Purpose:
To evaluate the influence on the performance of an automated vessel detection algorithm, of CT dose and reconstruction in anthropomorphic phantom images.
Methods: A 3D-printed vessel phantom (material Visijet-EX200; 0.1-4.25mm radius range) inside a PMMA thorax-shaped holder was scanned [CT-thorax protocol; (CTDIvol=4.0-2.1-1.0-0.5-0.2mGy); Canon_Aquilion_Prism, 4 repetitions] and images reconstructed with filtered-back-projection (FBP-FC08), iterative (AIDR3De-FC08) and deep-learning (DL) (AiCE-lung-standard) methods.
Images were analyzed with an automated in-house graph-cuts-based method for pulmonary vessel extraction and quantification, measuring, for each radius, the median pixel value (MPV, Hounsfield units-HU) and inter-quartile-range of pixel values (IQR, noise measurement) together with the total volume of voxels identified as vessels (averaged over 4 acquisitions).
Results:
As an example, for 3mm-radius, (MVP±σ) were, for (CTDIvol=0.2-0.5-1.0-2.1-4.0 mGy): [FBP: (98±5HU)-(97±9HU)-(93±7HU)-(94±7HU)-(91±2HU)]; [iterative: (101±3HU)-(104±4HU)-(104±7HU)-(102±4HU)-(99±3HU)]; [DL-based: (106±5HU)-(102±10HU)-(106±8HU)-(107±11HU)-(103±6HU)].
The IQR decreased with increasing dose for all reconstructions: (IQR±σ; 3mm-radius) were, for (CTDIvol=0.2-0.5-1.0-2.1-4.0 mGy): [FBP: (120±6HU)-(72±5HU)-(44±3HU)-(34±4HU)-(37±10HU)]; [iterative: (62±8HU)-(42±2HU)-(33±9HU)-(27±4HU)-(25±4HU)]; [DL-based: (62±12HU)-(59±8HU)-(43±7HU)-(39±5HU)-(36±6HU)].
The average detected vessel tree volume (ml) varied with dose and reconstruction: [FBP: (7.14±0.02ml)-(5.66±0.03ml)-(5.39±0.01ml)-(5.33±0.05ml)-(5.24±0.07ml)]; [iterative: (4.22±0.06ml)-(4.91±0.10ml)-(5.19±0.04ml)-(5.41±0.08ml)-(5.47±0.05ml)]; [DL-based: (6.36±0.07ml)-(7.15±0.08ml)-(7.22±0.06ml)-(7.39±0.03ml)-(7.42±0.03ml)].
Conclusion:
Vessel detection output depended on reconstruction method (with more vessels detected with DL reconstruction) and dose level. 3D-printed anthropomorphic phantoms with known structures are useful to test objectively the performance of automated tools for clinical diagnosis.

**Purpose:**
To evaluate the influence on the performance of an automated vessel detection algorithm, of CT dose and reconstruction in anthropomorphic phantom images.
**Methods:** A 3D-printed vessel phantom (material Visijet-EX200; 0.1-4.25mm radius range) inside a PMMA thorax-shaped holder was scanned [CT-thorax protocol; (CTDIvol=4.0-2.1-1.0-0.5-0.2mGy); Canon_Aquilion_Prism, 4 repetitions] and images reconstructed with filtered-back-projection (FBP-FC08), iterative (AIDR3De-FC08) and deep-learning (DL) (AiCE-lung-standard) methods.
Images were analyzed with an automated in-house graph-cuts-based method for pulmonary vessel extraction and quantification, measuring, for each radius, the median pixel value (MPV, Hounsfield units-HU) and inter-quartile-range of pixel values (IQR, noise measurement) together with the total volume of voxels identified as vessels (averaged over 4 acquisitions).
**Results:**
As an example, for 3mm-radius, (MVP±σ) were, for (CTDIvol=0.2-0.5-1.0-2.1-4.0 mGy): [FBP: (98±5HU)-(97±9HU)-(93±7HU)-(94±7HU)-(91±2HU)]; [iterative: (101±3HU)-(104±4HU)-(104±7HU)-(102±4HU)-(99±3HU)]; [DL-based: (106±5HU)-(102±10HU)-(106±8HU)-(107±11HU)-(103±6HU)].
The IQR decreased with increasing dose for all reconstructions: (IQR±σ; 3mm-radius) were, for (CTDIvol=0.2-0.5-1.0-2.1-4.0 mGy): [FBP: (120±6HU)-(72±5HU)-(44±3HU)-(34±4HU)-(37±10HU)]; [iterative: (62±8HU)-(42±2HU)-(33±9HU)-(27±4HU)-(25±4HU)]; [DL-based: (62±12HU)-(59±8HU)-(43±7HU)-(39±5HU)-(36±6HU)].
The average detected vessel tree volume (ml) varied with dose and reconstruction: [FBP: (7.14±0.02ml)-(5.66±0.03ml)-(5.39±0.01ml)-(5.33±0.05ml)-(5.24±0.07ml)]; [iterative: (4.22±0.06ml)-(4.91±0.10ml)-(5.19±0.04ml)-(5.41±0.08ml)-(5.47±0.05ml)]; [DL-based: (6.36±0.07ml)-(7.15±0.08ml)-(7.22±0.06ml)-(7.39±0.03ml)-(7.42±0.03ml)].
**Conclusion:**
Vessel detection output depended on reconstruction method (with more vessels detected with DL reconstruction) and dose level. 3D-printed anthropomorphic phantoms with known structures are useful to test objectively the performance of automated tools for clinical diagnosis.

Impact of deep learning reconstruction and CT dose on automatic lung vessel morphology characterization software: a 3D printed anthropomorphic phantom study

[Networking hour](https://app.wonder.me/?spaceId=fe093845-759f-448f-9f55-01d3adccd820)
[COIs](https://drive.google.com/drive/folders/1nbfwAa9lf9Kz-wAZ7iLKhfpBFjpN_gkn?usp=sharing)