Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners

Muckley, Matthew J.; Chen, Baiyu; Vahle, Thomas; O’Donnell, Thomas; Knoll, Florian; Sodickson, Aaron D; Sodickson, Daniel K.; Otazo, Ricardo

doi:10.1088/1361-6560/ab2df1

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…In this work, we demonstrated the working principle of a novel 4D moving blocker method, which is the first time, to our best knowledge, to combine a moving blocker with 4D CBCT to achieve simultaneous dose reduction and SC. Although there were a few studies using blockers/modulators for SC in 3D CBCT (Zhu et al 2006, Ouyang et al 2013, Zhu 2016 or using moving multi-slit collimator (MSC) for dose reduction in MDCT (Koesters et al 2017, Muckley et al 2017, all of them are limited to 2D or 3D reconstruction. The current study is the first study that utilizes the random under-sampling in each projection view for imaging and reconstruction of a moving object (lung here), which can be modeled as a 4D signal in nature.…”

Section: Discussionmentioning

confidence: 99%

4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction

Zhao

Zhong

Duan³

et al. 2018

Phys. Med. Biol.

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Four-dimensional (4D) x-ray cone-beam computed tomography (CBCT) is important for a precise radiation therapy for lung cancer. Due to the repeated use and 4D acquisition over a course of radiotherapy, the radiation dose becomes a concern. Meanwhile, the scatter contamination in CBCT deteriorates image quality for treatment tasks. In this work, we propose the use of a moving blocker (MB) during the 4D CBCT acquisition ('4D MB') and to combine motion-compensated reconstruction to address these two issues simultaneously. In 4D MB CBCT, the moving blocker reduces the x-ray flux passing through the patient and collects the scatter information in the blocked region at the same time. The scatter signal is estimated from the blocked region for correction. Even though the number of projection views and projection data in each view are not complete for conventional reconstruction, 4D reconstruction with a total-variation (TV) constraint and a motion-compensated temporal constraint can utilize both spatial gradient sparsity and temporal correlations among different phases to overcome the missing data problem. The feasibility simulation studies using the 4D NCAT phantom showed that 4D MB with motion-compensated reconstruction with 1/3 imaging dose reduction could produce satisfactory images and achieve 37% improvement on structural similarity (SSIM) index and 55% improvement on root mean square error (RMSE), compared to 4D reconstruction at the regular imaging dose without scatter correction. For the same 4D MB data, 4D reconstruction outperformed 3D TV reconstruction by 28% on SSIM and 34% on RMSE. A study of synthetic patient data also demonstrated the potential of 4D MB to reduce the radiation dose by 1/3 without compromising the image quality. This work paves the way for more comprehensive studies to investigate the dose reduction limit offered by this novel 4D MB method using physical phantom experiments and real patient data based on clinical relevant metrics.

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Section: Discussionmentioning

confidence: 99%

4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction

Zhao

Zhong

Duan³

et al. 2018

Phys. Med. Biol.

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“…Some scholars reconstructed multiorientation simultaneous computation of back-projection (MSBP) between image pixels and projection rays in different projection directions [11]. In the processing of CT images, MSBP algorithm can be used for rapid calculation in the space domain of direct back-projection, further reducing the amount of calculation in pixel positioning operation [12][13][14]. At present, there are very few data to apply HIR algorithm to CT scan images of bladder cancer patients.…”

Section: Introductionmentioning

confidence: 99%

Multiorientation Simultaneous Computation of Back-Projection CT Image Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer

Zhu

et al. 2022

Computational and Mathematical Methods in Medicine

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The objective of this research was to investigate the multidirectional synchronous calculation of the back-projection computed tomography (CT) image reconstruction algorithm (MSBP) in the staging diagnosis of bladder cancer. Sixty patients with bladder cancer admitted to the hospital were selected for enhanced CT scanning, all of which were randomly divided into control group ( n = 30 ) and study group ( n = 30 ). The filtered back-projection (FBP) algorithm was employed to reconstruct the scanned image, and the MSBP was additionally applied to the images of the study group. Fringe artifact (SA), overall mass (OQ), effective radiation dose (ED), CT dose-exponential volume (CTDI), and dose-length product (DLP) of the two groups of images were compared and analyzed. The results showed that the total time of the traditional algorithm was 5.473 s, and the total time of MSBP combined with FBP algorithm was 2.832 s, which was significantly higher than that of the traditional algorithm ( P < 0.05 ). CT scan bladder cancer staging results of all patients were compared with surgical pathological staging results, and the results were evaluated according to the coincidence rate. SA in the study group was lower than that in the control group ( P < 0.05 ), and OQ was not statistically significant. The ED of the study group was significantly lower than that of the control group by 33%. The coincidence rate of postoperative pathological staging results and CT staging results was 96%, and T1, T2a, and T4 coincidence rate was 100%, The coincidence rates of T2b, T3a, and T3b were 90%, 83.3%, and 66.67%, respectively. In summary, using MSBP method combined with FBP algorithm can improve OQ while reducing ED of patients. The introduction of MSBP into CT reconstruction image simplified the pixel location operation of projection calculation, showing an important application value in preoperative staging diagnosis of bladder cancer.

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“…Advances in technology have focused on reducing radiation exposures while preserving image quality. These include efficiencies in detector technology, 2 x-ray beam filtration, 3 sparse image sampling, 4,5 and image reconstruction algorithms. 6,7 Another approach can use deep learning to facilitate low radiation dose imaging, increasingly feasible with computational advances.…”

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confidence: 99%

Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis

Azour¹,

Hu²,

Ko³

et al. 2023

J Comput Assist Tomogr

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PurposeTo assess deep learning denoised (DLD) computed tomography (CT) chest images at various low doses by both quantitative and qualitative perceptual image analysis.MethodsSimulated noise was inserted into sinogram data from 32 chest CTs acquired at 100 mAs, generating anatomically registered images at 40, 20, 10, and 5 mAs. A DLD model was developed, with 23 scans selected for training, 5 for validation, and 4 for test.Quantitative analysis of perceptual image quality was assessed with Structural SIMilarity Index (SSIM) and Fréchet Inception Distance (FID). Four thoracic radiologists graded overall diagnostic image quality, image artifact, visibility of small structures, and lesion conspicuity. Noise-simulated and denoised image series were evaluated in comparison with one another, and in comparison with standard 100 mAs acquisition at the 4 mAs levels. Statistical tests were conducted at the 2-sided 5% significance level, with multiple comparison correction.ResultsAt the same mAs levels, SSIM and FID between noise-simulated and reconstructed DLD images indicated that images were closer to a perfect match with increasing mAs (closer to 1 for SSIM, and 0 for FID).In comparing noise-simulated and DLD images to standard-dose 100-mAs images, DLD improved SSIM and FID. Deep learning denoising improved SSIM of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in SSIM from 0.91 to 0.94, 0.87 to 0.93, 0.67 to 0.87, and 0.54 to 0.84, respectively. Deep learning denoising improved FID of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in FID from 20 to 13, 46 to 21, 104 to 41, and 148 to 69, respectively.Qualitative image analysis showed no significant difference in lesion conspicuity between DLD images at any mAs in comparison with 100-mAs images. Deep learning denoising images at 10 and 5 mAs were rated lower for overall diagnostic image quality (P < 0.001), and at 5 mAs lower for overall image artifact and visibility of small structures (P = 0.002), in comparison with 100 mAs.ConclusionsDeep learning denoising resulted in quantitative improvements in image quality. Qualitative assessment demonstrated DLD images at or less than 10 mAs to be rated inferior to standard-dose images.

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Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners

Cited by 11 publications

References 34 publications

4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction

4D cone-beam computed tomography (CBCT) using a moving blocker for simultaneous radiation dose reduction and scatter correction

Multiorientation Simultaneous Computation of Back-Projection CT Image Reconstruction Algorithm in Staging Diagnosis of Bladder Cancer

Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis

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