Temporal similarity perfusion mapping: A standardized and model-free method for detecting perfusion deficits in stroke - Fig 6

<p>Panel A shows the inter-rater bias measures for TSP, TTP or MTT map-based lesion volumes. Panel B shows that the Pearson's correlation between TSP and TTP map-based lesion volumes was high (r(<i>18</i>) = 0.73, p<0.0003). Panel C shows that the effective CNR was greater for TSP (352.3) compared to TTP (p<0.03) and MTT maps (p<0.03).</p

Demographic and clinical characteristics of the study groups.

<p>Demographic and clinical characteristics of the study groups.</p

TSP maps in the 20 TIA imaging-negative patients.

<p>The TSP maps were uniform with values approaching 1.0 for the whole brain. The color scale for all TSP maps runs from <0 to 1, blue to red.</p

Example of the automated TSP maps in an ischemic stroke patient with a right-sided perfusion deficit.

<p>The images were generated using an iterative method that first calculated an average time-series of healthy tissue (top left) to generate a Pearson’s correlation map of all voxels in the brain based on correlation with the average time-series of healthy tissue (top right). Signal intensity time-series for voxels in healthy and under perfused tissue are in the bottom panel. Voxels in healthy tissue demonstrate a signal intensity time-series that is like the average signal intensity time-series for all healthy tissue, while a voxel in the perfusion deficit will have a signal intensity time-series that is delayed, dispersed and/or decreased.</p

Example of TSP maps with varying Pearson’s R correlation thresholds in a patient with a right-sided perfusion deficit.

<p>The red encircled map shows the threshold used for the automated lesion detection. No visual differences were found between TSP maps constructed with these different thresholds (B) Mean signal values for lesion and healthy tissue (based on the unbiased perfusion lesion) showed relatively stable TSP values within TSP maps for healthy and lesion tissue demonstrating robustness. (C) The mean difference in signal between healthy and perfusion tissue increased slightly (by ~0.03 in the 0.9 map compared to the 0.6 map) in TSP values.</p

Bland-Altman plots of the inter-rater reliability between the two readers for TSP, TTP and MTT map-based lesion volumes.

<p>Bland-Altman plots of the inter-rater reliability between the two readers for TSP, TTP and MTT map-based lesion volumes.</p

Figure comparing the spatial overlap between lesion volumes drawn on TSP and TTP maps for each patient.

<p>Overlap from lesion volumes from each rater was analyzed separately and averaged for each patient.</p

Expertise levels and experience of the human observers.

a<p>level of expertise based as on Reference <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042227#pone.0042227-tenCate1" target="_blank">[24]</a>: <i>I</i> has knowledge and some skills, <i>II</i> acts under full supervision, <i>III</i> acts under limited supervision, <i>IV</i> acts without supervision, <i>V</i> supervises and teaches;</p>b<p>Years since the observer started reading and evaluating chest CT scans; Observer 1 to 5 were considered ‘less experienced observers’ and observer 6 to 8 were considered ‘experienced observers’.</p

Comparison between the included subsample of participants and the total cohort of screening participants in the study period between July 2007–September 2008.

<p>Comparison using Mann-Whitney U test revealed no significant differences between the two groups.</p>a<p>data in the randomly selected subsample of male participants;</p>b<p>data in the total group of male participants screened in the study period</p

Flow diagram of the study.

<p>Flow diagram showing the selection of the study population from the total screening trial cohort. The index test presented is for the observer with the highest positive predictive value.</p