scholarly journals Validation of a deep learning reconstruction framework for 3D delayed myocardial enhancement imaging

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
G Delso ◽  
K Suryanarayanan ◽  
JT Ortiz-Perez ◽  
S Prat ◽  
A Doltra ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Blood Pool ◽  
Image Noise ◽  
Delayed Enhancement ◽  
New Method ◽  
Acquisition Time ◽  
Trade Off ◽  
Feature Preservation ◽  
Myocardial Delayed Enhancement

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Myocardial delayed enhancement (MDE) MRI plays an important role in the identification of several cardiac conditions, both ischemic and non-ischemic (e.g. myocarditis, IDC, amyloidosis). 3D imaging offers increased resolution, full heart coverage and better depiction of complex pathologies, but its image quality is limited by long acquisition times. Deep learning (DL) models enable advanced reconstruction algorithms that yield regularized images in practical computation times. In this study we evaluate a novel 3D-DL reconstruction to overcome the trade-off between reconstructed quality and acquisition time on MDE data. Methods A group of 14 subjects referred for CMR (5 F / 9 M, 59 ± 11 y.o., 78 ± 13 kg) were scanned with a 3D MDE sequence prototype: SPGR with IR preparation, fat & spatial saturation, respiratory navigator, ARC 2x, FOV 40x40cm, ST 1.4-2.4mm, matrix 280²-320², FA 20deg, BW 62.5 kHz, TE 2.1 ± 0.1ms, TI based on a CINE IR scout. All were retrospectively reconstructed using a 3D DL algorithm, trained on a database of over 700 datasets to reconstruct high-quality images with adjustable noise reduction. The images were compared with standard 3D Cartesian reconstruction by two experienced cardiologists, to identify alterations in morphology or contrast distribution. Noise was estimated using the intensity standard deviation on a blood pool ROI. Feature preservation was estimated using the structural similarity index (SSI). Results The new method improved perceived image quality without loss of structural information or resolution (fig 1). Quantitative analysis (fig 2) confirmed these results: The average coefficient of variation in the blood was 0.08 ± 0.02 in the reference and 0.05 ± 0.02 with the new method; Given a target image noise level, DL reconstruction yielded up to 10% better SSI, compared to anisotropic filtering. The clinical review didn’t reveal diagnostically significant alterations of structure or uptake pattern. A perceived reduction of sharpness was initially reported but individual examination of landmarks (e.g. pulmonary and coronary arteries) confirmed that no relevant features were being lost with the new reconstruction. Discussion The 3D MDE images obtained with DL reconstruction improved the trade-off between image noise -estimated by the blood pool intensity deviation- and feature preservation -estimated by SSI-. Consistent improvement of image quality without morphological alterations of diagnostic relevance indicates that the new method can be considered for clinical practice. The next step in the validation process will require testing the robustness over a large set of cases with heterogeneous acquisition settings. Conclusion We presented the preliminary evaluation of a deep learning reconstruction method with 3D myocardial delayed enhancement data. The results show systematic improvement of overall image quality without loss of relevant diagnostic information. Abstract Figure.

Deep learning–accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality

2021 ◽  
Vol 137 ◽  
pp. 109600
Author(s):  
Sebastian Gassenmaier ◽  
Saif Afat ◽  
Dominik Nickel ◽  
Mahmoud Mostapha ◽  
Judith Herrmann ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Acquisition Time

Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

2021 ◽  
pp. 1-8
Author(s):  
Jihang Sun ◽  
Haoyan Li ◽  
Haiyun Li ◽  
Michelle Li ◽  
Yingzi Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Ct Angiography ◽  
Takayasu Arteritis ◽  
Image Noise ◽  
Diagnostic Confidence ◽  
Ct Number ◽  
Laboratory Test Results ◽  
Significant Difference

BACKGROUND: The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE: To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS: hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS: There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P >  0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P <  0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION: DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

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.

Clinical Implementation of Deep Learning MR reconstruction for TSE Sequences: Reduction of Acquisition Time and Maintenance

2021 ◽  
Author(s):  
Judith Herrmann ◽  
Sebastian Gassenmaier ◽  
Thomas Kuestner ◽  
Matthias Kuendel ◽  
Dominik Nickel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Acquisition Time ◽  
Clinical Implementation ◽  
Clinical Workflow ◽  
Diagnostic Confidence ◽  
Clinical Routine ◽  
Time Saving ◽  
Reconstruction Process

Abstract Background: The application of Deep Learning (DL) in MR image reconstruction is increasingly gaining attention due to its potential of increasing image quality and reducing acquisition time. However, the technology hasn’t been yet implemented in clinical routine. The aim of this study was therefore to describe the implementation of this novel DL image reconstruction for turbo spin echo (TSE) sequences in clinical workflow including a thorough explanation of the required steps and an evaluation of the obtainable image quality compared to conventional TSE.Methods: DL image reconstruction using a variational network was clinically implemented to enable acquisition of accelerated TSE sequences. After internal review board’s approval and informed consent, 30 examinations for knee, shoulder, and lumbar spine in 15 volunteers at 3 T were included in this prospective study. Conventional TSE sequences (TSE) and TSE with deep learning reconstruction (TSEDL) were compared regarding overall image quality, noise, sharpness, and subjective signal-to-noise-ratio (SNR), as well diagnostic confidence and image impression. Comparative analyses were conducted to assess the differences between the sequences. A survey on technologists’ acceptance was performed for DL image reconstruction. Results: DL image reconstruction was successfully implemented in a clinical workflow and TSEDL allowed a remarkable time saving of more than 50%. Overall image quality, diagnostic confidence and image impression for TSEDL were rated as excellent (median 4, IQR 4-4) and comparable to TSE (image quality: p=0.059, diagnostic confidence: p=0.157, image impression: p=0.102). Noise, sharpness, artifacts, and subjective SNR for TSEDL reached significantly superior levels to TSE (noise: p<0.001, sharpness: p=0.001, artifacts: p=0.014, subjective SNR: p<0.001). Technologists reported high levels of acceptance for DL image reconstruction. Required time for the reconstruction process was rated moderate and longer than standard sequences (median 2, IQR 2-3). Required time and effort for the implementation in daily workflow was rated as low effort (median 4, IQR 3-4). General applicability of DL reconstruction as well as acceptance of DL sequences in clinical routine were rated excellent (median 4, IQR 3-4). Conclusion: DL image reconstruction for TSE sequences can be implemented in clinical workflow and enables a remarkable time saving (>50%) in image acquisition while maintaining excellent image quality.Trial registration: Your clinical trial is officially registered at the German DRKS with the registration number: DRKS00023278.

scholarly journals Image quality assessment of deep learning image reconstruction in computed tomography using tube current modulation: a phantom study

2021 ◽  
Author(s):  
kazuhiro takeuchi ◽  
Yasuhiro Ide ◽  
Yuichiro Mori ◽  
Yusuke Uehara ◽  
Hiroshi Sukeishi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Quality Characteristics ◽  
Image Noise ◽  
Tube Current Modulation ◽  
Current Modulation ◽  
Tube Current ◽  
The Impact

Abstract The novel deep learning image reconstruction (DLIR) is known to change its image quality characteristics according to object contrast and image noise. In clinical practice, computed tomography (CT) image noise is usually controlled by tube current modulation (TCM) to accommodate changes in object size. This study aimed to evaluate the image quality characteristics of DLIR for different object sizes when in-plane noise is controlled by TCM. We used Mercury 4.0 phantoms with different object sizes. Phantom image acquisition was performed on a GE Revolution CT system to investigate the impact of the DLIR algorithm compared to standard reconstructions: filtered back projection (FBP) and hybrid iterative reconstruction (hybrid-IR). For image quality evaluation, the noise power spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were determined. The NPS of DLIR was very similar to that of FBP, and the information in the high-frequency region was maintained. In terms of TTF, DLIR showed higher resolution than hybrid-IR at low- to medium-contrast (Δ50, Δ90HU), but not necessarily higher than FBP. At the simulated contrast and lesion size, DLIR showed higher detectability than hybrid-IR, regardless of the phantom size. In this study, we evaluated a novel DLIR algorithm by reproducing clinical behaviors. The findings indicate that DLIR produces higher image quality than hybrid-IR regardless of the phantom size, although it depends on the reconstruction strength.

scholarly journals Zoom Rendering: Improving 3-D Rendering Performance with 2-D Operations

1996 ◽  
Vol 2 (3) ◽  
pp. 15-35
Author(s):  
Tomasz Mazuryk ◽  
Dieter Schmalstieg ◽  
Michael Gervautz
Keyword(s):  
Image Quality ◽  
High Performance ◽  
Interactive Visualization ◽  
New Method ◽  
Trade Off ◽  
Frame Buffer ◽  
Complex Scenes ◽  
Uniform Frame ◽  
The Cost

We propose a new method to accelerate rendering during the interactive visualization of complex scenes. The method is applicable if the cost of per-pixel processing is high compared to simple frame buffer transfer operations, as supported by low-end graphics systems with high-performance CPUs or 2-D graphics accelerators. In this case rendering an appropriately down-scaled image and then enlarging it allows a trade-off of rendering speed and image quality. Using this method, more uniform frame rates can be achieved and the dynamic viewing error can be reduced.

scholarly journals Deep Learning Versus Iterative Reconstruction for CT Pulmonary Angiography in the Emergency Setting: Improved Image Quality and Reduced Radiation Dose

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 558
Author(s):  
Marc Lenfant ◽  
Olivier Chevallier ◽  
Pierre-Olivier Comby ◽  
Grégory Secco ◽  
Karim Haioun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Noise Signal ◽  
Pulmonary Angiography ◽  
Image Noise ◽  
Emergency Setting ◽  
Dose Length Product ◽  
Noise Ratio

To compare image quality and the radiation dose of computed tomography pulmonary angiography (CTPA) subjected to the first deep learning-based image reconstruction (DLR) (50%) algorithm, with images subjected to the hybrid-iterative reconstruction (IR) technique (50%). One hundred forty patients who underwent CTPA for suspected pulmonary embolism (PE) between 2018 and 2019 were retrospectively reviewed. Image quality was assessed quantitatively (image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) and qualitatively (on a 5-point scale). Radiation dose parameters (CT dose index, CTDIvol; and dose-length product, DLP) were also recorded. Ninety-three patients were finally analyzed, 48 with hybrid-IR and 45 with DLR images. The image noise was significantly lower and the SNR (24.4 ± 5.9 vs. 20.7 ± 6.1) and CNR (21.8 ± 5.8 vs. 18.6 ± 6.0) were significantly higher on DLR than hybrid-IR images (p < 0.01). DLR images received a significantly higher score than hybrid-IR images for image quality, with both soft (4.4 ± 0.7 vs. 3.8 ± 0.8) and lung (4.1 ± 0.7 vs. 3.6 ± 0.9) filters (p < 0.01). No difference in diagnostic confidence level for PE between both techniques was found. CTDIvol (4.8 ± 1.4 vs. 4.0 ± 1.2 mGy) and DLP (157.9 ± 44.9 vs. 130.8 ± 41.2 mGy∙cm) were lower on DLR than hybrid-IR images. DLR both significantly improved the image quality and reduced the radiation dose of CTPA examinations as compared to the hybrid-IR technique.

scholarly journals Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection

2021 ◽  
Vol 94 (1123) ◽  
pp. 20201357
Author(s):  
Akio Tamura ◽  
Eisuke Mukaida ◽  
Yoshitaka Ota ◽  
Masayoshi Kamata ◽  
Shun Abe ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Abdominal Aorta ◽  
Low Dose ◽  
Image Noise ◽  
Obese Patients ◽  
Back Projection ◽  
Hepatic Parenchyma ◽  
Abdominal Ct

Objective: This study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and filtered back projection (FBP). Methods: Datasets from consecutive patients who underwent low-dose liver CT were retrospectively identified. Images were reconstructed using DLR, MBIR, and FBP. Mean image noise and contrast-to-noise ratio (CNR) were calculated, and noise, artifacts, sharpness, and overall image quality were subjectively assessed. Dunnett’s test was used for statistical comparisons. Results: Ninety patients (67 ± 12.7 years; 63 males; mean body mass index [BMI], 25.5 kg/m2) were included. The mean noise in the abdominal aorta and hepatic parenchyma of DLR was lower than that in FBP and MBIR (p < .001). For FBP and MBIR, image noise was significantly higher for obese patients than for those with normal BMI. The CNR for the abdominal aorta and hepatic parenchyma was higher for DLR than for FBP and MBIR (p < .001). MBIR images were subjectively rated as superior to FBP images in terms of noise, artifacts, sharpness, and overall quality (p < .001). DLR images were rated as superior to MBIR images in terms of noise (p < .001) and overall quality (p = .03). Conclusions: Based on objective and subjective comparisons, the image quality of DLR was found to be superior to that of MBIR and FBP on low-dose abdominal CT. DLR was the only method for which image noise was not higher for obese patients than for those with a normal BMI. Advances in knowledge: This study provides previously unavailable information on the properties of DLR systems and their clinical utility.

scholarly journals Feasibility of Low-Dose 68Ga-DOTATATE With Short Acquisition Time Using Total-Body PET/CT in Patients With Neuroendocrine Tumor

2021 ◽  
Author(s):  
Jie Xiao ◽  
Haojun Yu ◽  
Hongyan Yin ◽  
Guobin Liu ◽  
Yan Hu ◽  
...  
Keyword(s):  
Image Quality ◽  
Dose Regimen ◽  
Low Dose ◽  
Signal To Noise Ratio ◽  
Subjective Evaluation ◽  
Blood Pool ◽  
Acquisition Time ◽  
Short Acquisition Time ◽  
Pet Ct ◽  
Total Body

Abstract Purpose To explore the feasibility of a low dose regimen with short acquisition time of 68Ga-DOTATATE total-body PET/CT without compromising image quality of patients with NETs. Methods Fifty-seven consecutive NETs patients who underwent 68Ga-DOTATATE total-body PET/CT, with a low dose regimen (0.8-1.2 MBq/kg) of 68Ga-DOTATATE and acquisition time of 10 min prior to any treatment, were enrolled in the present study. The PET data were split into 1 min, 2 min, 3 min, 4 min, 5 min, 8 min and 10 min reconstruction groups, referenced as R1, R2, R3, R4, R5, R8 and R10. The subjective evaluation of image quality was scored in 5-point Likert scale based on three aspects: the overall impression of the image quality, the image noise, the lesion detectability. The objective image quality was assessed by the signal-to-noise ratio of liver (SNRL), the coefficient of variation (CV), the SUVmax, SUVmean, SD of liver, mediastinal blood pool and lesion, the tumor-liver ratio (TLR), the tumor-mediastinal blood pool-ratio (TMR) of lesion. Results The sufficient subjective image quality with a score of 3.44±0.53 could be obtained at 3 min acquisition duration, with a kappa value of 0.90. In quantitative analysis, the value of SNRL is over 10 in all reconstruction groups. As the acquisition time increases, SNRL was increased and CV was decreased within 3 min, while SNRL and CV showed no significant different between R4-R10. There was no significant different in TMR and TLR of lesion between R1-R10 (all p < 0.05). Referenced as PET images of R10, 90 SSTR-positive lesions are identified, and all those lesions are found in the R1-R10 groups (100%).Conclusion The low-dose (0.8-1.2 MBq/kg) 68Ga-DOTATATE total-body PET/CT not only shortens acquisition time, but maintains a sufficient image quality for the NETs patients.

A feasibility study of realizing low-dose abdominal CT using deep learning image reconstruction algorithm

2021 ◽  
pp. 1-12
Author(s):  
Lu-Lu Li ◽  
Huang Wang ◽  
Jian Song ◽  
Jin Shang ◽  
Xiao-Ying Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Image Quality ◽  
Radiation Dose ◽  
Low Dose ◽  
Standard Dose ◽  
Image Noise ◽  
Arterial Phase ◽  
Abdominal Ct ◽  
Reconstruction Methods

OBJECTIVES: To explore the feasibility of achieving diagnostic images in low-dose abdominal CT using a Deep Learning Image Reconstruction (DLIR) algorithm. METHODS: Prospectively enrolled 47 patients requiring contrast-enhanced abdominal CT scans. The late-arterial phase scan was added and acquired using lower-dose mode (tube current range, 175–545 mA; 80 kVp for patients with BMI ≤24 kg/m2 and 100 kVp for patients with BMI >  24 kg/m2) and reconstructed with DLIR at medium setting (DLIR-M) and high setting (DLIR-H), ASIR-V at 0% (FBP), 40% and 80% strength. Both the quantitative measurement and qualitative analysis of the five types of reconstruction methods were compared. In addition, radiation dose and image quality between the early-arterial phase ASIR-V images using standard-dose and the late-arterial phase DLIR images using low-dose were compared. RESULTS: For the late-arterial phase, all five reconstructions had similar CT value (P >  0.05). DLIR-H, DLIR-M and ASIR-V80% images significantly reduced the image noise and improved the image contrast noise ratio, compared with the standard ASIR-V40% images (P <  0.05). ASIR-V80% images had undesirable image characteristics with obvious “waxy” artifacts, while DLIR-H images maintained high spatial resolution and had the highest subjective image quality. Compared with the early-arterial scans, the late-arterial phase scans significantly reduced the radiation dose (P <  0.05), while the DLIR-H images exhibited lower image noise and good display of the specific image details of lesions. CONCLUSIONS: DLIR algorithm improves image quality under low-dose scan condition and may be used to reduce the radiation dose without adversely affecting the image quality.

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