Table 1.

Arterial Input Function (AIF) Quantification Methods by Participating Quantitative Imaging Network (QIN) Centers

Center	Method
OHSU	A single, fixed-size region of interest (ROI) was manually placed inside the femoral artery within the field of view (FOV). Averaged blood intensity time-course was extracted from the ROI, which is further converted to Cp(t) using the parameter values provided in the Materials and Methods section.
BWH	GE's OncoQuant prototype tool was used, which includes the following: (1) AIF Search Region Slice Localization; (2) AIF Search Mask Localization; (3) AIF Detection Using Shape-Based Statistics; and (4) AIF Signal-to-Concentration Conversion. See (27) for more details.
MCW	Motion-corrected dynamic contrast-enhanced (DCE) series were processed using probabilistic-independent component analysis implemented in the FSL (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). These were further whitened and projected into a 20-dimensional subspace using principal component analysis. The AIFs were manually chosen from the results (4, 36–38).
MS	ROIs were manually placed inside the iliac arteries within the field of view (FOV) using Osirix (v5.8; Pixmeo, Switzerland). For each AIF determination, 1 ROI was drawn on 1 DCE frame, and its position was adjusted when necessary to account for interframe subject motion. Blood intensity time-courses were extracted from the ROIs.
UM1	ROIs of 5 × 5 voxels were manually placed in 2 to 4 slices showing the highest artery conspicuity on maximum intensity projection displays of the baseline-subtracted DCE images. Voxel time-courses within the ROI were individually displayed on a 5 × 5 panel. Voxels with time-courses showing an AIF curve shape were then individually selected, and their locations and time-courses were automatically saved.
UM3	ROIs were manually drawn on both left and right femoral arteries on the central 4 slices. To minimize the in-flow effect, the inferior and superior slices were excluded. Further, 20 voxels within the ROIs with the highest signal increases were determined by thresholding the histogram of intensity changes. The average signal intensity time curve of the 20 voxels yielded the final AIF signal intensity time-course.
UPitt	Images were loaded into PMOD 3.505 (PMOD Technologies Ltd.), a commercial software package. Images were examined to search for an artery near the lesion. A region including the identified artery was surveyed using the voxel browser of PMOD to identify an area with high signal intensity change, followed by AIF ROI delineation.
UW	An adapted version of a positron emission tomography (PET) AIF extraction scheme (39), which does not require user-specified AIF ROI, was used. The approach was implemented in R (open-source). The extracted input function was then scaled so that the apparent extraction of gadolinium contrast reagent (CR) based on the analysis of the entire tissue volume signal is 2.5%.
VU	A seed point was placed manually inside an artery and then a region-growing method was applied to automatically select the AIF voxels. The intensity range for the region-growing method was set as 80% to 120% of that of the seed point, and the voxel distance from the seed was <10 voxels. Mean signal intensity time-course of the selected voxels was obtained.