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Abstract
Multiphasic multidetector computed tomographic angiography is a standard diagnostic test for canine abdominal vascular disorders. Three imaging protocols have been previously described. The test-bolus protocol allows precise timing but can be time consuming to perform. Bolus-tracking software is fast and easy to use but can be problematic for exact timing of vascular phases. A recently described fixed-injection-duration protocol is not influenced by body weight and provides a wider temporal window for arterial acquisitions. Objectives of this retrospective and prospective, multicentric, method comparison study were to determine which of the three multidetector computed tomographic angiography protocols allows best vascular conspicuity of the canine abdomen and to assess the influence of different multidetector computed tomography (CT) scanners on study quality. Triple-phase multidetector computed tomographic angiography canine abdominal studies from 30 dogs were retrospectively retrieved from three different institutions. Each institution performed one of the three computed tomographic angiography protocols (4-row and 16-row multidetector CT). Prospectively, the three protocols were also acquired with similar conditions on a 64-row MDCT in 21 dogs. Main abdominal vessels were scored by blinded readers for each phase. The fixed-injection-duration protocol had the best combined arterial and portal vascular conspicuity on scanners of limited speed, while the test-bolus protocol provided the best overall vascular conspicuity on 64-row multidetector CT scanner. The quality of arterial studies performed on 64-row MDCT scanner was improved compared to the ones performed on four- to 16-row multidetector CT scanners. Findings supported the fixed-injection-duration protocol as the best compromise between an ideal portal vascular enhancement and an easily reproducible protocol on scanners with low and high number of detector rows.
EPub: Veterinary Radiology & Ultrasound 2018 Nov;59(6):677-686
DOI: 10.1111/vru.12679
PMID: 30192038
RGID: 327505379
Full Text: article.pdf
Authors: Longo Maurizio, Thierry Florence, Chau Jennifer, Makara Mariano, Specchi Swan, Auriemma Edoardo, Handel Ian, Schwarz Tobias.
