Virtual monoenergetic imaging in photon-counting CT of the head and neck.
Clin Imaging
; 102: 109-115, 2023 Oct.
Article
in En
| MEDLINE
| ID: mdl-37672849
PURPOSE: Advantages of virtual monoenergetic images (VMI) have been reported for dual energy CT of the head and neck, and more recently VMIs derived from photon-counting (PCCT) angiography of the head and neck. We report image quality metrics of VMI in a PCCT angiography dataset, expanding the anatomical regions evaluated and extending observer-based qualitative methods further than previously reported. METHODS: In a prospective study, asymptomatic subjects underwent contrast enhanced PCCT of the head and neck using an investigational scanner. Image sets of low, high, and full spectrum (Threshold-1) energies; linear mix of low and high energies (Mix); and 23 VMIs (40-150 keV, 5 keV increments) were generated. In 8 anatomical locations, SNR and radiologists' preferences for VMI energy levels were measured using a forced-choice rank method (4 observers) and ratings of image quality using visual grading characteristic (VGC) analysis (2 observers) comparing VMI to Mix and Threshold-1 images. RESULTS: Fifteen subjects were included (7 men, 8 women, mean 57 years, range 46-75). Among all VMIs, SNRs varied by anatomic location. The highest SNRs were observed in VMIs. Radiologists preferred 50-60 keV VMIs for vascular structures and 75-85 keV for all other structures. Cumulative ratings of image quality averaged across all locations were higher for VMIs with areas under the curve of VMI vs Mix and VMI vs Threshold-1 of 0.67 and 0.68 for the first reader and 0.72 and 0.76 for the second, respectively. CONCLUSION: Preferred keV level and quality ratings of VMI compared to mixed and Threshold-1 images varied by anatomical location.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Head
/
Neck
Type of study:
Observational_studies
/
Qualitative_research
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Clin Imaging
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2023
Document type:
Article
Affiliation country:
United States
Country of publication:
United States