Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children
Jihang Sun1, Haoyan Li1, Haiyun Li2
1Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Insights
Deep learning image reconstruction (DLIR) significantly enhances CT angiography (CTA) image quality for pediatric Takayasu arteritis (TAK) patients. DLIR-H offers improved diagnostic confidence and better noise reduction compared to traditional methods.
Area of Science:
- Radiology
- Medical Imaging
- Pediatric Cardiology
Background:
- Takayasu arteritis (TAK) in children often shows normal inflammatory markers post-treatment.
- CT angiography (CTA) is crucial for evaluating TAK status, sometimes surpassing laboratory tests in sensitivity.
Purpose of the Study:
- To assess the image quality improvements of CTA in pediatric TAK patients using deep learning image reconstruction (DLIR).
- To compare DLIR with conventional reconstruction algorithms like Filtered Back-Projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V).
Main Methods:
- Thirty-two pediatric TAK patients underwent neck, chest, and abdominal CTA.
- Images were reconstructed using FBP, 50%ASIR-V, 100%ASIR-V, and DLIR-H.
- Quantitative analysis included CT number, standard deviation, and contrast-to-noise ratio (CNR).
- Qualitative assessment involved vessel visualization, image noise, and diagnostic confidence on a 5-point scale.
Main Results:
- DLIR-H and 100%ASIR-V demonstrated significantly lower noise and higher CNR compared to FBP and 50%ASIR-V.
- Both DLIR-H and 100%ASIR-V yielded comparable noise and CNR values.
- DLIR-H and 50%ASIR-V were superior for visualizing small arteries, with DLIR-H achieving higher diagnostic confidence.
Conclusions:
- Deep learning image reconstruction (DLIR-H) enhances CTA image quality and diagnostic confidence in pediatric TAK patients.
- DLIR-H provides an optimal balance between image noise reduction and spatial resolution.
- DLIR-H represents a valuable advancement for CTA in the assessment of pediatric Takayasu arteritis.
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.
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