Model-based Iterative Reconstruction Algorithm Research Articles

Influence of model based iterative reconstruction algorithm on image quality of multiplanar reformations in reduced dose chest CT.

BackgroundModel-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown.PurposeTo evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition.Material and MethodsRaw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT.ResultsThe lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts.ConclusionThe MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique.

Robust X-Ray Phase Ptycho-Tomography

Synchrotron-based soft X-ray ptychography has enabled the reconstruction of both the phase and attenuation projections of samples relevant to the physical and biological sciences. The phase projection images typically have higher fidelity and hence are used for tomographic reconstruction. In practice, three-dimensional tomographic reconstruction can be challenging because the measurements may have outliers, a fluctuating background and may be restricted to a limited angular range of sample rotations. Thus, conventional reconstruction algorithms such as filtered back projection can result in reconstructions with strong artifacts. In this paper, we present a robust model-based iterative reconstruction algorithm for X-ray ptychography-based phase tomography. Our method casts the reconstruction as a regularized inverse problem, involving a novel data fitting term that accounts for noise, the fluctuating background as well as outliers, combined with an image model term that enforces regularity on the volume to be reconstructed. We use a majorization-minimization strategy to find a minimum of the formulated cost function. Reconstructions on a simulated as well as a real dataset show that it is possible to acquire high-quality phase reconstructions compared to the typically used filtered-back projection algorithm as well as conventional regularized inversion approaches.

Low contrast detectability and spatial resolution with model-based Iterative reconstructions of MDCT images: a phantom and cadaveric study.

To compare image quality [low contrast (LC) detectability, noise, contrast-to-noise (CNR) and spatial resolution (SR)] of MDCT images reconstructed with an iterative reconstruction (IR) algorithm and a filtered back projection (FBP) algorithm. The experimental study was performed on a 256-slice MDCT. LC detectability, noise, CNR and SR were measured on a Catphan phantom scanned with decreasing doses (48.8 down to 0.7mGy) and parameters typical of a chest CT examination. Images were reconstructed with FBP and a model-based IR algorithm. Additionally, human chest cadavers were scanned and reconstructed using the same technical parameters. Images were analyzed to illustrate the phantom results. LC detectability and noise were statistically significantly different between the techniques, supporting model-based IR algorithm (p < 0.0001). At low doses, the noise in FBP images only enabled SR measurements of high contrast objects. The superior CNR of model-based IR algorithm enabled lower dose measurements, which showed that SR was dose and contrast dependent. Cadaver images reconstructed with model-based IR illustrated that visibility and delineation of anatomical structure edges could be deteriorated at low doses. Model-based IR improved LC detectability and enabled dose reduction. At low dose, SR became dose and contrast dependent. • Model- based Iterative Reconstruction improves detectability of low contrast object. • With model- based Iterative Reconstruction, spatial resolution is dose and contrast dependent. • Model-based Iterative Reconstruction algorithms enable improved IQ combined with dose-reduction possibilities. • Improvement of SR and LC detectability on the same IMR data set would reduce reconstructions.

High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction

ObjectiveTo compare the image quality of high-resolution computed tomography (HRCT) for evaluating lung nodules reconstructed with the new version of model-based iterative reconstruction and spatial resolution preference algorithm (MBIRn) vs. conventional model-based iterative reconstruction (MBIRc) and adaptive statistical iterative reconstruction (ASIR). Materials and methodsThis retrospective clinical study was approved by our institutional review board and included 70 lung nodules in 58 patients (mean age, 71.2±10.9years; 34 men and 24 women). HRCT of lung nodules were reconstructed using MBIRn, MBIRc and ASIR. Objective image noise was measured by placing the regions of interest on lung parenchyma. Two blinded radiologists performed subjective image analyses. ResultsSignificant improvements in the following points were observed in MBIRn compared with ASIR (p<0.005): objective image noise (24.4±8.0 vs. 37.7±10.4), subjective image noise, streak artifacts, and adequateness for evaluating internal characteristics and borders of nodules. The sharpness of small vessels and bronchi and diagnostic acceptability with MBIRn were significantly better than with MBIRc and ASIR (p<0.008). ConclusionHRCT reconstructed with MBIRn provides diagnostically more acceptable images for the detailed analyses of lung nodules compared with MBIRc and ASIR.

Objective assessment of low contrast detectability in computed tomography with Channelized Hotelling Observer

PurposeIterative algorithms introduce new challenges in the field of image quality assessment. The purpose of this study is to use a mathematical model to evaluate objectively the low contrast detectability in CT. Materials and methodsA QRM 401 phantom containing 5 and 8 mm diameter spheres with a contrast level of 10 and 20 HU was used. The images were acquired at 120 kV with CTDIvol equal to 5, 10, 15, 20 mGy and reconstructed using the filtered back-projection (FBP), adaptive statistical iterative reconstruction 50% (ASIR 50%) and model-based iterative reconstruction (MBIR) algorithms. The model observer used is the Channelized Hotelling Observer (CHO). The channels are dense difference of Gaussian channels (D-DOG). The CHO performances were compared to the outcomes of six human observers having performed four alternative forced choice (4-AFC) tests. ResultsFor the same CTDIvol level and according to CHO model, the MBIR algorithm gives the higher detectability index. The outcomes of human observers and results of CHO are highly correlated whatever the dose levels, the signals considered and the algorithms used when some noise is added to the CHO model. The Pearson coefficient between the human observers and the CHO is 0.93 for FBP and 0.98 for MBIR. ConclusionThe human observers' performances can be predicted by the CHO model. This opens the way for proposing, in parallel to the standard dose report, the level of low contrast detectability expected. The introduction of iterative reconstruction requires such an approach to ensure that dose reduction does not impair diagnostics.

PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.

New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained.

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.

A Model-Based Image Reconstruction Algorithm With Simultaneous Beam Hardening Correction for X-Ray CT

Beam hardening is a well-known effect in X-ray CT scanning that is caused by the interaction of a broad polychromatic source spectrum with energy-dependent material attenuation. If the scanned object only consists of a single material, the beam hardening effect can be corrected by sinogram precorrection techniques. However, when multiple materials are present, it becomes much more difficult to fully compensate for this distortion; in general, the beam hardening can contribute to reconstruction artifacts such as cupping and streaking. In this paper, we present a novel model-based iterative reconstruction algorithm that incorporates beam hardening correction (MBIR-BHC). Unlike most correction algorithms, which require knowledge of the X-ray spectrum or mass attenuation functions, the MBIR-BHC algorithm works by simultaneously reconstructing the image and estimating the beam-hardening function. The method is based on the assumption that the object is formed by a combination of two distinct materials that can be separated based on their densities. We formulate a poly-energetic X-ray forward model using a polynomial function of two material projections: one for the low-density material and one for the high. We then develop an alternating minimization algorithm for jointly estimating the reconstructed image, the material segmentation, and the coefficients of the two component polynomial that models the beam-hardening function. With this approach, the spectrum and mass attenuation functions are not needed in advance, and the correction is adapted to the dataset being reconstructed. We examine the performance of the proposed algorithm using both simulated and real datasets. Results indicate that the MBIR-BHC algorithm significantly reduces several reconstruction artifacts without advanced knowledge of the X-ray spectrum and material properties.

Model-Based Iterative Reconstruction (MBIR) for the Reduction of Metal Artifacts on CT.

The purpose of this article is to study the added value of model-based iterative reconstruction (MBIR) on metal artifact reduction on CT compared with standard filtered back projection (FBP). Ex vivo imaging was performed on several metal implants. Datasets were reconstructed with standard FBP and MBIR algorithms. The sizes of the artifacts surrounding the metal implant were recorded and compared. In vivo imaging was performed on 62 patients with metal implants. Each dataset was reconstructed with FBP and MBIR algorithms. Objective image quality was assessed by measuring the size of the artifact generated by the metal implant. Subjective image quality was graded on a 3-point scale, taking into account the visibility of the bone-metal interface, as well as the visibility of the neighboring soft tissues. Ex vivo analysis yielded a reduction of 82% in the size of the artifact when using the MBIR algorithm, compared with the FBP algorithm. The mean (SD) size of the artifacts was 1.4 ± 0.8 and 0.25 ± 0.06 cm(2) with FBP and MBIR, respectively. In vivo, the mean size of the artifacts decreased from 7.3 ± 1.5 cm(2) to 4.0 ± 0.9 cm(2) for FBP and MBIR, respectively (p = 0.012). The subjective image quality analysis showed an equal or better bone-metal interface of MBIR algorithm in 85% of cases. Visibility of the soft tissue surrounding the metal implant was determined to be equal or better in 97% of cases in which MBIR was used. This study shows that the MBIR algorithm allows a clear reduction of metal artifacts on CT images and, hence, a better analysis of the soft tissue surrounding the metal implant compared with FBP.

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.

TIMBIR: A Method for Time-Space Reconstruction From Interlaced Views

Synchrotron X-ray computed tomography (SXCT) is increasingly being used for 3-D imaging of material samples at micron and finer scales. The success of these techniques has increased interest in 4-D reconstruction methods that can image a sample in both space and time. However, the temporal resolution of widely used 4-D reconstruction methods is severely limited by the need to acquire a very large number of views for each reconstructed 3-D volume. Consequently, the temporal resolution of current methods is insufficient to observe important physical phenomena. Furthermore, measurement nonidealities also tend to introduce ring and streak artifacts into the 4-D reconstructions. In this paper, we present a time-interlaced model-based iterative reconstruction (TIMBIR) method, which is a synergistic combination of two innovations. The first innovation, interlaced view sampling, is a novel method of data acquisition, which distributes the view angles more evenly in time. The second innovation is a 4-D model-based iterative reconstruction algorithm (MBIR), which can produce time-resolved volumetric reconstruction of the sample from the interlaced views. In addition to modeling both the sensor noise statistics and the 4-D object, the MBIR algorithm also reduces ring and streak artifacts by more accurately modeling the measurement nonidealities. We present reconstructions of both simulated and real X-ray synchrotron data, which indicate that TIMBIR can improve temporal resolution by an order of magnitude relative to existing approaches.

TU‐EF‐204‐03: Task‐Based KV and MAs Optimization for Radiation Dose Reduction in CT: From FBP to Statistical Model‐Based Iterative Reconstruction (MBIR)

Purpose:The introduction of the highly nonlinear MBIR algorithm to clinical CT systems has made CNR an invalid metric for kV optimization. The purpose of this work was to develop a task‐based framework to unify kV and mAs optimization for both FBP‐ and MBIR‐based CT systems.Methods:The kV‐mAs optimization was formulated as a constrained minimization problem: to select kV and mAs to minimize dose under the constraint of maintaining the detection performance as clinically prescribed. To experimentally solve this optimization problem, exhaustive measurements of detectability index (d’) for a hepatic lesion detection task were performed at 15 different mA levels and 4 kV levels using an anthropomorphic phantom. The measured d’ values were used to generate an iso‐detectability map; similarly, dose levels recorded at different kV‐mAs combinations were used to generate an iso‐dose map. The iso‐detectability map was overlaid on top of the iso‐dose map so that for a prescribed detectability level d’, the optimal kV‐mA can be determined from the crossing between the d’ contour and the dose contour that corresponds to the minimum dose.Results:Taking d’=16 as an example: the kV‐mAs combinations on the measured iso‐d’ line of MBIR are 80–150 (3.8), 100–140 (6.6), 120–150 (11.3), and 140–160 (17.2), where values in the parentheses are measured dose values. As a Result, the optimal kV was 80 and optimal mA was 150. In comparison, the optimal kV and mA for FBP were 100 and 500, which corresponded to a dose level of 24 mGy. Results of in vivo animal experiments were consistent with the phantom results.Conclusion:A new method to optimize kV and mAs selection has been developed. This method is applicable to both linear and nonlinear CT systems such as those using MBIR. Additional dose savings can be achieved by combining MBIR with this method.This work was partially supported by an NIH grant R01CA169331 and GE Healthcare. K. Li, D. Gomez‐Cardona, M. G. Lubner: Nothing to disclose. P. J. Pickhardt: Co‐founder, VirtuoCTC, LLC Stockholder, Cellectar Biosciences, Inc. G.‐H. Chen: Research funded, GE Healthcare; Research funded, Siemens AX.

Ultra-low-dose CT for left atrium and pulmonary veins imaging using new model-based iterative reconstruction algorithm

To evaluate the feasibility of ultra-low-dose CT for left atrium and pulmonary veins using new model-based iterative reconstruction (MBIR) algorithm. Two hundred patients scheduled for catheter ablation were randomized into two groups: Group 1 (100 patients, Multidetector row CT (MDCT) with MBIR, no ECG triggering, tube voltage and tube current of 100 kV and 60 mA, respectively) and Group 2 [100 patients, MDCT with adaptive statistical iterative reconstruction algorithm (ASIR), no ECG triggering, and kV and mA tailored on patient BMI]. Image quality, CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) of left atrium (LA) and pulmonary veins, and effective dose (ED) were evaluated for each exam and compared between two groups.No significant differences between groups in terms of population characteristics, cardiovascular risk factors, anatomical features, prevalence of persistent atrial fibrillation and image quality score. Statistically significant differences were found between Group 1 and Group 2 in mean attenuation, SNR, and CNR of LA. Significantly, lower values of noise were found in Group 1 versus Group 2. Group 1 showed a significantly lower mean ED in comparison with Group 2 (0.41 ± 0.04 versus 4.17 ± 2.7 mSv). The CT for LA and pulmonary veins imaging using MBIR is feasible and allows examinations with very low-radiation exposure without loss of image quality.

Clinical impact of model-based type iterative reconstruction with fast reconstruction time on image quality of low-dose screening chest CT.

Model-based type iterative reconstruction algorithms with fast reconstruction times are now available. The clinical feasibility of their reconstruction has not been evaluated adequately. To investigate the effects of model-based type iterative reconstruction, i.e. iterative model reconstruction (IMR), with fast reconstruction time on the qualitative and quantitative image quality at low-dose chest computed tomography (CT). Thirty-one patients undergoing low-dose screening chest CT were enrolled. Images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (HIR), and IMR algorithms. The CT attenuation and image noise for all reconstructions were calculated at the lung apex, middle, and base. Using a 4-point scale, two reviewers visually evaluated the image quality with respect to vessel sharpness, streak artifact, the mediastinum, and the overall image quality of each reconstruction method. The mean estimated effective dose was 1.0 ± 0.3 mSv. There was no significant difference in the CT attenuation among the three reconstructions. The mean image noise of FBP, HIR, and IMR images was 124.3 ± 57.3, 34.8 ± 10.2, and 22.9 ± 5.8 HU, respectively. There were significant differences for all comparison combinations among the three methods (P < 0.01). The best subjective overall image quality for the lung and mediastinum was obtained with IMR (P < 0.01). The reconstruction time for IMR was within 3 min in all cases. At low-dose chest CT, IMR can improve the qualitative and quantitative visualization of both lung and mediastinal structures especially in the lung apex at a clinically acceptable reconstruction time. Its application may improve diagnostic performance.

Model-Based Iterative Reconstruction for Bright-Field Electron Tomography

Bright-Field (BF) electron tomography (ET) has been widely used in the life sciences for 3-D imaging of biological specimens. However, while BF-ET is popular in the life sciences, 3-D BF-ET imaging has been avoided in the physical sciences due to measurement anomalies from crystalline samples caused by dynamical diffraction effects such as Bragg scatter. In practice, these measurement anomalies cause undesirable artifacts in 3-D reconstructions computed using filtered back-projection (FBP). Alternatively, model-based iterative reconstruction (MBIR) is a powerful framework for tomographic reconstruction that combines a forward model for the measurement system and a prior model for the object to obtain reconstructions by minimizing a single cost function. In this paper, we present an MBIR algorithm for BF-ET reconstruction from crystalline materials that can account for the presence of anomalous measurements. We propose a new forward model for the acquisition system which accounts for the presence of anomalous measurements and combine it with a prior model for the object to obtain the MBIR cost function. We then propose a fast algorithm based on majorization–minimization to find a minimum of the corresponding cost function. Results on simulated as well as real data show that our method can dramatically improve reconstruction quality as compared to FBP and conventional MBIR without anomaly modeling.

투과 컴퓨터 단층촬영을 위한 모델 기반 반복연산 재구성에서 투사선 구동 시스템 모델의 성능 비교

The key to model-based iterative reconstruction (MBIR) algorithms for transmission computed tomography lies in the ability to accurately model the data formation process from the emitted photons produced in the transmission source to the measured photons at the detector. Therefore, accurately modeling the system matrix that accounts for the data formation process is a prerequisite for MBIR-based algorithms. In this work we compared quantitative performance of the three representative ray-driven methods for calculating the system matrix; the ray-tracing method (RTM), the distance-driven method (DDM), and the strip-area based method (SAM). We implemented the ordered-subsets separable surrogates (OS-SPS) algorithm using the three different models and performed simulation studies using a digital phantom. Our experimental results show that, in spite of the more advanced features in the SAM and DDM, the traditional RTM implemented in the OS-SPS algorithm with an edge-preserving regularizer out-performs the SAM and DDM in restoring complex edges in the underlying object. The performance of the RTM in smooth regions was also comparable to that of the SAM or DDM.

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.

Image Quality in Children with Low-Radiation Chest CT Using Adaptive Statistical Iterative Reconstruction and Model-Based Iterative Reconstruction

ObjectiveTo evaluate noise reduction and image quality improvement in low-radiation dose chest CT images in children using adaptive statistical iterative reconstruction (ASIR) and a full model-based iterative reconstruction (MBIR) algorithm.MethodsForty-five children (age ranging from 28 days to 6 years, median of 1.8 years) who received low-dose chest CT scans were included. Age-dependent noise index (NI) was used for acquisition. Images were retrospectively reconstructed using three methods: MBIR, 60% of ASIR and 40% of conventional filtered back-projection (FBP), and FBP. The subjective quality of the images was independently evaluated by two radiologists. Objective noises in the left ventricle (LV), muscle, fat, descending aorta and lung field at the layer with the largest cross-section area of LV were measured, with the region of interest about one fourth to half of the area of descending aorta. Optimized signal-to-noise ratio (SNR) was calculated.ResultIn terms of subjective quality, MBIR images were significantly better than ASIR and FBP in image noise and visibility of tiny structures, but blurred edges were observed. In terms of objective noise, MBIR and ASIR reconstruction decreased the image noise by 55.2% and 31.8%, respectively, for LV compared with FBP. Similarly, MBIR and ASIR reconstruction increased the SNR by 124.0% and 46.2%, respectively, compared with FBP.ConclusionCompared with FBP and ASIR, overall image quality and noise reduction were significantly improved by MBIR. MBIR image could reconstruct eligible chest CT images in children with lower radiation dose.

X-Ray Dose Reduction in Abdominal Computed Tomography Using Advanced Iterative Reconstruction Algorithms

ObjectiveThis work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) algorithms in reducing computed tomography (CT) radiation dosages in abdominal imaging.MethodsCT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the filtered back-projection (FBP), 50% ASiR and MBIR algorithms and compared. The CT value, image noise and contrast-to-noise ratios (CNRs) of the reconstructed abdominal images were measured. Volumetric CT dose indexes (CTDIvol) were recorded.ResultsAt different tube currents, 50% ASiR and MBIR significantly reduced image noise and increased the CNR when compared with FBP. The minimal tube current values required by FBP, 50% ASiR, and MBIR to achieve acceptable image quality using this phantom were 200, 140, and 80 mA, respectively. At the identical image quality, 50% ASiR and MBIR reduced the radiation dose by 35.9% and 59.9% respectively when compared with FBP.ConclusionsAdvanced iterative reconstruction techniques are able to reduce image noise and increase image CNRs. Compared with FBP, 50% ASiR and MBIR reduced radiation doses by 35.9% and 59.9%, respectively.

Model-based Iterative Reconstruction: A Promising Algorithm for Today's Computed Tomography Imaging

Because of its fast image acquisition and the rich diagnostic information it provides, computed tomography (CT) has gradually become a popular imaging modality among clinicians. Because CT scanners emit x-rays, the increased use of CT in clinical applications inevitably leads to increased medical radiation dose to the population. Because of the well-known cancer-inducing effects of high dose x-ray radiation, this increased dose has caused concerns among policy makers and general public that CT patients may be at a higher risk of developing cancer. Over the years, CT manufacturers have developed a variety of strategies to address this issue, the latest being a model-based iterative reconstruction (MBIR) algorithm. MBIR is an advanced CT algorithm that incorporates modeling of several key parameters that were omitted in earlier algorithms to reduce computational requirement and speed up scans. This review article examines the latest literature in the clinical CT field and discusses the general principles of MBIR, its dose and noise reduction potentials, its imaging characteristics, and its limitations. MBIR algorithm and its application in today's CT imaging will greatly reduce the radiation dose to patients and improve image quality for clinicians.