Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

C uring drug-sensitive tuberculosis (TB) takes 6 to 9 months of combination therapy despite the availability of antibiotics with potent in vitro activity, yet other pulmonary infectious diseases can be cured with single drugs that have similar mechanisms of action with only 3 to 14 days of treatment. One hypothesis used to explain the extended duration required with TB therapy is that subpopulations of bacteria become phenotypically drug tolerant in response to specific local microenvironmental conditions determined by the pathology of individual lesions (37). Understanding the features of these microenvironments and the conditions that generate tolerance may allow a rational design of drug regimens capable of shortening the time required to achieve a durable TB cure, but the methods used to evaluate new regimens have changed little and rely heavily on murine models of tuberculosis that typically have less complex lung pathology than human lesions. Premature discontinuation of treatment in humans results in disease relapse and the presence of cavities, and advanced lung pathology is strongly correlated with relapse (7,19,23). Only the rabbit and nonhuman primate models of pulmonary tuberculosis develop similar heterogeneous pathology, including the formation of cavitary disease. Guinea pigs, and some newer mouse models, develop more highly organized lesions, but these do not progress to cavities (for a comprehensive review of the comparative pathology of tuberculosis animal models, see reference 2).Nonterminal monitoring procedures, such as live imaging modalities, are increasingly being applied during TB drug efficacy experiments in animals and in human clinical trials (12,32,40,52). Structural and/or functional features observed in imaging modalities such as computed tomography (CT) and positron emission tomography (PET) are particularly attractive because they can be measured serially in a single subject at many time points during treatment. Computed tomography (CT) can add highly detailed information to the characteristic features of pulmonary tuberculosis visualized using conventional chest X-rays (1). CT scanning is typically used to monitor patients, assist in diagnosis, and assess surgical options for drug-resistant cases of disease (26), but there have been few examinations of the rate of change in CT findings during chemotherapy. The most detailed study of TB chemotherapy in patients (25) examined high-resolution CT scans from patients undergoing TB chemotherapy for up to 20 months. Old fibrotic lesions could be distinguished from active lesions, and criteria for the state of metabolic activity of lesions were proposed. However, that study did not sequentially

The primary goal of this study was to assess the suitability of 11C-Pittsburgh compound B (11C-PiB) blood–brain barrier delivery (K1) and relative delivery (R1) parameters as surrogate indices of cerebral blood flow (CBF), with a secondary goal of directly examining the extent to which simplified uptake measures of 11C-PiB retention (amyloid-β load) may be influenced by CBF, in a cohort of controls and patients with mild cognitive impairment (MCI) and Alzheimer disease (AD). Methods Nineteen participants (6 controls, 5 AD, 8 MCI) underwent MR imaging, 15O-water PET, and 11C-PiB PET in a single session. Fourteen regions of interest (including cerebellar reference region) were defined on MR imaging and applied to dynamic coregistered PET to generate time–activity curves. Multiple analysis approaches provided regional 15O-water and 11C-PiB measures of delivery and 11C-PiB retention that included compartmental modeling distribution volume ratio (DVR), arterial- and reference-based Logan DVR, simplified reference tissue modeling 2 (SRTM2) DVR, and standardized uptake value ratios. Spearman correlation was performed among delivery measures (i.e., 15O-water K1 and 11C-PiB K1, relative K1 normalized to cerebellum [Rel-K1-Water and Rel-K1-PiB], and 11C-PiB SRTM2-R1) and between delivery measures and 11C-PiB retention, using the Bonferroni method for multiple-comparison correction. Results Primary analysis showed positive correlations (ρ ≈0.2–0.5) between 15O-water K1 and 11C-PiB K1 that did not survive Bonferroni adjustment. Significant positive correlations were found between Rel-K1-Water and Rel-K1-PiB and between Rel-K1-Water and 11C-PiB SRTM2-R1 (ρ ≈0.5–0.8, P < 0.0036) across primary cortical regions. Secondary analysis showed few significant correlations between 11C-PiB retention and relative 11C-PiB delivery measures (but not 15O-water delivery measures) in primary cortical areas that arose only after accounting for cerebrospinal fluid dilution. Conclusion 11C-PiB SRTM2-R1 is highly correlated with regional relative CBF, as measured by 15O-water K1 normalized to cerebellum, and cross-sectional 11C-PiB retention did not strongly depend on CBF across primary cortical regions. These results provide further support for potential dual-imaging assessments of regional brain status (i.e., amyloid-β load and relative CBF) through dynamic 11C-PiB imaging.

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-enhanced magnetic resonance imaging (DCE-MRI), intraclass correlation coefficient (ICC), rate constant for plasma/interstitium contrast reagent transfer (K trans ), rate constant for contrast reagent intravasation (kep), pre-contrast tissue longitudinal relaxation rate constant (R10 ϭ 1/T10), region of interest (ROI), Tofts model (TM), extravascular, extracellular volume fraction (ve), within-subject coefficient of variation (wCV) Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as K trans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and v e (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical practice is limited with uncertainty in arterial input function (AIF) determination being one of the primary reasons. In this multicenter study to assess the effects of AIF variations on pharmacokinetic parameter estimation, DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Individual AIF from each data set was determined by each center and submitted to the managing center. These AIFs, along with a literature population averaged AIF, and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic data analysis using the Tofts model (TM). All other variables, including tumor region of interest (ROI) definition and pre-contrast T 1 , were kept constant to evaluate parameter variations caused solely by AIF discrepancies. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) of K trans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in K trans than v e and both were reduced when reference-tissue-adjusted AIFs were used. These variations were largely systematic, resulting in nearly unchanged parametric map patterns. The intravasation rate constant, k ep (ϭ K trans /v e ), was less sensitive to AIF variation than K trans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than K trans . INTRODUCTIONDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used in studies of cancer and other pathologies. Often included as one component of a prostate multiparametric MRI protocol (1), DCE-MRI is routinely used in clinical MRI