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Purpose: Calculation of the precontrast longitudinal relaxation times T10 is an integral part of the Tofts-based pharmacokinetic PK analysis of dynamic contrast enhanced–magnetic resonance images. The purpose of this study was to investigate the interpatient and over time variability of T10 in head and neck primary tumors and involved nodes and to determine the median T10 for primary and nodes T 10 p,n . The authors also looked at the implication of using voxel-based T10 values versus region of interest ROI-based T10 on the calculated values for vascular permeability K trans and extracellular volume fraction ve. Methods: Twenty head and neck cancer patients receiving concurrent chemoradiation and molecularly targeted agents on a prospective trial comprised the study population. Voxel-based T10’s were generated using a gradient echo sequence on a 1.5 T MR scanner using the variable flip angle method with two flip angles J. A. Brookes et al., “Measurement of spin-lattice relaxation times with FLASH for dynamic MRI of the breast,” Br. J. Radiol. 69, 206–214 1996. The voxel-based T10, K trans , and ve were calculated using iCAD’s ® Nashua, NH software. The mean T10’s in muscle and fat ROIs were calculated T10 m,f . To assess reliability of ROI drawing, T10 p,n values from ROIs delineated by 2 users A and B were calculated as the average of the T10’s for 14 patients. For a subset of three patients, the T10 variability from baseline to end of treatment was also investigated. The K trans and ve from primary and node ROIs were calculated using voxel-based T10 values and T 10 p,n and differences reported. Results: The calculated T 10 values for fat and muscle are within the range of values reported in literature for 1.5 T, i.e., T 10 m = 0.958 s and T 10 f = 0.303 s. The average over 14 patients of the T10’s based on drawings by users A and B were T 10 pA = 0.804 s, T 10 nA = 0.760 s, T 10 pB = 0.849 s, and T10 nB = 0.810 s. The absolute percentage difference between K trans and ve calculated with voxelbased T10 versus T 10 p,n ranged from 6% to 81% and from 2% to 24%, respectively. Conclusions: There is a certain amount of variability in the median T10 values between patients, but the differences are not significant. There were also no statistically significant differences between the T10 values for primary and nodes at baseline and the subsequent time points p = 0.94 Friedman test. Voxel-based T10 calculations are essential when quantitative Tofts-based PK analysis in heterogeneous tumors is needed. In the absence of T10 mapping capability, when a relative, qualitative analysis is deemed sufficient, a value of T 10 p,n = 0.800 s can be used as an estimate for T10 for both the primary tumor and the affected nodes in head and neck cancers at all the time points considered. © 2010 American Association of Physicists in Medicine. DOI: 10.1118/1.3427487

Purpose To quantify the repeatability of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in patients with glioblastoma multiforme. Materials and Methods IRB approval and informed consent were obtained for this Health Insurance Portability and Accountability Act-compliant study. Sixteen patients with glioblastoma multiforme underwent MR imaging at two time points without interval intervention. ADC and FA maps were registered to the contrast-enhanced and fluid-attenuated inversion recovery (FLAIR) image volumes. Volumes of tumor-related enhancement (TRE) and FLAIR signal abnormality (FSA) were defined using a semiautomated segmentation technique. Results Repeated observations of mean ADC and mean FA were highly consistent within both TRE (ADC: r = 0.947,P < 0.0001; FA: r = 0.947, P < 0.0001) and FSA (ADC: r = 0.979, P < 0.0001; FA: r = 0.972, P < 0.0001). Within TRE, repeatability coefficients and 95% confidence intervals (CIs) for change measured 0.104 × 10−3 mm2S−1 and 7.4% (ADC) and 0.0196 and 13.9% (FA), respectively. Within FSA, repeatability coefficients and 95% CI for change measured 0.071 × 10−3 mm2S−1 and 5.2% (ADC) and 0.0159 and 8.7% (FA), respectively. To detect 10% changes in mean ADC, sample sizes of nine (TRE) and six (FSA) patients would be required. The same change in mean FA would require sample sizes of 21 (TRE) and 10 (FSA) patients, respectively. Conclusion Changes after therapy greater than the repeatability coefficient or 95% CI for change are unlikely to be related to variability in the measurement of ADC and FA. J. Magn. Reson. Imaging 2009;29:1199–1205. © 2009 Wiley-Liss, Inc.

To compare the performance of three physiologically-based pharmacokinetic (PBPK) models for predicting gadolinium contrast agent concentration-time curves (Gd-CTCs) obtained in superior sagittal sinus (SSS), cerebral cortex, and psoas muscle.Three published whole-body PBPK models were modified to predict Gd-CTCs in normal-appearing tissue. The models differed in the number of organs modeled and total number of compartments, and were designated as the "well-mixed," "delay," and "dispersion" models. The suitability of the three models to predict Gd-CTC was studied using data from dynamic contrast-enhanced MR perfusion imaging obtained at 1.5T from 10 patients with glioblastoma multiforme and at 3.0T from five patients with liver metastases.The dispersion model produced better fits than the delay model in the SSS (P0.0001) and cerebral cortex (P0.0001), and better fits than the well-mixed model in psoas muscle (P0.005). No model produced better fits than the dispersion model at any of the three locations.In this evaluation, the dispersion model was most robust for prediction of Gd-CTCs derived from dynamic contrast-enhanced (DCE)-MRI. This represents a preliminary step in the development of a PBPK model useful for predicting Gd-CTCs at a time resolution appropriate for dynamic MRI applications.

Purpose: To determine the optimal flip angle combination (oFAc) that generates voxel‐based T10 values, the median T10 for primary and nodes ( T 10 p,n ) , and its implications on vascular permeability (PERM) and extracellular volume fraction (EVF) in HN patients treated with targeted therapy and chemoradiation. Method and Materials: To generate voxel‐based T10, a gradient echo sequence was used on a 1.5 T scanner with TR=6.44 msec and FA of 10, 15, 20, 30, 45°. For different FA combinations, the voxel‐based values were calculated using CAD Sciences® (White Plains, NY). The average of the median T10 in muscle and fat ( T 10 m,f ) regions of interest (ROI) in 3 patients was calculated. Criteria for oFAc included minimal variation from published muscle and fat values at 1.5T, and minimum number of FA used for fitting. To determine T 10 p,n , values from ROIs delineated by 2 users (A,B) were calculated. For 3 patients, the PERM and EVF from primary and nodes ROIs were calculated using T10 maps and T 10 p,n . Results: The 10–45° FAc was chosen for subsequent T10 mapping as it had the greatest percentage of fitted pixels (90% for muscle, 100% for fat, % 83 for primary, 72% for nodes) and a T 10 m = 0.923 sec , and T 10 f =0.379 sec , compared to reported 0.870 and 0.260 sec for muscle and fat, respectively. From14 patients, T 10 pA = 0.804, T 10 nA = 0.760, T 10 pB = 0.849, T 10 nB = 0.810 sec . The difference between the PERM and EVF calculated with voxel‐based T10 versus T 10 p,n ranged from 6–81% for PERM, and 2.5–23% for EVF. Conclusion. The 10–45° FAc is fast and accurately describes the known T10 of normal tissue. Voxel‐based T10 calculations are essential for correct Tofts‐based PA in heterogeneous tumors. For HN, primary and nodes T 10 p,n = 0.8 sec is a good estimate for T10 in the absence of T10 mapping capability.

Purpose: To evaluate the use of DCE‐MRI to measure changes in tumorphysiology in LAHN cancer patients receiving TT and cisplatin based concurrent chemoradiation (ChemoRT). Material and Methods: Eligible patients with LAHN were enrolled on an IRB approved clinical trial to establish the safety and efficacy of adding TT (bevacizumab and erlotinib) to ChemoRT. To quantify this efficacy, DCE‐MR images were acquired on a 1.5T GE Signa Exite scanner before treatment started, at the end of the lead‐in phase (2 weeks of TT alone), at the end of week 1 of ChemoRT, and at the end of the ChemoRT (70 Gy). The images were analyzed using a full Time Point (fTP) pharmacokinetic analysis implemented by CAD Sciences® (White Plains, NY) that measures the vascular permeability (PERM) and extracellular volume fraction (EVF). The T10 for the primary and nodes was determined from series acquired with varying TRs. A dynamic 3D spoiled gradient echo sequence was used before and after bolus injection of Gd DTPA (Magnevist®). Regions of interest (ROIs) were defined over the entire extent of the tumor and LN, respectively. Enhancement curve analysis, PERM and EVF statistics and ROI volume comparisons were performed. Results: The T10 for tumor was 1500 msec, and 2000 msec for LN. All the fTP analyses used these values. Fifteen patients have been imaged to date. We found that coronal imaging allows better ROI selection without vascular averaging for this type of subjects.. Detailed analyses of all time points have been completed on three patients, all with clinical complete response. Combined PERM and EVF analyses showed marked decreases with treatment (p