scholarly journals Image quality and radiologists’ subjective acceptance using model-based iterative and deep learning reconstructions as adjuncts to ultrahigh-resolution CT in low-dose contrast-enhanced abdominopelvic CT: phantom and clinical pilot studies

2021 ◽  
Author(s):  
Makiko Nishikawa ◽  
Haruhiko Machida ◽  
Yuta Shimizu ◽  
Toshiya Kariyasu ◽  
Hiroyuki Morisaka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Low Dose ◽  
Tube Voltage ◽  
Ultrahigh Resolution ◽  
Matrix Size ◽  
Noise Index ◽  
Model Based ◽  
Contrast Enhanced ◽  
Abdominopelvic Ct

Abstract Purpose In contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiation. We compared image quality and radiologists’ acceptance of model-based iterative (MBIR) and deep learning (DLR) reconstructions of low-dose CE-APCT by UHRCT. Methods Using our high-resolution (matrix size: 1024) and low-dose (tube voltage 100 kV; noise index: 20–40 HU) protocol, we scanned phantoms to compare the modulation transfer function and noise power spectrum between MBIR and DLR and assessed findings in 36 consecutive patients who underwent CE-APCT (noise index: 35 HU; mean CTDIvol: 4.2 ± 1.6 mGy) by UHRCT. We used paired t-test to compare objective noise and contrast-to-noise ratio (CNR) and Wilcoxon signed-rank test to compare radiologists’ subjective acceptance regarding noise, image texture and appearance, and diagnostic confidence between MBIR and DLR using our routine protocol (matrix size: 512; tube voltage: 120 kV; noise index: 15 HU) for reference. Results Phantom studies demonstrated higher spatial resolution and lower low-frequency noise by DLR than MBIR at equal doses. Clinical studies indicated significantly worse objective noise, CNR, and subjective noise by DLR than MBIR, but other subjective characteristics were better (P < 0.001 for all). Compared with the routine protocol, subjective noise was similar or better by DLR, and other subjective characteristics were similar or worse by MBIR. Conclusion Image quality, except regarding noise characteristics, and acceptance by radiologists were better by DLR than MBIR in low-dose CE-APCT by UHRCT. Graphical abstract

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis

2021 ◽  
pp. 028418512110358
Author(s):  
Aurélien Delabie ◽  
Roger Bouzerar ◽  
Raphaël Pichois ◽  
Xavier Desdoit ◽  
Jérémie Vial ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
Intraclass Correlation ◽  
Reconstruction Algorithm ◽  
Urinary Stones ◽  
Abdominopelvic Ct ◽  
Noise Ratio

Background Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction. Purpose To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT. Material and Methods A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers. Results Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (<3 mm), results were different (ICC = 0.77 vs. 0.86). Conclusion For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

Image quality in liver CT: low-dose deep learning vs standard-dose model-based iterative reconstructions

2021 ◽  
Author(s):  
Sungeun Park ◽  
Jeong Hee Yoon ◽  
Ijin Joo ◽  
Mi Hye Yu ◽  
Jae Hyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Low Dose ◽  
Standard Dose ◽  
Liver Ct ◽  
Model Based ◽  
Dose Model

scholarly journals Deep learning–based reconstruction may improve non-contrast cerebral CT imaging compared to other current reconstruction algorithms

2021 ◽  
Author(s):  
Luuk J. Oostveen ◽  
Frederick J. A. Meijer ◽  
Frank de Lange ◽  
Ewoud J. Smit ◽  
Sjoert A. Pegge ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Quality Characteristics ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Contrast Ct ◽  
Lower Noise ◽  
Reconstruction Time

Abstract Objectives To evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT). Methods Cerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests. Results For all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively. Conclusions With a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times. Key Points • Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.

scholarly journals Opportunities to reduce the radiation exposure during computed tomography to assess the changes in the lungs in patients with COVID-19: use of adaptive statistical iterative reconstruction

2021 ◽  
Vol 2 (2) ◽  
pp. 94-104
Author(s):  
Daria A. Filatova ◽  
Valentin E. Sinitsin ◽  
Elena A. Mershina
Keyword(s):  
Image Quality ◽  
Low Dose ◽  
Diagnostic Value ◽  
University Hospital ◽  
State University ◽  
Radiation Load ◽  
Adaptive Statistical Iterative Reconstruction ◽  
Tube Voltage ◽  
Statistical Iterative Reconstruction ◽  
Moscow State University

BACKGROUND: Several COVID-19 patients are subjected to multiple imaging examinations during hospitalization, the cumulative effect of which can significantly increase the total dose of radiation received. The effective radiation dose can be reduced by lowering the current and voltage of the X-ray tube, but this reduces image quality. One possible solution is to use adaptive statistical iterative reconstruction technology on the raw CT data. Recently, data on the efficacy of low-dose CT (LDCT) in the diagnosis of COVID-19 have appeared in the literature. AIM: To analyze the quality and diagnostic value of LDCT images of the lungs after applying an iterative processing algorithm and to assess the possibility of reducing the radiation load on the patient when diagnosing COVID-19. MATERIALS AND METHODS: Patients from the Infectious Diseases Department of the Moscow State University Hospital participated in the prospective study. CT examinations were performed at the time of patient admission and discharge and were repeated as needed during hospitalization. In the first study, a standard CT protocol with a tube voltage of 120 kV and automatic current modulation in the range of 200400 mA was used; in repeated CT scans, the LDKT protocol was used with reduced tube voltage parameters (100 or 110 kV) and automatic current modulation in the range of 40120 mA. To assess the diagnostic value of LDCT in comparison with standard CT, a survey was conducted among doctors from the Department of Radiation Diagnostics at Moscow State University Hospital. The questionnaire included a comparison of the two methods for identifying the following pathological processes: ground-glass opacities, compaction of the lung tissue with reticular changes, areas of lung tissue consolidation, and lymphadenopathy. RESULTS: The study included 151 patients. The average age was 5814.2 years, with men accounting for 53.6% of the population. During LDCT the radiation load was reduced by 2.96 times on average, CTDI by 2.6 times, DLP by 3.1 times, the current on the tube by 1.83 times, and the voltage on the tube by 1.2 times. The results indicate that the effectiveness of detecting the main signs of viral pneumonia and assessing the dynamics of the patients condition does not differ significantly from CT performed according to the standard protocol. CONCLUSIONS: The results of a comparison of standard and low-dose CT show that there is no significant loss of diagnostic information and image quality as the radiation load is reduced. Thus, chest LDCT can be used to successfully diagnose COVID-19 in routine practice.

Low-dose whole-body CT using deep learning image reconstruction: image quality and lesion detection

2021 ◽  
Vol 94 (1121) ◽  
pp. 20201329
Author(s):  
Yoshifumi Noda ◽  
Tetsuro Kaga ◽  
Nobuyuki Kawai ◽  
Toshiharu Miyoshi ◽  
Hiroshi Kawada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Low Dose ◽  
Standard Dose ◽  
Lesion Detection ◽  
Whole Body ◽  
Hybrid Iterative Reconstruction ◽  
Whole Body Ct ◽  
Attenuation Values

Objectives: To evaluate image quality and lesion detection capabilities of low-dose (LD) portal venous phase whole-body computed tomography (CT) using deep learning image reconstruction (DLIR). Methods: The study cohort of 59 consecutive patients (mean age, 67.2 years) who underwent whole-body LD CT and a prior standard-dose (SD) CT reconstructed with hybrid iterative reconstruction (SD-IR) within one year for surveillance of malignancy were assessed. The LD CT images were reconstructed with hybrid iterative reconstruction of 40% (LD-IR) and DLIR (LD-DLIR). The radiologists independently evaluated image quality (5-point scale) and lesion detection. Attenuation values in Hounsfield units (HU) of the liver, pancreas, spleen, abdominal aorta, and portal vein; the background noise and signal-to-noise ratio (SNR) of the liver, pancreas, and spleen were calculated. Qualitative and quantitative parameters were compared between the SD-IR, LD-IR, and LD-DLIR images. The CT dose-index volumes (CTDIvol) and dose-length product (DLP) were compared between SD and LD scans. Results: The image quality and lesion detection rate of the LD-DLIR was comparable to the SD-IR. The image quality was significantly better in SD-IR than in LD-IR (p < 0.017). The attenuation values of all anatomical structures were comparable between the SD-IR and LD-DLIR (p = 0.28–0.96). However, background noise was significantly lower in the LD-DLIR (p < 0.001) and resulted in improved SNRs (p < 0.001) compared to the SD-IR and LD-IR images. The mean CTDIvol and DLP were significantly lower in the LD (2.9 mGy and 216.2 mGy•cm) than in the SD (13.5 mGy and 1011.6 mGy•cm) (p < 0.0001). Conclusion: LD CT images reconstructed with DLIR enable radiation dose reduction of >75% while maintaining image quality and lesion detection rate and superior SNR in comparison to SD-IR. Advances in knowledge: Deep learning image reconstruction algorithm enables around 80% reduction in radiation dose while maintaining the image quality and lesion detection compared to standard-dose whole-body CT.

Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

2020 ◽  
Vol 215 (6) ◽  
pp. 1321-1328
Author(s):  
Akinori Hata ◽  
Masahiro Yanagawa ◽  
Yuriko Yoshida ◽  
Tomo Miyata ◽  
Mitsuko Tsubamoto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Low Dose Ct

scholarly journals Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200677
Author(s):  
Andrea Steuwe ◽  
Marie Weber ◽  
Oliver Thomas Bethge ◽  
Christin Rademacher ◽  
Matthias Boschheidgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Dose Reduction ◽  
Low Dose ◽  
Ct Images ◽  
Image Noise ◽  
The Novel ◽  
Subjective Image Quality ◽  
Image Information ◽  
Noise Ratio

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

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