Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.
Med Phys
; 45(1): 258-276, 2018 Jan.
Article
in En
| MEDLINE
| ID: mdl-29091269
ABSTRACT
PURPOSE:
Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully automated analysis method for two common PET/CT quality assurance phantoms the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts.METHODS:
We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.71 activity ratio over background, and CTN phantoms were filled with 41 and 21 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts.RESULTS:
The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.71, 41, and 21, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 s and was found to be significantly lower (P ⪠0.001) compared to manual analysis (mean 247.8 s).CONCLUSIONS:
The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intraoperator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Pattern Recognition, Automated
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Phantoms, Imaging
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Radiopharmaceuticals
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Fluorodeoxyglucose F18
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Positron Emission Tomography Computed Tomography
Type of study:
Guideline
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Med Phys
Year:
2018
Document type:
Article
Affiliation country:
United States
Publication country:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
/
EUA
/
UNITED STATES
/
UNITED STATES OF AMERICA
/
US
/
USA