Literature DB >> 28267350

Variation in CT Number and Image Noise Uniformity According to Patient Positioning in MDCT.

Timothy P Szczykutowicz1,2,3, Andrew DuPlissis3, Perry J Pickhardt1.   

Abstract

OBJECTIVE: Many algorithms for clinical decision making rely on assessment of the CT number (expressed as Hounsfield units); however, to our knowledge, few, if any, studies have addressed how CT numbers change as a function of patient positioning within the scanner.
MATERIALS AND METHODS: An anthropomorphic phantom underwent imaging with varying amounts of vertical orientation misalignment with respect to isocenter. CT number and noise were measured using ROIs in the upper thorax, mid thorax, and abdomen. The degree of noise nonuniformity and changes in the CT number were assessed by comparing values obtained in the anterior versus posterior ROIs. To add clinical relevance, data on vertical mispositioning were collected from 20,316 clinical abdominal CT scans. Box-and-whisker plot analysis was used to identify the range of patient positioning.
RESULTS: Absolute CT number changes of more than 20 HU were observed for some ROIs at phantom positions of 10 cm from isocenter, with important differences noted between the thoracic and abdominal regions. Noise uniformity varied by more than twofold for all regions at 10 cm below isocenter. On clinical CT examinations, off-centering of more than 1, 2, 4, and 6 cm occurred for 41%, 19%, 1.9%, and 0.3% of patients, respectively.
CONCLUSION: Radiologists should treat CT number measurements with caution when patients are grossly mispositioned in the scanner. The substantial changes in attenuation values shown in the present study are large enough to warrant further investigation.

Entities:  

Keywords:  CT; image quality; quantitative CT

Mesh:

Year:  2017        PMID: 28267350     DOI: 10.2214/AJR.16.17215

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  11 in total

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10.  Influence of breathing state on the accuracy of automated patient positioning in thoracic CT using a 3D camera for body contour detection.

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