Model-based Iterative Reconstruction Techniques Research Articles

The Feasibility of a Model-Based Iterative Reconstruction Technique Tuned for the Myocardium on Myocardial Computed Tomography Late Enhancement.

This study evaluated the feasibility of a model-based iterative reconstruction technique (MBIR) tuned for the myocardium on myocardial computed tomography late enhancement (CT-LE). Twenty-eight patients who underwent myocardial CT-LE and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) within 1 year were retrospectively enrolled. Myocardial CT-LE was performed using a 320-row CT with low tube voltage (80 kVp). Myocardial CT-LE images were scanned 7 min after CT angiography (CTA) without additional contrast medium. All myocardial CT-LE images were reconstructed with hybrid iterative reconstruction (HIR), conventional MBIR (MBIR_cardiac), and new MBIR tuned for the myocardium (MBIR_myo). Qualitative (5-grade scale) scores and quantitative parameters (signal-to-noise ratio [SNR] and contrast-to-noise ratio [CNR]) were assessed as image quality. The sensitivity, specificity, and accuracy of myocardial CT-LE were evaluated at the segment level using an American Heart Association (AHA) 16-segment model, with LGE-MRI as a reference standard. These results were compared among the different CT image reconstructions. In 28 patients with 448 segments, 160 segments were diagnosed with positive by LGE-MRI. In the qualitative assessment of myocardial CT-LE, the mean image quality scores were 2.9 ± 1.2 for HIR, 3.0 ± 1.1 for MBIR_cardiac, and 4.0 ± 1.0 for MBIR_myo. MBIR_myo showed a significantly higher score than HIR (P < 0.001) and MBIR_cardiac (P = 0.018). In the quantitative image quality assessment of myocardial CT-LE, the median image SNR was 10.3 (9.1-11.1) for HIR, 10.8 (9.8-12.1) for MBIR_cardiac, and 16.8 (15.7-18.4) for MBIR_myo. The median image CNR was 3.7 (3.0-4.6) for HIR, 3.8 (3.2-5.1) for MBIR_cardiac, and 6.4 (5.0-7.7) for MBIR_myo. MBIR_myo significantly improved the SNR and CNR of CT-LE compared to HIR and MBIR_cardiac (P < 0.001). The sensitivity, specificity, and accuracy for the detection of myocardial CT-LE were 70%, 92%, and 84% for HIR; 71%, 92%, and 85% for MBIR_cardiac; and 84%, 92%, and 89% for MBIR_myo, respectively. MBIR_myo showed significantly higher image quality, sensitivity, and accuracy than the others (P < 0.05). MBIR tuned for myocardium improved image quality and diagnostic performance for myocardial CT-LE assessment.

Physical and visual evaluations of CT image quality of large low-contrast objects with visual model-based iterative reconstruction technique: a phantom study.

We aimed to verify whether the image quality of large low-contrast objects can be improved using visual model-based iterative reconstruction (VMR) while maintaining the visibility of conventional filtered back projection (FBP) and reducing radiation dose through physical and visual evaluation.A 64-row multi-slice CT system with SCENARIA View (FUJIFILM healthcare Corp. Tokyo, Japan) was used. The noise power spectrum (NPS), task-based transfer function (TTF), and signal-to-noise ratio (SNR) were physically evaluated. A low contrast object as a substitute for a liver mass was visually evaluated.In the noise measurement, STD1 showed an 18% lower noise compared to FBP. STR4 was able to reduce noise by 58% compared to FBP. The NPS of VMR was similar to those of FBP from low to high spatial frequency. The NPS of VMR reconstructions showed a similar variation with frequency as FBP reconstructions. STD1 showed the highest 10% TTF, and higher 10% TTF was observed with lower VMR level. The SNR of VMR was close to that of FBP, and higher SNR was observed with higher VMR level. In the results of the visual evaluation, there was no significant difference in visual evaluation between STD1 and FBP (p = 0.99) and between STD2 and FBP (p = 0.56).We found that the NPS of VMR images was similar to that of FBP images, and it can reduce noise and radiation dose by 25% and 50%, respectively, without decreasing the visual image quality compared to FBP.

Multi-channel convolutional analysis operator learning for dual-energy CT reconstruction

Objective. Dual-energy computed tomography (DECT) has the potential to improve contrast and reduce artifacts and the ability to perform material decomposition in advanced imaging applications. The increased number of measurements results in a higher radiation dose, and it is therefore essential to reduce either the number of projections for each energy or the source x-ray intensity, but this makes tomographic reconstruction more ill-posed. Approach. We developed the multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies and we propose an optimization method which jointly reconstructs the attenuation images at low and high energies with mixed norm regularization on the sparse features obtained by pre-trained convolutional filters through the convolutional analysis operator learning (CAOL) algorithm. Main results. Extensive experiments with simulated and real computed tomography data were performed to validate the effectiveness of the proposed methods, and we report increased reconstruction accuracy compared with CAOL and iterative methods with single and joint total variation regularization. Significance. Qualitative and quantitative results on sparse views and low-dose DECT demonstrate that the proposed MCAOL method outperforms both CAOL applied on each energy independently and several existing state-of-the-art model-based iterative reconstruction techniques, thus paving the way for dose reduction.

Model-based iterative reconstruction in paediatric head computed tomography: a pilot study on dose reduction in children.

PurposeTo evaluate the potential of model-based iterative reconstruction (MBIR) on dose reduction and image quality in children undergoing computed tomography (CT) head examinations.Material and methodsThis prospective study was approved by the institutional ethics committee. A total of 88 children (age range of 5 to 16 years) with a history of seizures underwent contrast-enhanced CT scan. Forty-one children underwent CT study according to the MBIR technique, while 47 children underwent CT of the head with the non-MBIR protocol. Images were reviewed by 2 blinded paediatric radiologists in a random order. Mean dose-length product, CT dose index (CTDI) volume, and mean effective dose were recorded for both groups. Image quality, image noise, and diagnostic acceptability of 2 image sets were also recorded.ResultsIn the MBIR group, the mean dose-length product was reduced by 79.8%; the mean CTDI volume was reduced by 88.5%, while the mean effective dose was reduced by 81% when compared to the non-MBIR group. No significant difference was seen in diagnostic acceptability, image noise, and image quality between the 2 groups.ConclusionsMBIR technique is highly effective in reducing radiation dose in paediatric head CT examinations without any significant difference in image quality, image noise, and diagnostic acceptability.

Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure

Model-based iterative reconstruction techniques for CT that include a description of the noise statistics and a physical forward model of the image formation process have proven to increase image quality for many applications. Specifically, including models of the system blur into the physical forward model and thus implicitly performing a deconvolution of the projections during tomographic reconstruction, could demonstrate distinct improvements, especially in terms of resolution. However, the results strongly rely on an exact characterization of all components contributing to the system blur. Such characterizations can be laborious and even a slight mismatch can diminish image quality significantly. Therefore, we introduce a novel objective function, which enables us to jointly estimate system blur parameters during tomographic reconstruction. Conventional objective functions are biased in terms of blur and can yield lowest cost to blurred reconstructions with low noise levels. A key feature of our objective function is a new normalized sparsity measure for CT based on total-variation regularization, constructed to be less biased in terms of blur. We outline a solving strategy for jointly recovering low-dimensional blur parameters during tomographic reconstruction. We perform an extensive simulation study, evaluating the performance of the regularization and the dependency of the different parts of the objective function on the blur parameters. Scenarios with different regularization strengths and system blurs are investigated, demonstrating that we can recover the blur parameter used for the simulations. The proposed strategy is validated and the dependency of the objective function with the number of iterations is analyzed. Finally, our approach is experimentally validated on test-bench data of a human wrist phantom, where the estimated blur parameter coincides well with visual inspection. Our findings are not restricted to attenuation-based CT and may facilitate the recovery of more complex imaging model parameters.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm−1 which was increased to 1.2 mm−1 by SDIR, at half maximum.

Efficacy of model-based iterative reconstruction technique in non-enhanced CT of the renal tracts for ureteric calculi.

The purpose of this study was to assess the efficacy of model-based iterative reconstruction (MBIR), statistical iterative reconstruction (SIR), and filtered back projection (FBP) image reconstruction algorithms in the delineation of ureters and overall image quality on non-enhanced computed tomography of the renal tracts (NECT-KUB). This was a prospective study of 40 adult patients who underwent NECT-KUB for investigation of ureteric colic. Images were reconstructed using FBP, SIR, and MBIR techniques and individually and randomly assessed by two blinded radiologists. Parameters measured were overall image quality, presence of ureteric calculus, presence of hydronephrosis or hydroureters, image quality of each ureteric segment, total length of ureters unable to be visualized, attenuation values of image noise, and retroperitoneal fat content for each patient. There were no diagnostic discrepancies between image reconstruction modalities for urolithiasis. Overall image qualities and for each ureteric segment were superior using MBIR (67.5% rated as 'Good to Excellent' vs. 25% in SIR and 2.5% in FBP). The lengths of non-visualized ureteric segments were shortest using MBIR (55.0% measured 'less than 5cm' vs. ASIR 33.8% and FBP 10%). MBIR was able to reduce overall image noise by up to 49.36% over SIR and 71.02% over FBP. MBIR technique improves overall image quality and visualization of ureters over FBP and SIR.

Multiresolution iterative reconstruction in high-resolution extremity cone-beam CT

Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution penalized-weighted least squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10× can be used without introducing artifacts, yielding a ~50× speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view.

Coronary Stent on Coronary CT Angiography: Assessment with Model-Based Iterative Reconstruction Technique

Coronary Stent on Coronary CT Angiography: Assessment with Model-Based Iterative Reconstruction Technique

Improving head and neck CTA with hybrid and model-based iterative reconstruction techniques

To compare image quality of head and neck computed tomography angiography (CTA) reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MIR) algorithms. The raw data of 34 studies were simultaneously reconstructed with FBP, HIR (iDose(4), Philips Healthcare, Best, the Netherlands), and with a prototype version of a MIR algorithm (IMR, Philips Healthcare). Objective (contrast-to-noise ratio [CNR], vascular contrast, automatic vessel analysis [AVA], stenosis grade) and subjective image quality (ranking at level of the circle of Willis, carotid bifurcation, and shoulder) of the five reconstructions were compared using repeated-measures analysis of variance (ANOVA) and post-hoc analysis. Vascular contrast was significantly higher in both the circle of Willis and carotid bifurcation with both levels of MIR compared to the other reconstruction methods (all p<0.0001). The CNR was highest for high MIR, followed by low MIR, high HIR, mid HIR and FBP (p<0.001 except low MIR versus high HIR; p>0.33). AVA showed most complete carotids in both MIR-levels, followed by high HIR (p>0.08), mid HIR (p<0.023) and FBP (p<0.010), vertebral arteries completeness was similar (p=0.40 and p=0.06). Stenosis grade showed no significant differences (p=0.16). High HIR showed the best subjective image quality at the circle of Willis and carotid bifurcation level, followed by mid HIR. At shoulder level, low MIR and high HIR were ranked best, followed by high MIR. Objectively, MIR significantly improved the overall image quality, reduced image noise, and improved automated vessel analysis, whereas FBP showed the lowest objective image quality. Subjectively, the highest level of HIR was considered superior at the level of the circle of Willis and the carotid bifurcation, and along with the lowest level of MIR for the origins of the neck arteries at shoulder level.

Limiting CT radiation dose in children with craniosynostosis: phantom study using model-based iterative reconstruction.

Medical professionals need to exercise particular caution when developing CT scanning protocols for children who require multiple CT studies, such as those with craniosynostosis. To evaluate the utility of ultra-low-dose CT protocols with model-based iterative reconstruction techniques for craniosynostosis imaging. We scanned two pediatric anthropomorphic phantoms with a 64-slice CT scanner using different low-dose protocols for craniosynostosis. We measured organ doses in the head region with metal-oxide-semiconductor field-effect transistor (MOSFET) dosimeters. Numerical simulations served to estimate organ and effective doses. We objectively and subjectively evaluated the quality of images produced by adaptive statistical iterative reconstruction (ASiR) 30%, ASiR 50% and Veo (all by GE Healthcare, Waukesha, WI). Image noise and contrast were determined for different tissues. Mean organ dose with the newborn phantom was decreased up to 83% compared to the routine protocol when using ultra-low-dose scanning settings. Similarly, for the 5-year phantom the greatest radiation dose reduction was 88%. The numerical simulations supported the findings with MOSFET measurements. The image quality remained adequate with Veo reconstruction, even at the lowest dose level. Craniosynostosis CT with model-based iterative reconstruction could be performed with a 20-μSv effective dose, corresponding to the radiation exposure of plain skull radiography, without compromising required image quality.

Model-Based Iterative Reconstruction in Low-Dose CT Colonography—Feasibility Study in 65 Patients for Symptomatic Investigation

To compare image quality on computed tomographic colonography (CTC) acquired at standard dose (STD) and low dose (LD) using filtered-back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) techniques. A total of 65 symptomatic patients were prospectively enrolled for the study and underwent STD and LD CTC with filtered-back projection, adaptive statistical iterative reconstruction, and MBIR to allow direct per-patient comparison. Objective image noise, subjective image analyses, and polyp detection were assessed. Objective image noise analysis demonstrates significant noise reduction using MBIR technique (P < .05) despite being acquired at lower doses. Subjective image analyses were superior for LD MBIR in all parameters except visibility of extracolonic lesions (two-dimensional) and visibility of colonic wall (three-dimensional) where there were no significant differences. There was no significant difference in polyp detection rates (P > .05). Doses: LD (dose-length product, 257.7), STD (dose-length product, 483.6). LD MBIR CTC objectively shows improved image noise using parameters in our study. Subjectively, image quality is maintained. Polyp detection shows no significant difference but because of small numbers needs further validation. Average dose reduction of 47% can be achieved. This study confirms feasibility of using MBIR in this context of CTC in symptomatic population.

A Quantitative Comparison of Noise Reduction Across Five Commercial (Hybrid and Model-Based) Iterative Reconstruction Techniques: An Anthropomorphic Phantom Study

OBJECTIVE. The objective of our study was to compare the performance of three hybrid iterative reconstruction techniques (IRTs) (ASiR, iDose4, SAFIRE) and their respective strengths for image noise reduction on low-dose CT examinations using filtered back projection (FBP) as the standard reference. Also, we compared the performance of these three hybrid IRTs with two model-based IRTs (Veo and IMR) for image noise reduction on low-dose examinations. MATERIALS AND METHODS. An anthropomorphic abdomen phantom was scanned at 100 and 120 kVp and different tube current-exposure time products (25-100 mAs) on three CT systems (for ASiR and Veo, Discovery CT750 HD; for iDose4 and IMR, Brilliance iCT; and for SAFIRE, Somatom Definition Flash). Images were reconstructed using FBP and using IRTs at various strengths. Nine noise measurements (mean ROI size, 423 mm(2)) on extracolonic fat for the different strengths of IRTs were recorded and compared with FBP using ANOVA. Radiation dose, which was measured as the volume CT dose index and dose-length product, was also compared. RESULTS. There were no significant differences in radiation dose and image noise among the scanners when FBP was used (p > 0.05). Gradual image noise reduction was observed with each increasing increment of hybrid IRT strength, with a maximum noise suppression of approximately 50% (48.2-53.9%). Similar noise reduction was achieved on the scanners by applying specific hybrid IRT strengths. Maximum noise reduction was higher on model-based IRTs (68.3-81.1%) than hybrid IRTs (48.2-53.9%) (p < 0.05). CONCLUSION. When constant scanning parameters are used, radiation dose and image noise on FBP are similar for CT scanners made by different manufacturers. Significant image noise reduction is achieved on low-dose CT examinations rendered with IRTs. The image noise on various scanners can be matched by applying specific hybrid IRT strengths. Model-based IRTs attain substantially higher noise reduction than hybrid IRTs irrespective of the radiation dose.

Comparison of image quality between filtered back-projection and the adaptive statistical and novel model-based iterative reconstruction techniques in abdominal CT for renal calculi

ObjectivesTo compare image quality on computed tomographic (CT) images acquired with filtered back-projection (FBP), adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) techniques in CT kidney/ureter/bladder (KUB) examination.MethodsEighteen patients underwent standard protocol CT KUB at our institution. The same raw data were reconstructed using FBP, ASIR and MBIR. Objective [mean image noise, contrast-to-noise ratio (CNR) for kidney and mean attenuation values of subcutaneous fat] and subjective image parameters (image noise, image contrast, overall visibility of kidneys/ureters/bladder, visibility of small structures, and overall diagnostic confidence) were assessed using a scoring system from 1 (best) to 5 (worst).ResultsObjective image measurements revealed significantly less image noise and higher CNR and the same fat attenuation values for the MBIR technique (P < 0.05). MBIR scored best in all the subjective image parameters (P < 0.001) with averages ranging between 2.05–2.73 for MBIR, 2.95–3.10 for ASIR and 3.08–3.31 for FBP. No significant difference was observed between FBP and ASIR (P > 0.05), while there was a significant difference between ASIR vs. MBIR (P < 0.05). The mean effective dose was 3 mSv.ConclusionMBIR shows superior reduction in noise and improved image quality (both objective and subjective analysis) compared with ASIR and FBP CT KUB examinations.Main Messages• There are many reconstruction options in CT.• Novel model-based iterative reconstruction (MBIR) showed the least noise and optimal image quality.• For CT of the kidneys/ureters/bladder, MBIR should be utilised, if available.• Further studies to reduce the dose while maintaining image quality should be pursued.

Image Quality Assessment of Standard- and Low-Dose Chest CT Using Filtered Back Projection, Adaptive Statistical Iterative Reconstruction, and Novel Model-Based Iterative Reconstruction Algorithms

The purpose of this article is to compare image quality between filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR) at standard dose and two preselected low-dose scans. Thirty patients (16 men and 14 women; mean age, 67 years) were prospectively recruited. Patients underwent three scans (one standard-dose scan and two low-dose scans at noise indexes [NIs] of 33, 60, and 70, respectively). All three scans were reconstructed with FBP, ASIR, and MBIR. Objective and subjective image qualities were compared. Dose-length products and effective doses for each scans were recorded. Mean image noise and attenuation values were compared between different reconstruction algorithms using repeated-measures analysis of variance and paired Student t tests. The interobserver variation between the two radiologists for subjective image quality and lesion assessment was estimated by using weighted kappa statistics. Objective image analysis supports significant noise reduction with low-dose scans using the MBIR technique (p < 0.05). There was no significant change in mean CT numbers between different reconstructions (p > 0.05). Subjective analysis reveals no significant difference between image quality and diagnostic confidence between low-dose MBIR scans compared with standard-dose scans reconstructed using ASIR (p > 0.05). Average effective doses were 3.7, 1.2, and 0.9 mSv for standard scans at NIs of 33, 60, and 70, respectively. MBIR shows superior noise reduction and improved image quality. Substantial dose reduction can be achieved by increasing the NI parameters as tested in this study without affecting image quality and diagnostic confidence.

Assessment of Image Quality on Effects of Varying Tube Voltage and Automatic Tube Current Modulation With Hybrid and Pure Iterative Reconstruction Techniques in Abdominal/Pelvic CT

The objective of this study was to compare image quality on abdominal/pelvic computed tomographic images acquired with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and novel model-based iterative reconstruction (MBIR) techniques with varying levels of automatic tube current modulation and tube voltages. A 2-phase study was performed. In phase 1, a torso phantom was scanned at 17 different noise levels of automatic current modulation (selected using noise index [NI]) at 120 kilovolt (peak) (kVp). Images reconstructed with FBP, ASIR, and MBIR underwent objective analysis. In phase 2, additional scans were performed at 3 different kVp (80, 100, and 120 kVp at 3 different NIs (33, 50, and 70). Objective and subjective image qualities were assessed. Computed tomography dose index and dose-length products were recorded. The objective image analysis supports significant noise reduction with MBIR compared with ASIR and FBP (P < 0.05) at all 17 NI tested at 120 kVp. When lowering the kVp, the subjective image quality was improved, but when this is performed in conjunction with increasing NI, image quality was maintained only at moderately high NI of 50 but was degraded at higher NIs despite improving contrast-to-noise ratio. Our results represent the first exploration in the utility of MBIR technique with alteration of kVp in conjunction with tube current modulation in comparison with traditional methods. Objective image noise for MBIR is superior. Subjective image quality is only moderately improved. Scanning at low kVp and moderately high NI with MBIR can ensure that a balance of improved image noise and contrast can be achieved as well as reducing dose.

A Model-based Iterative Reconstruction Technique for Computer Tomography on Automated Contour of Small and Low Contrast Lesions

A Model-based Iterative Reconstruction Technique for Computer Tomography on Automated Contour of Small and Low Contrast Lesions

Image Quality Assessment in Torso Phantom Comparing Effects of Varying Automatic Current Modulation with Filtered Back Projection, Adaptive Statistical, and Model-Based Iterative Reconstruction Techniques in CT

ObjectivesTo compare image quality on computed tomographic (CT) images acquired with different levels of automatic tube current modulation reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and novel model-based iterative reconstruction (MBIR) techniques. MethodsA torso phantom was scanned at 17 different noise levels of automatic current modulation and images were reconstructed with FBP, ASIR, and MBIR. Objective and subjective image qualities were assessed. Effective dose was also calculated. ResultsObjective image analysis supports significant noise reduction and superior contrast to noise ratio with new a MBIR technique. Subjective image parameters were maximally rated for MBIR followed by ASIR then FBP. The reconstruction algorithms were evaluated over effective doses ranging from 0.7 to 3 mSv. ConclusionMBIR shows superior reduction in noise and improved image quality (both objective and subjective analysis) compared with ASIR and FBP. It was possible to achieve meaningful image quality even at the highest noise index of 70 achieving substantial dose reduction to as low as 0.7 mSv.

Model-Based Iterative Reconstruction Technique for Ultralow-Dose Computed Tomography of the Lung

The aim of this study was to assess the effectiveness of a model-based iterative reconstruction (MBIR) in improving image quality and diagnostic performance of ultralow-dose computed tomography (ULDCT) of the lung. The institutional review board approved this study, and all patients provided written informed consent. Fifty-two patients underwent low-dose computed tomography (LDCT) (screening-dose, 50 mAs) and ULDCT (4 mAs) of the lung simultaneously. The LDCT images were reconstructed with filtered back projection (LDCT-FBP images) and ULDCT images were reconstructed with both MBIR (ULDCT-MBIR images) and FBP (ULDCT-FBP images). On all the 156 image series, objective image noise was measured in the thoracic aorta, and 2 blinded radiologists independently assessed subjective image quality. Another 2 blinded radiologists independently evaluated the ULDCT-MBIR and ULDCT-FBP images for the presence of noncalcified and calcified pulmonary nodules; LDCT-FBP images served as the reference. Paired t test, Wilcoxon signed rank sum test, and free-response receiver-operating characteristic analysis were used for statistical analysis of the data. Compared with LDCT-FBP and ULDCT-FBP, ULDCT-MBIR had significantly reduced objective noise (both P <; 0.001). Subjective noise on the ULDCT-MBIR images was comparable with that on the LDCT-FBP images but lower than that on the ULDCT-FBP images (P <; 0.001). Artifacts on ULDCT-MBIR images were more numerous than those on the LDCT-FBP images (P = 0.007) but fewer than those on the ULDCT-FBP images (P <; 0.001). Compared with the LDCT-FBP images, ULDCT-MBIR and ULDCT-FBP images showed reduced image sharpness (both P <; 0.001). All the ULDCT-MBIR images showed a blotchy pixelated appearance; however, the performance of ULDCT-MBIR was significantly superior to that of ULDCT-FBP for the detection of noncalcified pulmonary nodules (P = 0.002). The average true-positive fractions for significantly sized noncalcified nodules (≥4 mm) and small noncalcified nodules (<;4 mm) on the ULDCT-MBIR images were 0.944 and 0.884, respectively, when LDCT-FBP images were used as reference. All of the calcified nodules were detected by both the observers on both the ULDCT-MBIR and ULDCT-FBP images. As compared with FBP, MBIR enables significant reduction of the image noise and artifacts and also better detection of noncalcified pulmonary nodules on ULDCT of the lung. Compared with LDCT-FBP images, ULDCT-MBIR images showed significantly reduced objective noise and comparable subjective image noise. Almost all of the noncalcified nodules and all of the calcified nodules could be detected on the ULDCT-MBIR images, when LDCT-FBP images were used as the reference.

Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique

To prospectively evaluate dose reduction and image quality characteristics of chest CT reconstructed with model-based iterative reconstruction (MBIR) compared with adaptive statistical iterative reconstruction (ASIR). One hundred patients underwent reference-dose and low-dose unenhanced chest CT with 64-row multidetector CT. Images were reconstructed with 50 % ASIR-filtered back projection blending (ASIR50) for reference-dose CT, and with ASIR50 and MBIR for low-dose CT. Two radiologists assessed the images in a blinded manner for subjective image noise, artefacts and diagnostic acceptability. Objective image noise was measured in the lung parenchyma. Data were analysed using the sign test and pair-wise Student's t-test. Compared with reference-dose CT, there was a 79.0 % decrease in dose-length product with low-dose CT. Low-dose MBIR images had significantly lower objective image noise (16.93 ± 3.00) than low-dose ASIR (49.24 ± 9.11, P < 0.01) and reference-dose ASIR images (24.93 ± 4.65, P < 0.01). Low-dose MBIR images were all diagnostically acceptable. Unique features of low-dose MBIR images included motion artefacts and pixellated blotchy appearances, which did not adversely affect diagnostic acceptability. Diagnostically acceptable chest CT images acquired with nearly 80 % less radiation can be obtained using MBIR. MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT images without severely compromising image quality. • Model-based iterative reconstruction (MBIR) creates high-quality low-dose CT images. • MBIR significantly improves image noise and artefacts over adaptive statistical iterative techniques. • MBIR shows greater potential than ASIR for diagnostically acceptable low-dose CT. • The prolonged processing time of MBIR may currently limit its routine use in clinical practice.