shown that the proposed technique outperforms the available approaches.

Evaluation of the Quality of CT Images Acquired with Smart Metal Artifact Reduction Software.

ObjectiveTo evaluate the practical effectiveness of smart metal artifact reduction (SMAR) in reducing artifacts caused by metallic implants.MethodsPatients with metal implants underwent computed tomography (CT) examinations on high definition CT scanner, and the data were reconstructed with adaptive statistical iterative reconstruction (ASiR) with value weighted to 40% and smart metal artifact reduction (SMAR) technology. The comparison was assessed by both subjective and objective assessment between the two groups of images. In terms of subjective assessment, three radiologists evaluated image quality and assigned a score for visualization of anatomic structures in the critical areas of interest. Objectively, the absolute CT value of the difference (ΔCT) and artifacts index (AI) were adopted in this study for the quantitative assessment of metal artifacts.ResultsIn subjective image quality assessment, three radiologists scored SMAR images higher than 40% ASiR images (P<0.01) and the result suggested that visualization of critical anatomic structures around the region of the metal object was significantly improved by using SMAR compared with 40% ASiR. The ΔCT and AI for quantitative assessment of metal artifacts showed that SMAR appeared to be superior for reducing metal artifacts (P<0.05) and indicated that this technical approach was more effective in improving the quality of CT images.ConclusionA variety of hardware (dental filling, embolization coil, instrumented spine, hip implant, knee implant) are processed with the SMAR algorithm to demonstrate good recovery of soft tissue around the metal. This artifact reduction allows for the clearer visualization of structures hidden underneath.

Automatic spectral imaging protocol and iterative reconstruction for radiation dose reduction in typical hepatic hemangioma computed tomography with reduced iodine load: a preliminary study.

To evaluate the effect of automatic spectral imaging protocol selection (ASIS) and adaptive statistical iterative reconstruction (ASiR) technique in the reduction of radiation and contrast medium dose in typical hepatic hemangioma (HH) dualenergy spectral CT (DEsCT). 62 patients with suspected HH were randomly divided into two groups equally: Group A, conventional 120-kVp CT with standard iodine load; Group B, DEsCT with ASIS technique and reduced iodine load, two sets of monochromatic spectral images were reconstructed: 69keV level with 30% ASiR (Group B1) and 52keV level with 50%ASiR (Group B2). The radiation and total iodine dose, quantitative analysis (standard deviation value, contrast-to-noise and contrast enhancement ratio) and qualitative analysis were evaluated. No difference was observed in the standard deviation values, subjective image noise, and the diagnostic acceptability score among the three groups (p > 0.05). Contrast to noise [Group B2 vs A, B1 in arterial phase(AP): 19.51 ± 6.29 vs 15.77 ± 5.93, 11.46 ± 2.84; Group B2 vs A, B1 in portal venous phase (PVP): 9.96 ± 2.18 vs 8.19 ± 3.04, 6.01 ± 1.82], contrast enhancement ratio (Group B2 vs A, B1 in AP: 6.88 ± 2.01 vs 5.47 ± 2.01, 4.15 ± 1.28; Group B2 vs A, B1 in PVP: 5.58 ± 1.02 vs 4.54 ± 1.13, 3.49 ± 0.83), and the lesion conspicuity score (Group B2 vs A, B1 in AP: 3.93 ± 0.26 vs 3.45 ± 0.51, 3.10 ± 0.49; Group B2 vs A, B1 in PVP: 3.90 ± 0.31 vs 3.48 ± 0.57, 3.14 ± 0.44) for Group B2 were higher than those in Group A and B1 (p < 0.05). Compared to Group A, the radiation dose and total iodine dose in Group B were reduced by30 and 41%, respectively (radiation dose in Group B vs A: 5.53 ± 1.59 vs 7.91±2.71 mSv; iodine dose in Group B vs A: 18.85 ± 2.88 vs 31.78±3.89 ml;p < 0.05). DEsCT with ASIS and ASiR technique can reduce the radiation dose without image quality degradation as compared to the conventional 120-kVp CT. The monochromatic spectral images at 52keV level with 50% ASiR allows the reduction in total iodine dose without deteriorating diagnostic performance. Advances in knowledge: ASIS combined with ASiR technique, by using monochromatic spectral images at 52keV level, represents a feasible imaging protocol to reduce the radiation and total iodine dose in assessment of typical HH.

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction.

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

CT pulmonary angiography using organ dose modulation with an iterative reconstruction algorithm and 3D Smart mA in different body mass indices: image quality and radiation dose.

To investigate the differences in imaging quality and radiation dose in CT pulmonary angiography (CTPA) by organ dose modulation and 3D Smart mA modulation in different body mass indices (BMIs) with an adaptive statistical iterative reconstruction (ASiR-V) algorithm. Three hundred female patients who underwent CTPA were equally divided into three groups: A (18.5kg/m2 ≦ BMI < 24.9kg/m2), B (24.9kg/m2 ≦ BMI < 29.9kg/m2) and C (29.9kg/m2 ≦ BMI≦ 34.9kg/m2). The groups were randomly subdivided into two subgroups (n = 50): A1-A2, B1-B2 and C1-C2. The patients in subgroups A1, B1 and C1 underwent organ dose modulation with the ASiR-V algorithm. The other patients underwent 3D Smart mA modulation. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of all images were calculated after CTPA. Images were then subjectively evaluated using a 5-point scale. The volume CT dose index and dose-length product (DLP) were recorded and their means calculated. The DLP was converted to the effective dose (ED). In group A, the SNR, CNR, and subjective image scores showed no statistical differences (P > 0.05). The ED in subgroup A1 was 33.36% lower than that in A2. In group B and C, the variables showed no significant differences between the subgroups B1 and B2 (P > 0.05), and the subgroups C1 and C2 (P > 0.05), respectively. The ED in subgroup B1 and C1 was 36.15 and 38.22% lower than that in B2 and C2, respectively. Using organ dose modulation and applying the ASiR-V algorithm can more effectively reduce the radiation dose in CTPA than in 3D Smart mA modulation, while maintaining image quality.

Multidetector Computed Tomography Imaging: Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure.

Multidetector computed tomography-based trabecular bone microstructure analysis ensures promising results in fracture risk prediction caused by osteoporosis. Because multidetector computed tomography is associated with high radiation exposure, its clinical routine use is limited. Hence, in this study, we investigated in 11 thoracic midvertebral specimens whether trabecular texture parameters are comparable derived from (1) images reconstructed using statistical iterative reconstruction (SIR) and filtered back projection as criterion standard at different exposures (80, 150, 220, and 500 mAs) and (2) from SIR-based sparse sampling projections (12.5%, 25%, 50%, and 100%) and equivalent exposures as criterion standard. Twenty-four texture features were computed, and those that showed similar values between (1) filtered back projection and SIR at the different exposure levels and (2) sparse sampling and equivalent exposures and reconstructed with SIR were identified. These parameters can be of equal value in determining trabecular bone microstructure with lower radiation exposure using sparse sampling and SIR.

Comparing Fourth Generation Statistical Iterative Reconstruction Technique to Standard Filtered Back Projection in Pediatric Head Computed Tomography Examinations.

Although advanced statistical iterative reconstruction (IR) techniques are valued in pediatric computed tomography (CT) imaging, there is little published data on how these techniques affect image quality and radiation dose in the pediatric population. This is particularly true in the context of pediatric head CT examinations. This study analyzed the differences in image quality and several standard metrics of radiation dose on multidetector pediatric head CT examinations performed using standard filtered back projection (FBP) with reconstructions using iDose, a fourth-generation statistical iterative reconstruction technique. Using a retrospective review of 282 pediatric head CT examinations, we compared how iDose fared against FBP for effects on several standard metrics of radiation dose and qualitative and quantitative assessment of image quality. Our assessment revealed that examinations obtained using low-dose protocols reconstructed using iDose, when compared with standard-dose examinations reconstructed using FBP, resulted in significant radiation dose reduction while performing equally or better in quantitative image quality parameters. For most qualitative image quality parameters, the iDose group demonstrated equal performance to standard filtered back technique with a few notable exceptions. In the parameter of image sharpness in the 1.5 to 7 year olds, iDose fared better than FBP. However, FBP outperformed iDose in the qualitative parameters of decreased image graininess/noise in patients older than 13 years, improved image sharpness in patients aged between 7 and 13 years, and improved visibility of small parts for those aged 7 to 13 years. We conclude that iDose is effective at allowing significant radiation dose reduction while maintaining or, rarely, even improving quantitative image quality compared with FBP in the setting of pediatric head CT examinations. However, for certain qualitative image quality parameters in older-aged children, the use of iDose resulted in a poorer performance compared with FBP.

The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma: A Phantom Study

This phantom study aimed to evaluate low-dose (LD) chest computed tomography (CT) protocols using model-based iterative reconstruction (MBIR) for diagnosing lung metastases in patients with sarcoma. An adult female anthropomorphic phantom was scanned with a 64-slice CT using four LD protocols and a standard-dose protocol. Absorbed organ doses were measured with 10 metal-oxide-semiconductor field-effect transistor dosimeters. Furthermore, Monte Carlo simulations were performed to estimate organ and effective doses. Image quality in terms of image noise, contrast, and resolution was measured from the CT images reconstructed with conventional filtered back projection, adaptive statistical iterative reconstruction, and MBIR algorithms. All the results were compared to the performance of the standard-dose protocol. Mean absorbed organ and effective doses were reduced by approximately 95% with the LD protocol (100-kVp tube voltage and a fixed 10-mA tube current) compared to the standard-dose protocol (120-kVp tube voltage and tube current modulation) while yielding an acceptable image quality for diagnosing round-shaped lung metastases. The effective doses ranged from 0.16 to 2.83 mSv in the studied protocols. The image noise, contrast, and resolution were maintained or improved when comparing the image quality of LD protocols using MBIR to the performance of the standard-dose chest CT protocol using filtered back projection. The small round-shaped lung metastases were delineated at levels comparable to the used protocols. Radiation exposure in patients can be reduced significantly by using LD chest CT protocols and MBIR algorithm while maintaining image quality for detecting round-shaped lung metastases.

A Third-Generation Adaptive Statistical Iterative Reconstruction Technique: Phantom Study of Image Noise, Spatial Resolution, Lesion Detectability, and Dose Reduction Potential.

The purpose of this study was to assess image noise, spatial resolution, lesion detectability, and the dose reduction potential of a proprietary third-generation adaptive statistical iterative reconstruction (ASIR-V) technique. A phantom representing five different body sizes (12-37 cm) and a contrast-detail phantom containing lesions of five low-contrast levels (5-20 HU) and three sizes (2-6 mm) were deployed. Both phantoms were scanned on a 256-MDCT scanner at six different radiation doses (1.25-10 mGy). Images were reconstructed with filtered back projection (FBP), ASIR-V with 50% blending with FBP (ASIR-V 50%), and ASIR-V without blending (ASIR-V 100%). In the first phantom, noise properties were assessed by noise power spectrum analysis. Spatial resolution properties were measured by use of task transfer functions for objects of different contrasts. Noise magnitude, noise texture, and resolution were compared between the three groups. In the second phantom, low-contrast detectability was assessed by nine human readers independently for each condition. The dose reduction potential of ASIR-V was estimated on the basis of a generalized linear statistical regression model. On average, image noise was reduced 37.3% with ASIR-V 50% and 71.5% with ASIR-V 100% compared with FBP. ASIR-V shifted the noise power spectrum toward lower frequencies compared with FBP. The spatial resolution of ASIR-V was equivalent or slightly superior to that of FBP, except for the low-contrast object, which had lower resolution. Lesion detection significantly increased with both ASIR-V levels (p = 0.001), with an estimated radiation dose reduction potential of 15% ± 5% (SD) for ASIR-V 50% and 31% ± 9% for ASIR-V 100%. ASIR-V reduced image noise and improved lesion detection compared with FBP and had potential for radiation dose reduction while preserving low-contrast detectability.

Tilted grating phase-contrast computed tomography using statistical iterative reconstruction

Grating-based phase-contrast computed tomography (GBPC-CT) enables increased soft tissue differentiation, but often suffers from streak artifacts when performing high-sensitivity GBPC-CT of biomedical samples. Current GBPC-CT setups consist of one-dimensional gratings and hence allow to measure only the differential phase-contrast (DPC) signal perpendicular to the direction of the grating lines. Having access to the full two-dimensional DPC signal can strongly reduce streak artefacts showing up as characteristic horizontal lines in the reconstructed images. GBPC-CT with gratings tilted by 45° around the optical axis, combining opposed projections, and reconstructing with filtered backprojection is one method to retrieve the full three-dimensional DPC signal. This approach improves the quality of the tomographic data as already demonstrated at a synchrotron facility. However, additional processing and interpolation is necessary, and the approach fails when dealing with cone-beam geometry setups. In this work, we employ the tilted grating configuration with a laboratory GBPC-CT setup with cone-beam geometry and use statistical iterative reconstruction (SIR) with a forward model accounting for diagonal grating alignment. Our results show a strong reduction of streak artefacts and significant increase in image quality. In contrast to the prior approach our proposed method can be used in a laboratory environment due to its cone-beam compatibility.

Determination of a suitable low‐dose abdominopelvic CT protocol using model‐based iterative reconstruction through cadaveric study

Cadaveric studies provide a means of safely assessing new technologies and optimizing scanning prior to clinical validation. Reducing radiation exposure in a clinical setting can entail incremental dose reductions to avoid missing important clinical findings. The use of cadavers allows assessment of the impact of more substantial dose reductions on image quality. Our aim was to identify a suitable low-dose abdominopelvic CT protocol for subsequent clinical validation. Five human cadavers were scanned at one conventional dose and three low-dose settings. All scans were reconstructed using three different reconstruction algorithms: filtered back projection (FBP), hybrid iterative reconstruction (60% FBP and 40% adaptive statistical iterative reconstruction (ASIR40)), and model-based iterative reconstruction (MBIR). Two readers rated the image quality both quantitatively and qualitatively. Model-based iterative reconstruction images had significantly better objective image noise and higher qualitative scores compared with both FBP and ASIR40 images at all dose levels. The greatest absolute noise reduction, between MBIR and FBP, of 34.3 HU (equating to a 68% reduction) was at the lowest dose level. MBIR reduced image noise and improved image quality even in CT images acquired with a mean radiation dose reduction of 62% compared with conventional dose studies reconstructed with ASIR40, with lower levels of objective image noise, superior diagnostic acceptability and contrast resolution, and comparable subjective image noise and streak artefact scores. This cadaveric study demonstrates that MBIR reduces image noise and improves image quality in abdominopelvic CT images acquired with dose reductions of up to 62%.

Low-dose CT with adaptive statistical iterative reconstruction for evaluation of urinary stone.

PurposeTo prospectively determine the diagnostic performance of low-dose CT (LDCT) with adaptive statistical iterative reconstruction (ASIR) technique for the detection of urinary stone disease.ResultsThe average DLP and ED was 408.16 ± 119.04 mGy and 6.12 ± 1.79 mSv in CDCT, and 138.19 ± 76.87 mGy and 2.07 ± 1.15 mSv in LDCT, respectively. The dose reduction rate of LDCT was nearly 66.1% for both DLP and ED (P < 0.05). LDCT–80% ASIR images showed great image quality (mean score = 4.09), which was similar to CDCT-FBP images (mean score = 4.17) (P > 0.05), but higher than LDCT-FBP images (mean score = 2.77) (P < 0.05).Materials and Methods70 consetutive patients with clinically suspected urolithiasis underwent non-enhanced CT. Followed by both conventional-dose CT (CDCT) and low-dose CT (LDCT) scans. Automatic tube current modulation (ATCM) scanning was used, with a noise index setting of 13 in CDCT and 25 in LDCT. Reconstructions were performed with filtered back projection (FBP) and different settings of adaptive statistical iterative reconstruction [ASIR(40%, 60%, 80%)]. Urinary calculi (size, location, number), image quality (scale 1–5), image noise (scale 1–3) and diagnostic confidence levels (scale 1–3) were evaluated and measured by two radiologists independently. Radiation dose was recorded by calculating dose length product (DLP) and effective dose (ED). Statistical analyses included Mann-Whitney U test and Paired t tests.ConclusionsLDCT with ASIR can reduce the radiation dose while maintain relatively high image quality in the diagnosis of urinary stone diseases.

Automatic spectral imaging protocol selection combined with iterative reconstruction can enhance image quality and decrease radiation and contrast dosage in abdominal CT angiography.

To investigate the effect of automatic spectral imaging protocol selection (ASIS) and adaptive statistical iterative reconstruction (ASIR) technology in reducing radiation and contrast dosage. Sixty-four patients were randomly divided into two groups for abdominal computed tomography (CT): the experiment group with ASIS plus 50% ASIR and the control with 120kVp voltage. The CT dose-index volume decreased by 23.68 and 23.57% and the dose-length product dropped by 25.59 and 18.45% in the arterial and portal venous phases, respectively, in the experiment than control group. The contrast dose was reduced by 16.86% in the experiment group. In the 55keV + 50% ASIR group, the arterial contrast-to-noise ratio and scores were significantly (P < 0.05) higher than in the control group in the arterial phase while the portal contrast-to-noise ratio and scores were not significantly different between the two groups (P > 0.05). The ASIS technique plus 50% ASIR can enhance image quality of the abdominal structures while decreasing the radiation and contrast dosage compared with the conventional scan mode.

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction.

Digital breast tomosynthesis (DBT) has been shown to somewhat alleviate the breast tissue overlapping issues of two-dimensional (2D) mammography. However, the improvement in current DBT systems over mammography is still limited. Statistical image reconstruction (SIR) methods have the potential to reduce through-plane artifacts in DBT, and thus may be used to further reduce anatomical clutter. The purpose of this work was to study the impact of SIR on anatomical clutter in the reconstructed DBT image volumes. An SIR with a slice-wise total variation (TV) regularizer was implemented to reconstruct DBT images which were compared with the clinical reconstruction method (filtered backprojection). The artifact spread function (ASF) was measured to quantify the reduction of the through-plane artifacts level in phantom studies and microcalcifications in clinical cases. The anatomical clutter was quantified by the anatomical noise power spectrum with a power law fitting model: NPSa ( f) = α f-β . The β values were measured from the reconstructed image slices when the two reconstruction methods were applied to a cohort of clinical breast exams (N = 101) acquired using Hologic Selenia Dimensions DBT systems. The full width half maximum (FWHM) of the measured ASF was reduced from 8.7 ± 0.1 mm for clinical reconstruction to 6.5 ± 0.1 mm for SIR which yields a 25% reduction in FWHM in phantom studies and the same amount of ASF reduction was also found in clinical measurements from microcalcifications. The measured β values for the two reconstruction methods were 3.17 ± 0.36 and 2.14 ± 0.39 for the clinical reconstruction method and the SIR method, respectively. This difference was statistically significant (P << 0.001). The dependence of β on slice location using either method was negligible. Statistical image reconstruction enabled a significant reduction of both the through-plane artifacts level and anatomical clutter in the DBT reconstructions. The β value was found to be β≈2.14 with the SIR method. This value stays in the middle between the β≈1.8 for cone beam CT and β≈3.2 for mammography. In contrast, the measured β value in the clinical reconstructions (β≈3.17) remains close to that of mammography.

Impact of a New Adaptive Statistical Iterative Reconstruction (ASIR)-V Algorithm on Image Quality in Coronary Computed Tomography Angiography

A new postprocessing algorithm named adaptive statistical iterative reconstruction (ASIR)-V has been recently introduced. The aim of this article was to analyze the impact of ASIR-V algorithm on signal, noise, and image quality of coronary computed tomography angiography. Fifty consecutive patients underwent clinically indicated coronary computed tomography angiography (Revolution CT; GE Healthcare, Milwaukee, WI). Images were reconstructed using filtered back projection and ASIR-V 0%, and a combination of filtered back projection and ASIR-V 20%-80% and ASIR-V 100%. Image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for left main coronary artery (LM), left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA) and were compared between the different postprocessing algorithms used. Similarly a four-point Likert image quality score of coronary segments was graded for each dataset and compared. A cutoff value of P < .05 was considered statistically significant. Compared to ASIR-V 0%, ASIR-V 100% demonstrated a significant reduction of image noise in all coronaries (P < .01). Compared to ASIR-V 0%, SNR was significantly higher with ASIR-V 60% in LM (P < .01), LAD (P < .05), LCX (P < .05), and RCA (P < .01). Compared to ASIR-V 0%, CNR for ASIR-V ≥60% was significantly improved in LM (P < .01), LAD (P < .05), and RCA (P < .01), whereas LCX demonstrated a significant improvement with ASIR-V ≥80%. ASIR-V 60% had significantly better Likert image quality scores compared to ASIR-V 0% in segment-, vessel-, and patient-based analyses (P < .01). Reconstruction with ASIR-V 60% provides the optimal balance between image noise, SNR, CNR, and image quality.

Impact of model-based iterative reconstruction (MBIR) on image quality in cerebral CT angiography before and after intracranial aneurysm treatment

PurposeTo subjectively and objectively assess the impact of model-based iterative reconstruction(MBIR) on image quality in cerebral computed tomography angiography compared to adaptive statistical iterative reconstruction (ASIR). Methods107 patients (mean age: 58 ± 14 years) were included prior to (n = 38) and after (n = 69) intracranial aneurysm treatment. Images were acquired using a routine protocol and reconstructed with MBIR and ASIR. Image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios in the internal carotid and middle cerebral arteries were compared between MBIR and ASIR using the Wilcoxon signed-rank test. Additionally, two neuroradiologists subjectively assessed noise, artefacts, vessel sharpness and overall quality using a semi-quantitative assessment scale. ResultsObjective assessment revealed that MBIR reduced noise (p < 0.0001) and additionally improved SNR (p < 0.0001) and CNR (p < 0.0001) compared to ASIR in untreated and treated patients. Subjective assessment revealed that in untreated patients, MBIR improved noise reduction, artefacts, vessel sharpness and overall quality relative to ASIR (p < 0.0001). In the treated groups, noise and vessel sharpness were improved (p < 0.0001) with no change in artefacts on images reconstructed with MBIR compared to ASIR. ConclusionMBIR significantly improves noise, SNR, CNR and vessel sharpness in untreated and treated patients with intracranial aneurysms. MBIR does not reduce artefacts generated by metallic devices following intracranial aneurysm treatment.

The combination of a reduction in contrast agent dose with low tube voltage and an adaptive statistical iterative reconstruction algorithm in CT enterography: Effects on image quality and radiation dose.

To investigate the subjective and quantitative image quality and radiation exposure of CT enterography (CTE) examination performed at low tube voltage and low concentration of contrast agent with adaptive statistical iterative reconstruction (ASIR) algorithm, compared with conventional CTE.One hundred thirty-seven patients with suspected or proved gastrointestinal diseases underwent contrast enhanced CTE in a multidetector computed tomography (MDCT) scanner. All cases were assigned to 2 groups. Group A (n = 79) underwent CT with low tube voltage based on patient body mass index (BMI) (BMI < 23 kg/m2, 80 kVp; BMI ≥ 23 kg/m2, 100 kVp) and low concentration of contrast agent (270 mg I/mL), the images were reconstructed with standard filtered back projection (FBP) algorithm and 50% ASIR algorithm. Group B (n = 58) underwent conventional CTE with 120 kVp and 350 mg I/mL contrast agent, the images were reconstructed with FBP algorithm. The computed tomography dose index volume (CTDIvol), dose length product (DLP), effective dose (ED), and total iodine dosage were calculated and compared. The CT values, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of the normal bowel wall, gastrointestinal lesions, and mesenteric vessels were assessed and compared. The subjective image quality was assessed independently and blindly by 2 radiologists using a 5-point Likert scale.The differences of values for CTDIvol (8.64 ± 2.72 vs 11.55 ± 3.95, P < .001), ED (6.34 ± 2.24 vs 8.52 ± 3.02, P < .001), and DLP (422.6 ± 149.40 vs 568.30 ± 213.90, P < .001) were significant between group A and group B, with a reduction of 25.2%, 25.7%, and 25.7% in group A, respectively. The total iodine dosage in group A was reduced by 26.1%. The subjective image quality did not differ between the 2 groups (P > .05) and all image quality scores were greater than or equal to 3 (moderate). Fifty percent ASIR-A group images provided lower image noise, but similar or higher quantitative image quality in comparison with FBP-B group images.Compared with the conventional protocol, CTE performed at low tube voltage, low concentration of contrast agent with 50% ASIR algorithm produce a diagnostically acceptable image quality with a mean ED of 6.34 mSv and a total iodine dose reduction of 26.1%.