RATIONALE AND OBJECTIVES: The authors evaluated the clinical utility of image coregistration in the interpretation of follow-up computed tomographic (CT) studies of the head. MATERIALS AND METHODS: Fourteen patients with 34 intracranial lesions underwent follow-up head CT. The images were coregistered automatically with proprietary software on a standard personal computer, and all patient demographic data were removed. A neuroradiologist read the coregistered images several days after first reading the nonregistered images. The reading was repeated some months later to assess intraobserver variability, and a second reader was recruited so that interobserver variability also could be assessed. The interpretations of nonregistered images served as controls for the interpretations of coregistered images. Readers were asked to assess changes in lesion size quantitatively and to record the time it took to evaluate each case. Differences in interpretation speed were evaluated with the t test. Univariate analysis was used to measure accuracy; interpretations were compared with those of a nonblinded senior neuroradiologist, which served as the diagnostic standard. Intra- and interindividual variability were assessed with the kappa statistic. RESULTS: The time needed to read the studies decreased by an average of 65.6% (P < .05), with statistically significant reductions for each reader. Coregistration also changed the interpretation results in 21.9% of cases. Coregistration increased the accuracy of reading, but not significantly. Intraobserver variability improved from 0.3554 to 0.7328 with coregistration, and interobserver variability improved from 0.2670 to 0.3309. CONCLUSION: Image coregistration is technically feasible and accurate. Coregistration of follow-up studies significantly reduces the time needed for comparison and interpretation. It does not detract from the accuracy of interpretation of follow-up studies and tends to decrease intra- and interobserver variability.
RATIONALE AND OBJECTIVES: The authors evaluated the clinical utility of image coregistration in the interpretation of follow-up computed tomographic (CT) studies of the head. MATERIALS AND METHODS: Fourteen patients with 34 intracranial lesions underwent follow-up head CT. The images were coregistered automatically with proprietary software on a standard personal computer, and all patient demographic data were removed. A neuroradiologist read the coregistered images several days after first reading the nonregistered images. The reading was repeated some months later to assess intraobserver variability, and a second reader was recruited so that interobserver variability also could be assessed. The interpretations of nonregistered images served as controls for the interpretations of coregistered images. Readers were asked to assess changes in lesion size quantitatively and to record the time it took to evaluate each case. Differences in interpretation speed were evaluated with the t test. Univariate analysis was used to measure accuracy; interpretations were compared with those of a nonblinded senior neuroradiologist, which served as the diagnostic standard. Intra- and interindividual variability were assessed with the kappa statistic. RESULTS: The time needed to read the studies decreased by an average of 65.6% (P < .05), with statistically significant reductions for each reader. Coregistration also changed the interpretation results in 21.9% of cases. Coregistration increased the accuracy of reading, but not significantly. Intraobserver variability improved from 0.3554 to 0.7328 with coregistration, and interobserver variability improved from 0.2670 to 0.3309. CONCLUSION: Image coregistration is technically feasible and accurate. Coregistration of follow-up studies significantly reduces the time needed for comparison and interpretation. It does not detract from the accuracy of interpretation of follow-up studies and tends to decrease intra- and interobserver variability.
Authors: Bradley J Erickson; Jayawant Mandrekar; Liqin Wang; Julia W Patriarche; Brian J Bartholmai; Christropher P Wood; E Paul Lindell; Anne-Marie Sykes; Gordon F Harms; Rebecca M Lindell; Katherine Andirole Journal: J Digit Imaging Date: 2007-06 Impact factor: 4.056
Authors: Daniel Forsberg; Amit Gupta; Christopher Mills; Brett MacAdam; Beverly Rosipko; Barbara A Bangert; Michael D Coffey; Christos Kosmas; Jeffrey L Sunshine Journal: Int J Comput Assist Radiol Surg Date: 2016-11-26 Impact factor: 2.924
Authors: Bradley J Erickson; Steve G Langer; Daniel J Blezek; William J Ryan; Todd L French Journal: J Digit Imaging Date: 2014-06 Impact factor: 4.056
Authors: Bradley J Erickson; Julia Patriarche; Christopher Wood; Norbert Campeau; E Paul Lindell; Vladimir Savcenko; Norman Arslanlar; Liqin Wang Journal: Cancer Inform Date: 2007-05-12