BACKGROUND: Computed tomography (CT) is extremely important in characterizing blood vessel anatomy and vascular lesions in children. Recent advances in CT reconstruction technology hold promise for improved image quality and also reductions in radiation dose. This report evaluates potential improvements in image quality for the depiction of small pediatric vessels with model-based iterative reconstruction (Veo™), a technique developed to improve image quality and reduce noise. OBJECTIVE: To evaluate Veo™ as an improved method when compared to adaptive statistical iterative reconstruction (ASIR™) for the depiction of small vessels on pediatric CT. MATERIALS AND METHODS: Seventeen patients (mean age: 3.4 years, range: 2 days to 10.0 years; 6 girls, 11 boys) underwent contrast-enhanced CT examinations of the chest and abdomen in this HIPAA compliant and institutional review board approved study. Raw data were reconstructed into separate image datasets using Veo™ and ASIR™ algorithms (GE Medical Systems, Milwaukee, WI). Four blinded radiologists subjectively evaluated image quality. The pulmonary, hepatic, splenic and renal arteries were evaluated for the length and number of branches depicted. Datasets were compared with parametric and non-parametric statistical tests. RESULTS: Readers stated a preference for Veo™ over ASIR™ images when subjectively evaluating image quality criteria for vessel definition, image noise and resolution of small anatomical structures. The mean image noise in the aorta and fat was significantly less for Veo™ vs. ASIR™ reconstructed images. Quantitative measurements of mean vessel lengths and number of branches vessels delineated were significantly different for Veo™ and ASIR™ images. Veo™ consistently showed more of the vessel anatomy: longer vessel length and more branching vessels. CONCLUSION: When compared to the more established adaptive statistical iterative reconstruction algorithm, model-based iterative reconstruction appears to produce superior images for depiction of small pediatric vessels on computed tomography.
BACKGROUND: Computed tomography (CT) is extremely important in characterizing blood vessel anatomy and vascular lesions in children. Recent advances in CT reconstruction technology hold promise for improved image quality and also reductions in radiation dose. This report evaluates potential improvements in image quality for the depiction of small pediatric vessels with model-based iterative reconstruction (Veo™), a technique developed to improve image quality and reduce noise. OBJECTIVE: To evaluate Veo™ as an improved method when compared to adaptive statistical iterative reconstruction (ASIR™) for the depiction of small vessels on pediatric CT. MATERIALS AND METHODS: Seventeen patients (mean age: 3.4 years, range: 2 days to 10.0 years; 6 girls, 11 boys) underwent contrast-enhanced CT examinations of the chest and abdomen in this HIPAA compliant and institutional review board approved study. Raw data were reconstructed into separate image datasets using Veo™ and ASIR™ algorithms (GE Medical Systems, Milwaukee, WI). Four blinded radiologists subjectively evaluated image quality. The pulmonary, hepatic, splenic and renal arteries were evaluated for the length and number of branches depicted. Datasets were compared with parametric and non-parametric statistical tests. RESULTS: Readers stated a preference for Veo™ over ASIR™ images when subjectively evaluating image quality criteria for vessel definition, image noise and resolution of small anatomical structures. The mean image noise in the aorta and fat was significantly less for Veo™ vs. ASIR™ reconstructed images. Quantitative measurements of mean vessel lengths and number of branches vessels delineated were significantly different for Veo™ and ASIR™ images. Veo™ consistently showed more of the vessel anatomy: longer vessel length and more branching vessels. CONCLUSION: When compared to the more established adaptive statistical iterative reconstruction algorithm, model-based iterative reconstruction appears to produce superior images for depiction of small pediatric vessels on computed tomography.
Authors: Stephan Baumueller; Anna Winklehner; Christoph Karlo; Robert Goetti; Thomas Flohr; Erich W Russi; Thomas Frauenfelder; Hatem Alkadhi Journal: Eur Radiol Date: 2012-06-15 Impact factor: 5.315
Authors: Tobias A Fuchs; Michael Fiechter; Cathérine Gebhard; Julia Stehli; Jelena R Ghadri; Egle Kazakauskaite; Bernhard A Herzog; Lars Husmann; Oliver Gaemperli; Philipp A Kaufmann Journal: Int J Cardiovasc Imaging Date: 2012-10-02 Impact factor: 2.357
Authors: Peter W A Willems; Patamintita Taeshineetanakul; Barry Schenk; Patrick A Brouwer; Karel G Terbrugge; Timo Krings Journal: Neuroradiology Date: 2011-04-05 Impact factor: 2.804
Authors: Javier E Villanueva-Meyer; David M Naeger; Jesse L Courtier; Michael D Hope; Jack W Lambert; John D MacKenzie; Andrew S Phelps Journal: Emerg Radiol Date: 2017-03-14