Use Of Model-based Iterative Reconstruction Research Articles

A "one-stop-shop" 4D CTA protocol using 320-row CT for advanced imaging in acute ischemic stroke: a technical note.

This technical note describes a novel CT scan protocol that includes a non-enhanced CT, dynamic CTA, and perfusion of the whole brain and CTA of the carotid arteries using a 320-row area detector CT scanner, with a unique contrast injection and acceptable radiation exposure dose in patients presenting with acute ischemic stroke. The acquisition parameters and reconstruction parameters will be discussed including the use of model-based iterative reconstruction (MBIR), time summing (tMIP), and subtraction techniques to optimize the results of this protocol.Key Points• Scanning on a 320-row area detector CT can achieve both brain perfusion with dynamic angiography and reconstructed arterial and venous CTA, and supra aortic trunk angiography, in a single acquisition. • It provides, in a single exam, a full diagnostic workup, i.e., all the acquisitions that are needed to make a quick decision, with reasonable exposure to ionizing radiation and reduced amount of medium contrast, in case of acute ischemic stroke presentation.

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.

Use of Model-based Iterative Reconstruction to Improve Detection of Congenital Cardiovascular Anomalies in Infants Undergoing Free-breathing Computed Tomographic Angiography

The aim of the study was to assess the detection of congenital cardiovascular anomalies (congenital heart disease) in neonates and infants using model-based iterative reconstruction (MBIR) algorithm compared with hybrid iterative reconstruction (HIR) and filtered back projection (FBP) reconstructions on axial computed tomography (CT) performed at minimum scanner dose. Over 1 year, all CT angiographies performed in infants below 3 months of age with congenital heart disease were assessed retrospectively. All were scanned on a 256-slice CT (Brilliance iCT) using single axial rotation at minimum allowable scanner dose (80 kV/10 mAs), with patients free-breathing. Intravenous contrast was 1 mL/kg. Scan reconstruction was 0.9 mm/0.45 mm overlap, reconstructed with FBP, HIR (iDose5), and MBIR (IMR2). The 3 reconstructions per study were anonymized and randomized. Four cardiac radiologists (23, 9, 7, and 6 y experience) evaluated each reconstruction on a workstation for presence of an atrial septal defect, a ventricular septal defect, patent ductus arteriosus, and surgical shunt or anomalies of the aorta, pulmonary arteries, and pulmonary veins. Unevaluable structures were classified as nondiagnostic. Gold standard was surgery or both echocardiogram and cardiac catheterization. The sensitivity, specificity, and accuracy were determined for each reconstruction. Fifteen scans in 14 infants met the inclusion criteria, with a total of 48 anomalies. Pooled sensitivity for MBIR of 0.82 (range, 0.75 to 0.9) was significantly better than those for FBP (0.58; range, 0.54 to 0.6; P<0.001) and HIR (0.67; range, 0.60 to 0.79; P<0.001). Pooled accuracy of MBIR, HIR, and FBP was 0.91, 0.84, and 0.81, respectively. Readers deemed 39 and 15 structures nondiagnostic with FBP and HIR, respectively, versus 2 with MBIR (MBIR-FBP, MBIR-HIR, P<0.0001). The CTDIvol, DLP, and estimated dose for all cases was 0.52 mGy, 4.2 mGy×cm, and 0.16 mSv. MBIR significantly improves the detection of congenital anomalies in neonates and infants undergoing CT angiography at minimum allowable dose.

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study.

Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis-particularly in younger patients-might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiation dose and MBIR in the MDCT assessment of airway WT. An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo(TM), GE Healthcare) were used to reconstruct CT images of the airways. For each kV-mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV-mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV-mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low radiation dose levels. For FBP, the relative bias and the angular standard deviation of the measured WT increased steeply with decreasing radiation dose. Except for the smallest airway, MBIR enabled significant reduction in both the relative bias and angular standard deviation of the WT, particularly at low radiation dose levels; the SSQ was reduced by 50%-96% by using MBIR. The optimal reconstruction algorithm was found to be MBIR for the seven airways being assessed, and the combined use of MBIR and optimal kV-mAs selection resulted in a radiation dose reduction of 37%-83% compared with a reference scan protocol with a dose level of 1 mGy. The quantification accuracy of airway WT is strongly influenced by radiation dose and reconstruction algorithm. The MBIR algorithm potentially allows the desired WT quantification accuracy to be achieved with reduced radiation dose, which may enable a wider clinical use of MDCT for the assessment of airway WT, particularly for younger patients who may be more sensitive to exposures with ionizing radiation.

Use of Model-Based Iterative Reconstruction (MBIR) in reduced-dose CT for routine follow-up of patients with malignant lymphoma: dose savings, image quality and phantom study.

To evaluate both in vivo and in phantom studies, dose reduction, and image quality of body CT reconstructed with model-based iterative reconstruction (MBIR), performed during patient follow-ups for lymphoma. This study included 40 patients (mean age 49years) with lymphoma. All underwent reduced-dose CT during follow-up, reconstructed using MBIR or 50% advanced statistical iterative reconstruction (ASIR). All had previously undergone a standard dose CT with filtered back projection (FBP) reconstruction. The volume CT dose index (CTDIvol), the density measures in liver, spleen, fat, air, and muscle, and the image quality (noise and signal to noise ratio, SNR) (ANOVA) observed using standard or reduced-dose CT were compared both in patients and a phantom study (Catphan 600) (Kruskal Wallis). The CTDIvol was decreased on reduced-dose body CT (4.06mGy vs. 15.64mGy p < 0.0001). SNR was higher in reduced-dose CT reconstructed with MBIR than in 50% ASIR or than standard dose CT with FBP (patients, p ≤ 0.01; phantoms, p = 0.003). Low contrast detectability and spatial resolution in phantoms were not altered on MBIR-reconstructed CT (p ≥ 0.11). Reduced-dose CT with MBIR reconstruction can decrease radiation dose delivered to patients with lymphoma, while keeping an image quality similar to that obtained on standard-dose CT. • In lymphoma patients, CT dose reduction is a major concern. • Reduced-dose body CT provides a fourfold radiation dose reduction. • Optimized CT reconstruction techniques (MBIR) can maintain image quality.

The use of model-based iterative reconstruction to decrease ED radiation exposure

IntroductionThe radiation risk posed by diagnostic computed tomography (CT) is a growing concern. The use of model-based iterative reconstruction (MBIR) technology reduces radiation exposure but requires additional processing time. The goal of this study was to compare MBIR and a standard CT reconstructive protocols in terms of emergency department (ED) visit duration and reduction in radiation exposure. MethodsA retrospective, matched, case-control design was used to compare patients who received MBIR and standard protocol abdomen and pelvis CTs. ED length of stay (LOS) and radiation exposure were the 2 primary outcome variables. ResultsDuring the study period, 121 patients met inclusion criteria and were matched to controls for a total of 242 subjects. Although the low-dose group LOS was slightly longer, there was no significant difference in LOS. Mean differences were 18 minutes overall (520 vs 502 minutes; P = .497), 11 minutes for admitted patients (587 vs 576 minutes; P = .839), and 22 minutes for discharged patients (490 vs 468 minutes; P = .482). The mean volume CT dose index for the standard-dose CT was 11.6 ± 8.3 and 7.7 ± 4.6 mGy for the reduced-dose CT, a 34% decrease (P < .001). ConclusionUse of MBIR in the ED may provide decreased radiation exposure while minimally impacting ED LOS.

62 The Use of Model-Based Iterative Reconstruction to Decrease Radiation Dose Without Increasing Emergency Department Length of Stay

62 The Use of Model-Based Iterative Reconstruction to Decrease Radiation Dose Without Increasing Emergency Department Length of Stay

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Model-Based Iterative Reconstruction Versus Adaptive Statistical Iterative Reconstruction and Filtered Back Projection in Liver 64-MDCT: Focal Lesion Detection, Lesion Conspicuity, and Image Noise

The purpose of this study is to compare three CT image reconstruction algorithms for liver lesion detection and appearance, subjective lesion conspicuity, and measured noise. Thirty-six patients with known liver lesions were scanned with a routine clinical three-phase CT protocol using a weight-based noise index of 30 or 36. Image data from each phase were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Randomized images were presented to two independent blinded reviewers to detect and categorize the appearance of lesions and to score lesion conspicuity. Lesion size, lesion density (in Hounsfield units), adjacent liver density (in Hounsfield units), and image noise were measured. Two different unblinded truth readers established the number, appearance, and location of lesions. Fifty-one focal lesions were detected by truth readers. For blinded reviewers compared with truth readers, there was no difference for lesion detection among the reconstruction algorithms. Lesion appearance was statistically the same among the three reconstructions. Although one reviewer scored lesions as being more conspicuous with MBIR, the other scored them the same. There was significantly less background noise in air with MBIR (mean [± SD], 2.1 ± 1.4 HU) than with ASIR (8.9 ± 1.9 HU; p < 0.001) or FBP (10.6 ± 2.6 HU; p < 0.001). Mean lesion contrast-to-noise ratio was statistically significantly higher for MBIR (34.4 ± 29.1) than for ASIR (6.5 ± 4.9; p < 0.001) or FBP (6.3 ± 6.0; p < 0.001). In routine-dose clinical CT of the liver, MBIR resulted in comparable lesion detection, lesion characterization, and subjective lesion conspicuity, but significantly lower background noise and higher contrast-to-noise ratio compared with ASIR or FBP. This finding suggests that further investigation of the use of MBIR to enable dose reduction in liver CT is warranted.

Comparison of pure and hybrid iterative reconstruction techniques with conventional filtered back projection: Image quality assessment in the cervicothoracic region

ObjectivesTo evaluate the impact on image quality of three different image reconstruction techniques in the cervicothoracic region: model-based iterative reconstruction (MBIR), adaptive statistical iterative reconstruction (ASIR), and filtered back projection (FBP). MethodsForty-four patients underwent unenhanced standard-of-care clinical computed tomography (CT) examinations which included the cervicothoracic region with a 64-row multidetector CT scanner. Images were reconstructed with FBP, 50% ASIR-FBP blending (ASIR50), and MBIR. Two radiologists assessed the cervicothoracic region in a blinded manner for streak artifacts, pixilated blotchy appearances, critical reproduction of visually sharp anatomical structures (thyroid gland, common carotid artery, and esophagus), and overall diagnostic acceptability. Objective image noise was measured in the internal jugular vein. Data were analyzed using the sign test and pair-wise Student's t-test. ResultsMBIR images had significant lower quantitative image noise (8.88±1.32) compared to ASIR images (18.63±4.19, P<0.01) and FBP images (26.52±5.8, P<0.01). Significant improvements in streak artifacts of the cervicothoracic region were observed with the use of MBIR (P<0.001 each for MBIR vs. the other two image data sets for both readers), while no significant difference was observed between ASIR and FBP (P>0.9 for ASIR vs. FBP for both readers). MBIR images were all diagnostically acceptable. Unique features of MBIR images included pixilated blotchy appearances, which did not adversely affect diagnostic acceptability. ConclusionsMBIR significantly improves image noise and streak artifacts of the cervicothoracic region over ASIR and FBP. MBIR is expected to enhance the value of CT examinations for areas where image noise and streak artifacts are problematic.

Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization

Recent applications of model-based iterative reconstruction (MBIR) algorithms to multislice helical CT reconstructions have shown that MBIR can greatly improve image quality by increasing resolution as well as reducing noise and some artifacts. However, high computational cost and long reconstruction times remain as a barrier to the use of MBIR in practical applications. Among the various iterative methods that have been studied for MBIR, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a fast model-based iterative reconstruction algorithm using spatially nonhomogeneous ICD (NH-ICD) optimization. The NH-ICD algorithm speeds up convergence by focusing computation where it is most needed. The NH-ICD algorithm has a mechanism that adaptively selects voxels for update. First, a voxel selection criterion VSC determines the voxels in greatest need of update. Then a voxel selection algorithm VSA selects the order of successive voxel updates based upon the need for repeated updates of some locations, while retaining characteristics for global convergence. In order to speed up each voxel update, we also propose a fast 1-D optimization algorithm that uses a quadratic substitute function to upper bound the local 1-D objective function, so that a closed form solution can be obtained rather than using a computationally expensive line search algorithm. We examine the performance of the proposed algorithm using several clinical data sets of various anatomy. The experimental results show that the proposed method accelerates the reconstructions by roughly a factor of three on average for typical 3-D multislice geometries.