Ultra-low-dose chest CT with iterative reconstruction does not alter anatomical image quality.
Diagn Interv Imaging
; 97(11): 1131-1140, 2016 Nov.
Article
in En
| MEDLINE
| ID: mdl-27451261
ABSTRACT
PURPOSE:
To evaluate the effect of dose reduction with iterative reconstruction (IR) on image quality of chest CT scan. MATERIALS ANDMETHODS:
Eighteen human cadavers had chest CT with one reference CT protocol (RP-CT; 120kVp/200mAs) and two protocols with dose reduction low-dose-CT (LD-CT; 120kVp/40mAs) and ultra-low-dose CT (ULD-CT; 120kVp/10mAs). Data were reconstructed with filter-back-projection (FBP) for RP-CT and with FBP and IR (sinogram affirmed iterative reconstruction [SAFIRE®]) algorithm for LD-CT and ULD-CT. Volume CT dose index (CTDIvol) were recorded. The signal-to-noise (SNR), contrast-to-noise (CNR) ratios of LD-CT and ULD-CT and quantitative parameters were compared to RP-CT. Two radiologists reviewed the CT examinations assessed independently the quality of anatomical structures and expressed a confidence level using a 2-point scale (50% and 95%).RESULTS:
CTDIvol was 2.69 mGy for LD-CT (-80%; P<0.01) and 0.67 mGy for ULD-CT (-95%; P<0.01) as compared to 13.42 mGy for RP-CT. SNR and CNR were significantly decreased (P<0.01) for LD-CT and ULD-CT, but IR improved these values satisfactorily. No significant differences were observed for quantitative measurements. Radiologists rated excellent/good the RP-CT and LD-CT images, whereas good/fair the ULD-CT images. Confidence level for subjective anatomical analysis was 95% for all protocols.CONCLUSIONS:
Dose reduction with a dose lower than 1 mGy, used in conjunction with IR allows performing chest CT examinations that provide a high quality of anatomical structures.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Radiation Dosage
/
Image Processing, Computer-Assisted
/
Radiography, Thoracic
/
Radiographic Image Interpretation, Computer-Assisted
/
Cone-Beam Computed Tomography
Limits:
Adult
/
Humans
Language:
En
Journal:
Diagn Interv Imaging
Year:
2016
Document type:
Article