Generalized iterative sparse-view CT reconstruction with deep neural network

BackgroundIterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).PurposeTo examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.Material and MethodsThe LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.ResultsThe image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).ConclusionAn ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen. A total of 64 patients (BMI<28) are selected for the enhanced upper abdomen scan and divided evenly into two groups. The tube voltages in Group A are 100kV in arterial phase and 80kV in venous phase, while tube voltages are 120kV during two phases in Group B. Image reconstruction algorithms used in Group A include the filtered back projection (FBP) algorithm, the adaptive statistical iterative reconstruction-Veo (ASIR-V 40% and 80%) algorithm, and the DLIR algorithm (DL-L, DL-M, DL-H). Image reconstruction algorithm used in Group B is ASIR-V40%. The different reconstruction algorithm images are used to measure the common hepatic artery, liver, renal cortex, erector spinae, and subcutaneous adipose in the arterial phase and the average CT value and standard deviation of the portal vein, liver, spleen, erector spinae, and subcutaneous adipose in the portal phase. The signal-to-noise ratio (SNR) is calculated, and the images are also scored subjectively. In Group A, noise in the aorta, liver, portal vein (the portal phase), spleen (the portal phase), renal cortex, retroperitoneal adipose, and muscle is significantly lower in both the DL-H and ASIR-V80% images, and the SNR is significantly higher than those in the remaining groups (P<0.05). The SNR of each tissue and organ in Group B is not significantly different from that in DL-M, DL-L, and ASIR-V40% in Group A (P>0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P<0.05). For upper abdominal low-kilovolt enhanced scan data, the DLIR-H gear yields a more satisfactory image quality than the FBP and ASIR-V.

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and shorten the scan time, while the severe streak artifacts will compromise anatomical information. How to reconstruct high-quality images from sparsely sampled projections is a challenging ill-posed problem. In this context, we propose the unrolled Deep Residual Error iterAtive Minimization Network (DREAM-Net) based on a novel iterative reconstruction framework to synergize the merits of deep learning and iterative reconstruction. DREAM-Net performs constraints using deep neural networks in the projection domain, residual space, and image domain simultaneously, which is different from the routine practice in deep iterative reconstruction frameworks. First, a projection inpainting module completes the missing views to fully explore the latent relationship between projection data and reconstructed images. Then, the residual awareness module attempts to estimate the accurate residual image after transforming the projection error into the image space. Finally, the image refinement module learns a non-standard regularizer to further fine-tune the intermediate image. There is no need to empirically adjust the weights of different terms in DREAM-Net because the hyper-parameters are embedded implicitly in network modules. Qualitative and quantitative results have demonstrated the promising performance of DREAM-Net in artifact removal and structural fidelity.

3D or multi-dimensional medical image reconstruction is attracting more and more attention in modern era. Rapid volumetric image reconstruction is of paramount importance to clinicians for prompt diagnosis and analysis of complex tissue alternations. However, due to a high demand on computation for either the analytical or iterative image reconstruction algorithms, it is almost impossible to meet the requirement of real-time reconstruction. On the other hand, with high computing capability and broad banding characteristic, recently multi-core processors provide a new possibility and hopeful start for some of the most computation-demanding applications, such as medical imaging, digital media and entertainment equipment. This study aims to parallelize an analytical 3D CT image reconstruction algorithm, the so-called FDK method, and to implement it on an existing multi-core platform system, towards a real-time 3D medical image reconstruction. In this paper, the parallelization of the 3D image reconstruction algorithm, the coding optimizations and a comparative result of run time savings are described.

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.

BackgroundThe arms-down position increases computed tomography (CT) radiation dose. Iterative reconstruction (IR) could enhance image quality without increasing radiation dose in patients with arms-down position.AimTo investigate the feasibility of reduced-dose CT with IR for patients with inappropriate arm positioningMethodsTwenty patients who underwent two-phase abdominopelvic CT including standard-dose and reduced-dose CT (performed with 80% of the radiation dose of the standard protocol) with their arms positioned in the abdominal area were included in this study. Reduced-dose CT images were reconstructed using filtered back projection (FBP), hybrid IR, and iterative model reconstruction (IMR). These images were compared with standard-dose CT images reconstructed with FBP. Objective image noise in the liver and subcutaneous fat was measured by standard deviation for the quantitative analysis. Then, two radiologists qualitatively assessed beam hardening artifacts, artificial texture, noise, sharpness, and overall image quality in consensus.ResultsReduced-dose CT with all IR levels had lower objective image noise compared to standard-dose CT with FBP reconstruction (P < 0.05). Quantitatively measured beam hardening artifacts were similar in reduced-dose CT with iDose levels 5–6 and fewer with IMR compared to standard-dose CT. In the qualitative analysis, beam hardening artifacts and noise decreased as the IR levels increased. However, artificial texture was significantly aggravated with iDose 5–6 and IMR, and overall image quality significantly worsened with IMR.ConclusionsIR algorithms can reduce beam hardening artifacts in a reduced-dose CT setting in patients with arms-down position, and an intermediate level of hybrid IR allows radiologists to obtain the best image quality. Because the retrospective and single-center nature of our study limited the number of patients, multicenter prospective clinical studies are required to validate our results.

Improved dose calculation accuracy for low energy brachytherapy by optimizing dual energy CT imaging protocols for noise reduction using sinogram affirmed iterative reconstruction

To prospectively compare traditional filtered back projection (FBP) and iterative image reconstruction for the evaluation of heavily calcified arteries with coronary computed tomography (CT) angiography. The study had institutional review board approval and was HIPAA compliant. Written informed consent was obtained from all patients. Fifty-five consecutive patients (35 men, 20 women; mean age, 58 years ± 12 [standard deviation]) with Agatston scores of at least 400 underwent coronary CT angiography and cardiac catheterization. Image data were reconstructed with both FBP and iterative reconstruction techniques with corresponding cardiac algorithms. Image noise and subjective image quality were compared. To objectively assess the effect of FBP and iterative reconstruction on blooming artifacts, volumes of circumscribed calcifications were measured with dedicated volume analysis software. FBP and iterative reconstruction series were independently evaluated for coronary artery stenosis greater than 50%, and their diagnostic accuracy was compared, with cardiac catheterization as the reference standard. Statistical analyses included paired t tests, Kruskal-Wallis analysis of variance, and a modified McNemar test. Image noise measured significantly lower (P = .011-.035) with iterative reconstruction instead of FBP. Image quality was rated significantly higher (P = .031 and .042) with iterative reconstruction series than with FBP. Calcification volumes measured significantly lower (P = .019 and .026) with iterative reconstruction (44.3 mm(3) ± 64.7 and 46.2 mm(3) ± 68.8) than with FBP (54.5 mm(3) ± 69.5 and 56.3 mm(3) ± 72.5). Iterative reconstruction significantly improved some measures of per-segment diagnostic accuracy of coronary CT angiography for the detection of significant stenosis compared with FBP (accuracy: 95.9% vs 91.8%, P = .0001; specificity: 95.8% vs 91.2%, P = .0001; positive predictive value: 76.9% vs 61.1%, P = .0001). Iterative reconstruction reduces image noise and blooming artifacts from calcifications, leading to improved diagnostic accuracy of coronary CT angiography in patients with heavily calcified coronary arteries.

Electrical capacitance tomography (ECT) is used to obtain information about the distribution of a mixture of dielectric materials inside a vessel or pipe. ECT has several advantages over other reconstruction algorithms and has found many applications in the industrial fields. However, there are some difficulties with image reconstruction in ECT: The relationship between the permittivity distribution and measured capacitance is nonlinear. And inverse problem is ill-posed so that the inverse solution is sensitive to measurement error. To cope with these difficulties iterative image reconstruction algorithms have been developed. In general, the iterative reconstruction algorithms in ECT have comparatively good-quality in reconstructed images but result in intensive computational burden. This paper presents the iterative image reconstruction algorithm for ECT that can enhance the speed of image reconstruction without degradation in the quality of reconstructed image. The main contribution of the proposed algorithm is new weighting matrices, which are obtained by the interpolation of the grouped electrical field centre lines (EFCLs). Extensive simulation results have demonstrated that proposed algorithm provides improved reconstruction performance in terms of computational time and image quality.

The purpose of this study is to evaluate the physical image quality of a commercially available image‐based iterative reconstruction (IIR) system for two object contrasts to resemble a soft tissue (60 HU) and an enhanced vessel (270 HU), and compare the results with those of filtered back projection (FBP) and iterative reconstruction (IR). A 192‐slice computed tomography (CT) scanner was used for data acquisitions. IIR images were processed from the FBP images. Task‐based in‐plane transfer function (TTF) and slice sensitivity profile (SSPtask) were measured from rod objects inside of a 25‐cm diameter water phantom at four dose levels (2.5, 5, 10, and 20 mGy). Noise power spectrum (NPS) was measured from the water‐only part. System performance (SP) function was calculated as TTF2/NPS over FBP, IR, and IIR for comparison. In addition, an image subtraction was performed using images of rod objects, a bar‐pattern phantom, and a clinical abdomen case to observe the noise reduction performance of IIR. As a results, IIR mostly preserved TTF and SSPtask of FBP, whereas IR exhibited enhanced TTF at 10 and 20 mGy for 60 HU contrast and at all doses for 270 HU contrast. SP of IIR at 2.5, 5, 10 mGy (half doses) were similar to those of FBP at 5, 10, 20 mGy, respectively. IR exhibited enhanced SP at medium to high frequencies. The subtracted images showed weak remained edge signals in the bar‐pattern and abdominal images. In conclusion, IIR uniformly improved the task‐based image quality of FBP over the entire frequency range, whereas IR improved the characteristics over medium to high frequencies. The dose reduction potential of IIR estimated from SP is approximately 50%, when allowing the slight signal reductions.

Evaluation of Left Ventricular Volumes and Ejection Fraction from Gated Myocardial Perfusion SPECT Processed with "Myovation Evolution": Comparison of Three Automated Software Packages using Cardiac Magnetic Resonance as Reference.

We evaluated the feasibility of sub-millisievert (mSv) coronary CT angiography (CCTA) using low tube voltage, prospective ECG gating, and a knowledge-based iterative model reconstruction algorithm. Twenty-four non-obese healthy subjects (M:F 13:11; mean age 50.2±7.8years) were enrolled. Three sets of CT images were reconstructed using three different reconstruction methods: filtered back projection (FBP), iterative reconstruction (IR), and knowledge-based iterative model reconstruction (IMR). The scanning parameters were as follows: step-and-shoot axial scanning, 80kVp, and 200mAs. On the three sets of CT images, the attenuation and image noise values were measured at the aortic root. The signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were calculated at the proximal right coronary artery and the left main coronary artery. The qualitative image quality of the CCTA with IMR was assessed using a 4-point grading scale (grade 1, poor; grade 4, excellent). The mean radiation dose of the CCTA was 0.89±0.09mSv. The attenuation values with IMR were not different from those of other reconstruction methods. The image noise with IMR was significantly lower than with IR and FBP. Compared to FBP, the noise reduction rate of IMR was 69%. The SNR and CNR of CCTA with IMR were significantly higher than with FBP or IR. On the qualitative analysis with IMR, all included segments were diagnostic (grades 2, 3, and 4), and the mean image quality score was 3.6±0.6. In conclusion, CCTA with low tube voltage, prospective ECG gating, and an IMR algorithm might be a feasible method that allows for sub-millisievert radiation doses and good image quality when used with non-obese subjects.

Purpose: MTF of iterative reconstruction (IR) CT is known to be contrast dependent, which is of critical importance for predicting signal response of lesions with different contrast. Previous studies attempting to characterize the contrast dependant MTF of IR CT were carried out using multi contrast phantoms, which is of limited effect in revealing the range of MTF change between extremely high and low contrast objects. This study presents a novel technique for assessing the range of MTF changes and compares the measurement results in four IR CT. Methods: An AAPM phantom was scanned with 4 different CT systems (Discovery 750, GE, Somatom Definition, Siemens, Ingenuity, Philips, and Aquillion One, Toshiba) at same dose level (12.5mGy), and was reconstructed using FBP and IR techniques (ASIR and VEO in GE, Safire in Siemens, iDose4 in Philips, and AIDR in Toshiba). Total MTF was measured using a small bead in the phantom to characterize frequency response for high contrast objects. In addition, the algorithmic MTF was derived from noise power spectrum (NPS) using uniform background images in order to evaluate the frequency response for very low contrast objects. Results: The algorithmic MTF 50 of FBP showed higher responses up to 16.5% than total MTF 50 in all 4 CT systems, which reflected the decoupling of focal spot blurring effect. The algorithmic MTF 50 ofIR recons was lower that total MTF 50 in2 vendors(GE, Siemens) showing up to 48% reduction of MTF 50 in VEO, whereas IR recons from the other 2 vendors showed no marked reduction of algorithmic MTF 50. Conclusion: Our study revealed the degrading trends of MTF for low contrast objects in some IR technique. This characteristic needs to be considered in interpreting signal patterns of low contrast lesions in IR CT. The research was supported by the Converging Research Center Program through the Ministry of Education, Science and Technology (2012K001498).

A newly-developed metal artifact reduction algorithm improves the visibility of oral cavity lesions on 320-MDCT volume scans

This paper presents an improved CT (Computed Tomography) image reconstruction algorithm, called PB-BPF (Parallel-Beam Back projection Filtration). It mains that the image is reconstructed under the parallel beam scanning mode. It doesn't need to resample to get the image, and it simplifies the usual BPF (Back projection Filtration) in helical cone-beam CT. At First, The projection data is differentiated by using the Shepp-Logan model. Then, the image is reconstructed by choosing parallel PI beams to back-projection, Hilbert transform and constant compensation. At last, the image quality is assessed by using peak signal-to-noise ratio and structural similarity. Being compared with FBP(Filtered Back Projection), the results indicate that the improved algorithm can reconstruct the image accurately, at the same time, it is more close to human visual system.