Clinical acceptance of deep learning reconstruction for abdominal CT imaging: objective and subjective image quality and low-contrast detectability assessment

2022 ◽  
Author(s):  
Pierre-Antoine Bornet ◽  
Nicolas Villani ◽  
Romain Gillet ◽  
Edouard Germain ◽  
Charles Lombard ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Ct Imaging ◽  
Abdominal Ct ◽  
Subjective Image Quality ◽  
Low Contrast

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.

scholarly journals Evaluation of Radiation Dose and Image Quality using High-Pitch 70-kV Chest CT in Immunosuppressed Patients

2018 ◽  
Vol 191 (02) ◽  
pp. 122-129 ◽  
Author(s):  
Ibrahim Yel ◽  
Simon Martin ◽  
Julian Wichmann ◽  
Lukas Lenga ◽  
Moritz Albrecht ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Image Data ◽  
Ct Imaging ◽  
Chest Ct ◽  
Control Group ◽  
High Pitch ◽  
Dose Length Product ◽  
Subjective Image Quality ◽  
Immunosuppressed Patients

Purpose The aim of the study was to evaluate high-pitch 70-kV CT examinations of the thorax in immunosuppressed patients regarding radiation dose and image quality in comparison with 120-kV acquisition. Materials and Methods The image data from 40 patients (14 women and 26 men; mean age: 40.9 ± 15.4 years) who received high-pitch 70-kV CT chest examinations were retrospectively included in this study. A control group (n = 40), matched by age, gender, BMI, and clinical inclusion criteria, had undergone standard 120-kV chest CT imaging. All CT scans were performed on a third-generation dual-source CT unit. For an evaluation of the radiation dose, the CT dose index (CTDIvol), dose-length product (DLP), effective dose (ED), and size-specific dose estimates (SSDE) were analyzed in each group. The objective image quality was evaluated using signal-to-noise (SNR) and contrast-to-noise ratios (CNR). Three blinded and independent radiologists evaluated subjective image quality and diagnostic confidence using 5-point Likert scales. Results The mean dose parameters were significantly lower for high-pitch 70-kV CT examinations (CTDIvol, 2.9 ± 0.9 mGy; DLP, 99.9 ± 31.0 mGyxcm; ED, 1.5 ± 0.6 mSv; SSDE, 3.8 ± 1.2 mGy) compared to standard 120-kV CT imaging (CTDIvol, 8.8 ± 3.7mGy; DLP, 296.6 ± 119.3 mGyxcm; ED, 4.4 ± 2.1 mSv; SSDE, 11.6 ± 4.4 mGy) (P≤ 0.001). The objective image parameters (SNR: 7.8 ± 2.1 vs. 8.4 ± 1.8; CNR: 7.7 ± 2.4 vs. 8.3 ± 2.8) (P≥ 0.065) and the cumulative subjective image quality (4.5 ± 0.4 vs. 4.7 ± 0.3) (p = 0.052) showed no significant differences between the two protocols. Conclusion High-pitch 70-kV thoracic CT examinations in immunosuppressed patients resulted in a significantly reduced radiation exposure compared to standard 120-kV CT acquisition without a decrease in image quality. Key Points:  Citation Format

SU-F-207-02: Use of Postmortem Subjects for Subjective Image Quality Assessment in Abdominal CT Protocols with Iterative Reconstruction

2015 ◽  
Vol 42 (6Part26) ◽  
pp. 3542-3542
Author(s):  
A Mench ◽  
I Lipnharski ◽  
C Carranza ◽  
L Sinclair ◽  
R Lamoureux ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Iterative Reconstruction ◽  
Image Quality Assessment ◽  
Abdominal Ct ◽  
Subjective Image Quality

scholarly journals Correlation of Patient Weight and Cross-Sectional Dimensions with Subjective Image Quality at Standard Dose Abdominal CT

2003 ◽  
Vol 4 (4) ◽  
pp. 234 ◽  
Author(s):  
Mannudeep K. Kalra ◽  
Michael M. Maher ◽  
Srinivasa R. Prasad ◽  
M. Sikandar Hayat ◽  
Michael A. Blake ◽  
...  
Keyword(s):  
Image Quality ◽  
Standard Dose ◽  
Abdominal Ct ◽  
Cross Sectional ◽  
Subjective Image Quality ◽  
Patient Weight

scholarly journals Quantifying the Utility of Low Resolution Brain Imaging:: Treatment of Infant Hydrocephalus

2021 ◽  
Author(s):  
Joshua Harper ◽  
Venkateswararao Cherukuri ◽  
Tom O'Riley ◽  
Mingzhao Yu ◽  
Edith Mbabazi-Kabachelor ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Treatment Planning ◽  
Image Resolution ◽  
Learning Enhancement ◽  
Low Contrast ◽  
Substantial Risk ◽  
Noise Ratio ◽  
Hydrocephalus Treatment ◽  
Post Hoc

As low-field MRI technology is being disseminated into clinical settings, it is important to assess the image quality required to properly diagnose and treat a given disease. In this post-hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. Images were degraded in terms of resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in LMIC for assessment of clinical utility in treatment planning for hydrocephalus. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of a useful image for hydrocephalus treatment planning. For images with 128x128 resolution, a contrast-to-noise ratio of 2.5 has a high probability of being useful (91%, 95% CI 73% to 96%; P=2e-16). Deep learning enhancement of a 128x128 image with very low contrast-to-noise (1.5) and low probability of being useful (23%, 95% CI 14% to 36%; P=2e-16) increases CNR improving the apparent likelihood of being useful, but carries substantial risk of structural errors leading to misleading clinical interpretation (CNR after enhancement = 5; risk of misleading results = 21%, 95% CI 3% to 32%; P=7e-11). Lower quality images not customarily considered acceptable by clinicians can be useful in planning hydrocephalus treatment. We find substantial risk of misleading structural errors when using deep learning enhancement of low quality images. These findings advocate for new standards in assessing acceptable image quality for clinical use.

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT

2020 ◽  
Vol 214 (3) ◽  
pp. 566-573 ◽  
Author(s):  
Ramandeep Singh ◽  
Subba R. Digumarthy ◽  
Victorine V. Muse ◽  
Avinash R. Kambadakone ◽  
Michael A. Blake ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Lesion Detection ◽  
Abdominal Ct
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