Statistical Iterative Reconstruction Algorithm Research Articles

A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms

PurposeTo characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images. MethodsCatphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index. ResultsThe five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values. ConclusionsThe improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.

Quantitative analysis evaluation of image reconstruction algorithms between digital and analog PET-CT

Positron emission tomography – computed tomography (PET-CT) is a non-invasive diagnostic tool that is widely used in oncology imaging. High quality diagnostic images and quantitative accuracy are often restricted by image noise, adequate spatial resolution and contrast ratio.Ordered Subset Expectation Maximisation (OSEM) is a widely used statistical iterative reconstruction algorithm in PET-CT due to its dependability, reconstruction quality and adequate signal-to-noise ratio. However, OSEM requires a large number of iterations to achieve high quantitative accuracy which results in increasing image noise. A novel algorithm, HYPER DPR (developed by United Imaging Healthcare) is an artificial intelligence-based reconstruction method that aims to provide increased sensitivity, higher spatial resolution and less noise.This study evaluates the accuracy and sensitivity of HYPER DPR against OSEM using reconstructed images from analog and digital PET-CT. Results demonstrate that both OSEM and HYPER DPR reconstruction algorithms in digital PET-CT has greater spatial resolution, increased detection sensitivity and less image noise when compared to analog PET-CT. Digital PET-CT and HYPER DPR enables better small lesion detection and increased resolution, thus resulting in better disease detection and improved patient management. Increased sensitivity of digital PET-CT results in low dose scans from reduced radiotracer injections, therefore having higher patient output.

Comparison of noise-power spectrum and modulation-transfer function for CT images reconstructed with iterative and deep learning image reconstructions: An initial experience study

Abstract Introduction Deep learning image reconstruction (DLIR) is a very recent image reconstruction method that is already available for commercial use. We evaluated the quality of DLIR images and compared it to the quality of images from the latest adaptive statistical iterative reconstruction (ASIR-V) algorithm in terms of noise-power spectrum (NPS) and modulation-transfer function (MTF). Methods We scanned a Revolution QA phantom (GE Healthcare, USA) and a 20 cm water phantom (GE Healthcare, USA) with our 512 multi-slice computed tomography (CT) scanner. Images of the tungsten wire within the Revolution QA phantom were reconstructed with a 50 mm field of view (FOV). The images were reconstructed with various ASIR-V strengths (i.e. 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%) and DLIRs (i.e. low, medium, and high) to assess the MTF. The images from the 20 cm water phantom were reconstructed with the same configuration to assess the NPS. Results The MTF was similar for both reconstruction algorithms of DLIR and ASiR-V. The peak frequency (fp) of the DLIR low was comparable to that from ASIR-V at 50, 60, 70%; the DLIR medium was comparable to ASIR-V at 80%; and the DLIR high was comparable to ASIR-V at 100%. The average frequency (fA) of the DLIR low was comparable to that from ASIR-V at 40%; the DLIR medium was comparable to ASIR-V at 50%; and the DLIR high was comparable to ASIR-V at 70%. Both the DLIR and ASIR-V were able to reduce noise, but they had a different texture. Conclusions The noise in the DLIR images was more homogenous at high and low frequencies, while in the ASIR-V images, the noise was more concentrated at high frequencies. The MTF was similar for both reconstruction algorithms. The DLIR method showed a better noise reduction than the ASIR-V reconstruction.

Statistical Interior Tomography via L1 Norm Dictionary Learning without Assuming an Object Support.

Interior tomography of X-ray computed tomography (CT) has many advantages, such as a lower radiation dose and lower detector hardware cost compared to traditional CT. However, this imaging technique only uses the projection data passing through the region of interest (ROI) for imaging; accordingly, the projection data are truncated at both ends of the detector, so the traditional analytical reconstruction algorithm cannot satisfy the demand of clinical diagnosis. To solve the above limitations, in this paper we propose a high-quality statistical iterative reconstruction algorithm that uses the zeroth-order image moment as novel prior knowledge; the zeroth-order image moment can be estimated in the projection domain using the Helgason–Ludwig consistency condition. Then, the L1norm of sparse representation, in terms of dictionary learning, and the zeroth-order image moment constraints are incorporated into the statistical iterative reconstruction framework to construct an objective function. Finally, the objective function is minimized using an alternating minimization iterative algorithm. The chest CT image simulated and CT real data experimental results demonstrate that the proposed approach can remove shift artifacts effectively and has superior performance in removing noise and persevering fine structures than the total variation (TV)-based approach.

High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses

To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26kg/m2, in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V). A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group (n = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group (n = 50) with 120kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal-noise-ratio (SNR) for vessel) were compared between the two groups. Low-dose group used significantly reduced contrast dose (23.82 ± 3.69mL, 50.6% reduction) and radiation dose (0.75 ± 0.14mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38mL and 1.65 ± 0.66mSv, respectively) (all p < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p < 0.001) compared to the conventional group. The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V. •The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol. •CCTA of normal size (BMI < 26kg/m2) patients acquired at sub-mSv radiation dose and 24mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement. •DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination.

Deep learning versus iterative image reconstruction algorithm for head CT in trauma

PurposeTo compare the image quality between a deep learning–based image reconstruction algorithm (DLIR) and an adaptive statistical iterative reconstruction algorithm (ASiR-V) in noncontrast trauma head CT.MethodsHead CT scans from 94 consecutive trauma patients were included. Images were reconstructed with ASiR-V 50% and the DLIR strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The image quality was assessed quantitatively and qualitatively and compared between the different reconstruction algorithms. Inter-reader agreement was assessed by weighted kappa.ResultsDLIR-M and DLIR-H demonstrated lower image noise (p < 0.001 for all pairwise comparisons), higher SNR of up to 82.9% (p < 0.001), and higher CNR of up to 53.3% (p < 0.001) compared to ASiR-V. DLIR-H outperformed other DLIR strengths (p ranging from < 0.001 to 0.016). DLIR-M outperformed DLIR-L (p < 0.001) and ASiR-V (p < 0.001). The distribution of reader scores for DLIR-M and DLIR-H shifted towards higher scores compared to DLIR-L and ASiR-V. There was a tendency towards higher scores with increasing DLIR strengths. There were fewer non-diagnostic CT series for DLIR-M and DLIR-H compared to ASiR-V and DLIR-L. No images were graded as non-diagnostic for DLIR-H regarding intracranial hemorrhage. The inter-reader agreement was fair-good between the second most and the less experienced reader, poor-moderate between the most and the less experienced reader, and poor-fair between the most and the second most experienced reader.ConclusionThe image quality of trauma head CT series reconstructed with DLIR outperformed those reconstructed with ASiR-V. In particular, DLIR-M and DLIR-H demonstrated significantly improved image quality and fewer non-diagnostic images. The improvement in qualitative image quality was greater for the second most and the less experienced readers compared to the most experienced reader.

Contribution of Statistical Iterative Reconstruction Algorithm for Orthopaedic Applications: A Study with a Cone-Beam CT Prototype

Contribution of Statistical Iterative Reconstruction Algorithm for Orthopaedic Applications: A Study with a Cone-Beam CT Prototype

A phantom study using dual-energy spectral computed tomography imaging: Comparison of image quality between two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms for evaluating ground-glass nodules of the lung.

To assess the image quality of the two adaptive statistical iterative reconstruction (ASiR and ASiR-V) algorithms for evaluating ground-glass nodules (GGNs) of the lung. The chest phantom with ground-glass simulated pulmonary nodules was scanned by dual-energy spectral computed tomography (CT) using gemstone spectral imaging mode. The image was reconstructed with ASiR and ASiR-V from 0 (FBP) to 90% blending percentages, respectively. The average noise and signal-to-noise ratio (SNR) of reconstruction images were calculated. The data were statistically analyzed. Compared with FBP, as the percentage of ASiR and ASiR-V increased from 10 to 90%, image noise reduced by 3.7%-45.2% and 14.1%-73.8%, respectively, and the SNR increased accordingly. There was significantly higher SNR value of ASiR-V images as the percentages of IR increased to 50% or greater, compared to those of ASiR. Subjective image quality scores of ASiR and ASiR-V improved significantly as percentage increased from 10 to 80% for ASiR and ASiR-V (peaked at 60% for both of them). Both ASiR and ASiR-V can reduce the image noise and improve the objective image quality for presenting pulmonary nodules on dual-energy spectral CT imaging compared with FBP. ASiR-V provided further image quality improvement than ASiR, and both ASiR and ASiR-V 60% had the highest image quality score.

CT Image Features under Reconstruction Algorithm in Analysis of the Effect of Probiotics Combined with Ursodeoxycholic Acid in Treatment of Intrahepatic Cholestasis of Pregnancy.

This research was to explore the adoption value of computed tomography (CT) images based on adaptive statistical iterative reconstruction (ASIR) algorithm in the evaluation of probiotics combined with ursodeoxycholic acid in the treatment of intrahepatic cholestasis of pregnancy (ICP). A total of 82 patients with ICP were selected as the research subjects and they were randomly rolled into experimental group (380 mg probiotics enteric-soluble capsule twice a day, combined with 90 mg ursodeoxycholic acid soft capsule three times a day) and control group (90 mg ursodeoxycholic acid soft capsule three times a day), with 41 cases in each. The treatment course was four months. The ASIR algorithm was constructed and applied to the CT image analysis and diagnosis of ICP patients. The effects of filtering back projection (FBP) reconstruction and ASIR algorithm on CT image quality, denoising degree, and artifacts of ICP patients were compared. Moreover, blood indicator levels of ICP patients before and after treatment were assessed. The results showed that the SD values of liver and gallbladder (20.77 Hu and 27.58 Hu) in the reconstructed image of the ASIR algorithm were significantly lower than those of the FBP algorithm (40.58 Hu and 45.63 Hu) (P < 0.05). The SNR values of the liver and gallbladder (3.68 and 2.05) of the reconstructed image were significantly higher than those of the FBP algorithm (1.91 and 1.19) (P < 0.05). The overall image quality after ASIR reconstruction (3.92 points) was significantly better than that of the FBP algorithm (2.36 points), and the image noise score (3.21 points) reconstructed by the FBP algorithm was higher than that by the ASIR algorithm (1.83 points). The artifact score of FBP reconstructed image (4.47 points) was greatly higher than that of ASIR algorithm (2.26 points) (P < 0.05). Before treatment, there was no remarkable difference in the indexes between the two groups of patients (P > 0.05). After treatment, the γ-glutamyltransferase (γ-GT) and alkaline phosphatase (ALP) levels (327.55 U/L and 778.15 μmol/L) of the experimental group of ICP patients were higher than those of the control group (248.63 U/L and 668.43 μmol/L), with substantial difference between the two groups (P < 0.05). The blood ammonia (BA) level (151.09 μmol/L) of the experimental group was lower than that of the control group (178.46 μmol/L), and the difference between the two groups was remarkable (P < 0.05). To sum up, the CT image denoising degree based on ASIR algorithm was high, with few artifacts and good overall quality. Probiotics combined with ursodeoxycholic acid in the treatment of ICP can effectively improve the liver function and intestinal flora of patients, which was of great significance in the clinical diagnosis and treatment of the disease.

Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

OBJECTIVE:To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose.METHODS:The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared.RESULTS:For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%).CONCLUSION:Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.

Chest Computed Tomography Images in Neonatal Bronchial Pneumonia under the Adaptive Statistical Iterative Reconstruction Algorithm.

This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower (P < 0.05). The accuracy rates of CT examination, CRP examination, ESR examination, CRP + ESR examination, and CRP + ESR + CT examination for the diagnosis of NBP were 80.7%, 75.3%, 75.1%, 80.3%, and 98.6%, respectively. CT technology based on AI algorithm showed high clinical application value in the feature analysis of NBP.

Evaluation of complex congenital heart disease with prospective ECG-gated cardiac CT in a single heartbeat at low tube voltage (70kV) and adaptive statistical iterative reconstruction in infants: a single center experience.

The purpose of this study is to evaluate the radiation dose, image quality, and diagnostic accuracy of prospective ECG-gated cardiac CT at 70kV and adaptive statistical iterative reconstruction (ASIR), with a single source, 512 slice MDCT in the diagnosis of complex congenital heart disease in infants. We retrospectively evaluated 47 infants (ages 1day to 353days) with prospective ECG-gated cardiac CT that was performed on a single source 512 slice CT at low tube voltage (70kV) using a wide detector aperture, adaptive statistical iterative reconstruction algorithm (ASIR), and specific reconstruction software reducing coronary motion artifacts (SnapShot Freeze). All cardiac images were obtained during the first pass of contrast material through the anatomic structures of interest and the targets for the center of the acquisition window were set 45% of the R-R interval during one cardiac cycle without sedation and breath-hold. The median effective dose measured in our study was 0.64 ± 0.16mSv. The average subjective overall image quality score was 4.34 ± 0.31 (range 3-5). For the determination of objective image quality, Mean Noise (HU), SNR, and CNR values ​​emerged as 20.8, 28.7(for pulmonary artery), and 27.1, respectively. Diagnostic accuracy was 100% for the main purposes for the main clinical indication. During cardiac CT examination, pathologies in addition to cardiac anomalies were found in 9/47 of cases (7 severe airway obstructions,1 posterior diaphragmatic hernia, 1 vertebral anomaly).Prospective ECG-gated cardiac CT scan at 70kV and ASIR in infants with complex CHD provides low radiation dose (submillisievert) in a single heartbeat with a good objective and subjective image quality. It also provides important benefits in the diagnosis of additional pathology.

Performance evaluation of using shorter contrast injection and 70 kVp with deep learning image reconstruction for reduced contrast medium dose and radiation dose in coronary CT angiography for children: a pilot study.

Iterative reconstruction algorithms are often used to reduce image noise in low-dose coronary computed tomography angiography (CCTA) but encounter limitations. The newly introduced deep learning image reconstruction (DLIR) algorithm may provide new opportunities. We assessed the image quality and diagnostic performance of DLIR in low radiation dose and contrast medium dose CCTA of pediatric patients with 70 kVp and a shortened injection protocol. This was a prospective study. A total of 27 consecutive arrhythmic pediatric patients were enrolled in the study group and underwent CCTA using a prospective ECG-triggered single-beat protocol: tube voltage 70 kVp, automatic tube current modulation for a noise index (NI) of 22, and contrast dose of 0.4-0.6 mL/kg. Images were reconstructed with DLIR. They were compared with 27 matched patients in the control group scanned with 80 kVp, a lower NI setting (NI =19), and a higher contrast dose (0.8-1.2 mL/kg). The images in the control group were reconstructed using the adaptive statistical iterative reconstruction (ASIR-V) algorithm. The image contrast, image quality, and diagnostic confidence were assessed by 2 experienced radiologists using a 5-point scale (1: nondiagnostic and 5: excellent). The CT value and standard deviation of the aorta and perivascular tissue were measured, and the contrast-to-noise ratio (CNR) for the aorta was calculated. The contrast medium and radiation doses were compared. The study and control groups had similar image contrast scores (4.75±0.57 vs. 4.78±0.42), image quality scores (3.67±0.47 vs. 3.44±0.51), and diagnostic confidence (4.74±0.44 vs. 4.74±0.45) (all P>0.05). There was an adequate enhancement in the aorta (614.74±127.73 vs. 705.89±111.20 HU) and similar CNR (20.34±4.64 vs. 20.99±4.14) in both groups. The image noise of the study group was lower in the aorta (30.61±3.88 vs. 34.77±3.49) and similar in perivascular tissue (27.66±6.24 vs. 27.55±3.33) compared with the control group. The study group reduced the total contrast medium dose by 53% to 15.07±3.68 mL and radiation dose by 36% to 0.57±0.31 mSv. The DLIR algorithm in CCTA for children using 70 kVp tube voltage with a shortened contrast medium injection protocol maintains image quality and diagnostic confidence while significantly reducing contrast medium dose and radiation dose compared with the use of the conventional CCTA protocol.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment.

Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment. MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted. Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle. Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Imaging pediatric acute head trauma using 100-kVp low dose CT with adaptive statistical iterative reconstruction (ASIR-V) in single rotation on a 16 cm wide-detector CT.

To demonstrate the ability of achieving low dose and high-quality head CT images for children with acute head trauma using 100 kVp and adaptive statistical iterative reconstruction (ASIR-V) algorithm in single rotation on a 16 cm wide-detector system. We retrospectively analyzed the CT dose index (CTDI) and image quality of 104 children aged 0-6 years with acute head trauma (1 hour -3 days) in two groups: Group 1(n = 50) on a 256-row CT with single rotation at a reduced-dose of 100 kVp/240 mA and reconstructed using ASIR-V at 70%level; Group 2(n = 54) on a 64-row CT with multiple rotations at a standard dose of 120 kVp/ 180mA and reconstructed using a conventional filtered back-projection (FBP). Both groups used the 0.5 s/r axial scan mode. CT dose index (CTDI) and quantitative image quality measurements were compared using the Student t test; qualitative image quality comparison was carried out using Mann-Whitney rank test and the inter-reviewer agreement was evaluated using Kappa test. The exposure time was 0.5 s for Group 1 and 3.27±0.29 s for Group 2. The CTDI in Group 1 was 9.74±0.86mGy, 36.38%lower than the 15.31mGy in Group 2 (p < 0.001). Group 1 and Group 2 had similar artifact index (2.06±1.06 vs. 2.37±1.18) in the cerebellar hemispheres, and similar contrast-to-noise ratio (2.32±0.83 vs. 1.69±0.68), (1.47±0.72 vs. 1.10±0.43) respectively for cerebellum and thalamus (p > 0.05). Image quality was acceptable for diagnosis, and motion artifacts were reduced in Group 1 (p < 0.001). Single rotation CT with 100 kVp and 70%ASIR-V on 16 cm wide-detector CT reduces radiation dose and motion artifacts for children with acute head trauma without compromising diagnostic quality as compared with standard dose protocol. Thus, it provides a novel imaging method in management of pediatric acute head trauma.

Low-Dose MDCT of Patients With Spinal Instrumentation Using Sparse Sampling: Impact on Metal Artifacts.

OBJECTIVE. The purpose of our study was to evaluate simulated sparse-sampled MDCT combined with statistical iterative reconstruction (SIR) for low-dose imaging of patients with spinal instrumentation. MATERIALS AND METHODS. Thirty-eight patients with implanted hardware after spinal instrumentation (24 patients with short- or long-term instrumentation-related complications [i.e., adjacent segment disease, screw loosening or implant failure, or postoperative hematoma or seroma] and 14 control subjects with no complications) underwent MDCT. Scans were simulated as if they were performed with 50% (P50), 25% (P25), 10% (P10), and 5% (P5) of the projections of the original acquisition using an in-house-developed SIR algorithm for advanced image reconstructions. Two readers performed qualitative image evaluations of overall image quality and artifacts, image contrast, inspection of the spinal canal, and diagnostic confidence (1 = high, 2 = medium, and 3 = low confidence). RESULTS. Although overall image quality decreased and artifacts increased with reductions in the number of projections, all complications were detected by both readers when 100% of the projections of the original acquisition (P100), P50, and P25 imaging data were used. For P25 data, diagnostic confidence was still high (mean score ± SD: reader 1, 1.2 ± 0.4; reader 2, 1.3 ± 0.5), and interreader agreement was substantial to almost perfect (weighted Cohen κ = 0.787-0.855). The mean volumetric CT dose index was 3.2 mGy for P25 data in comparison with 12.6 mGy for the original acquisition (P100 data). CONCLUSION. The use of sparse sampling and SIR for low-dose MDCT in patients with spinal instrumentation facilitated considerable reductions in radiation exposure. The use of P25 data with SIR resulted in no missed complications related to spinal instrumentation and allowed high diagnostic confidence, so using only 25% of the projections is probably enough for accurate and confident diagnostic detection of major instrumentation-related complications.

Reducing motion artifact in sequential-scan dual-energy CT imaging by incorporating deformable registration within joint statistical image reconstruction

Dual-energy computed tomography (DECT) has been widely used to reconstruct basis components. In previous studies, ou DECT algorithm has shown high accuracy in stopping power ratio (SPR) estimation of fixed objects for proton radiotherapy planning. However, patient movement between sequential data acquisitions may lead to severe motion artifacts in the component images. In order to reduce or eliminate the motion artifacts in clinical applications, we combine a deformable registration method with an accurate joint statistical iterative reconstruction algorithm, dual-energy alternating minimization (DEAM). Image registration is a process of geometrically aligning two or more images. We implement a multi-modality symmetric deformable registration method based on Advanced Normalization Tools (ANTs) to automatically align the scans we acquire for the same patient. The precalculated registration mapping and its inverse are then embedded into each iteration of the DEAM algorithm. The performance of warped DEAM is quantitatively assessed. Theoretically, the performance of warped DEAM on moved patients should be comparable to the performance of the original DEAM algorithm on fixed objects. The warped DEAM algorithm reduces motion artifacts while preserving the accuracy of the iterative joint statistical CT reconstruction algorithm, which enables us to reconstruct accurate results from sequentially scanned dual-energy patient data.

Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.

To compare the image quality between the vendor-agnostic and vendor-specific algorithms on ultralow-dose chest CT. Vendor-agnostic deep learning post-processing model (DLM), vendor-specific deep learning image reconstruction (DLIR, high level), and adaptive statistical iterative reconstruction (ASiR, 70%) algorithms were employed. One hundred consecutive ultralow-dose noncontrast CT scans (CTDIvol; mean, 0.33 ± 0.056 mGy) were reconstructed with five algorithms: DLM-stnd (standard kernel), DLM-shrp (sharp kernel), DLIR, ASiR-stnd, and ASiR-shrp. Three thoracic radiologists blinded to the reconstruction algorithms reviewed five sets of 100 images and assessed subjective noise, spatial resolution, distortion artifact, and overall image quality. They selected the most preferred algorithm among five image sets for each case. Image noise and signal-to-noise ratio were measured. Edge-rise-distance was measured at a pulmonary vessel, i.e., the distance between two points where attenuation was 10% and 90% of maximal intravascular intensity. The skewness of attenuation was calculated in homogeneous areas. DLM-stnd, followed by DLIR, showed the best subjective noise on both lung and mediastinal windows, while DLIR yielded the least measured noise (ps < .0001). Compared to DLM-stnd, DLIR showed inferior subjective spatial resolution on lung window and higher edge-rise-distance (ps < .0001). Additionally, DLIR showed the most frequent distortion artifacts and deviated skewness (ps < .0001). DLM-stnd scored the best overall image quality, followed by DLM-shrp and DLIR (mean score 3.89 ± 0.19, 3.68 ± 0.24, and 3.53 ± 0.33; ps < .001). Two among three readers preferred DLM-stnd on both windows. Although DLIR provided the best quantitative noise profile, DLM-stnd showed the best overall image quality with fewer artifacts and was preferred by two among three readers. • A vendor-agnostic deep learning post-processing algorithm applied to ultralow-dose chest CT exhibited the best image quality compared to vendor-specific deep learning algorithm and ASiR techniques. • Two out of three readers preferred a vendor-agnostic deep learning post-processing algorithm in comparison to vendor-specific deep learning algorithm and ASiR techniques. • A vendor-specific deep learning reconstruction algorithm yielded the least image noise, but showed significantly more frequent specific distortion artifacts and increased skewness of attenuation compared to a vendor-agnostic algorithm.

Quantitative Evaluation of Cardiothoracic CT Image Sharpness with an Integrated Circuit Detector: Comparison between Statistical and Model-Based Iterative Reconstruction Algorithms

Quantitative Evaluation of Cardiothoracic CT Image Sharpness with an Integrated Circuit Detector: Comparison between Statistical and Model-Based Iterative Reconstruction Algorithms

Adaptive statistical iterative reconstruction for computed tomography of the spine

IntroductionThe utility of evaluating a sagittal view of CT of the spine is well-known. In many clinical cases, the sagittal view includes noise generated from surrounding objects and may degrade the image quality. Iterative reconstruction (IR) techniques are useful for noise reduction; however, they can reduce spatial resolution. The aim of this study was to evaluate the effectiveness of the adaptive statistical iterative reconstruction (ASiR) for generating sagittal CT images of the spine when compared to filtered back projection (FBP). MethodsThe image quality of clinical images from 25 patients were subjectively assessed. Three radiologists rated spatial resolution, image noise, and overall image quality using a five-point scale. For objective assessment, z-direction modulation transfer function (z-MTF) was measured using a custom-made phantom. Additionally, z-axis noise power spectrum (z-NPS) was measured using a water phantom. An improved adaptive statistical iterative reconstruction algorithm called ASiR-V was used in this study. Blending levels were 50%, and 100% (ASiR-V50, ASiR-V100, respectively). ResultsFor subjective assessments, images using ASiR-V100 were determined to have the best overall image quality, despite having received the worst score in the assessment of spatial resolution. For objective assessments, the image using ASiR-V50 and ASiR-V100 curves were slightly degraded in terms of low contrast z-MTF when compared to FBP. ConclusionASiR-V was effective to improve the image quality when compared with FBP when reviewing sagittal reformats of the spine. Implications for practiceThis study suggests that high resolution is not the only thing that is key when reviewing sagittal CT spinal reformats. Such images should be provided as part of routine CT spine protocols, where available.