Assessment of micronecrotic tumor tissue using dynamic contrast-enhanced magnetic resonance imaging

In vivo labeling and quantitative imaging of neuronal populations using MRI

The study of neural circuits, which underlies perception, cognition, emotion, and behavior, is essential for understanding the mammalian brain, a complex organ consisting of billions of neurons. To study the structure and function of the brain, in vivo neuronal labeling and imaging techniques are crucial as they provide true physiological information that ex vivo methods cannot offer. In this paper, we present a new strategy for in vivo neuronal labeling and quantification using MRI. We demonstrate the efficacy of this method by delivering the oatp1a1 gene to the target neurons using rAAV2-retro virus. OATP1A1 protein expression on the neuronal membrane increased the uptake of a specific MRI contrast agent (Gd-EOB-DTPA), leading to hyperintense signals on T1W images of labeled neuronal populations. We also used dynamic contrast enhancement-based methods to obtain quantitative information on labeled neuronal populations in vivo.

Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer

The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast ([Formula: see text] and [Formula: see text]) and one slow ([Formula: see text] and [Formula: see text]) exchanging compartment, compared with the standard Tofts model parameters (Ktrans and kep). On average, prostate cancer had significantly higher values (p < 0.01) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.001) between Ktrans and [Formula: see text] for cancer, but weak correlation (r = 0.28, p < 0.05) between kep and [Formula: see text]. Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast [Formula: see text] had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM is useful for quantitative analysis of prostate DCE-MRI data and provides new information in the diagnosis of prostate cancer.

A compact solution for estimation of physiological parameters from ultrafast prostate dynamic contrast enhanced MRI

The Tofts pharmacokinetic model requires multiple calculations for analysis of dynamic contrast enhanced (DCE) MRI. In addition, the Tofts model may not be appropriate for the prostate. This can result in error propagation that reduces the accuracy of pharmacokinetic measurements. In this study, we present a compact solution allowing estimation of physiological parameters K ^trans and v _e from ultrafast DCE acquisitions, without fitting DCE-MRI data to the standard Tofts pharmacokinetic model. Since the standard Tofts model can be simplified to the Patlak model at early times when contrast efflux from the extravascular extracellular space back to plasma is negligible, K ^trans can be solved explicitly for a specific time. Further, v _e can be estimated directly from the late steady-state signal using the derivative form of Tofts model. Ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The contrast media concentration as function of time was calculated over each ROI using gradient echo signal equation with pre-contrast tissue T1 values, and using the 'reference tissue' model with a linear approximation. There was strong correlation (r  =  0.88-0.91, p   <  0.0001) between K ^trans extracted from the Tofts model and K ^trans estimated from the compact solution for prostate cancer and normal tissue. Additionally, there was moderate correlation (r  =  0.65-0.73, p   <  0.0001) between extracted versus estimated v _e. Bland-Altman analysis showed moderate to good agreement between physiological parameters extracted from the Tofts model and those estimated from the compact solution with absolute bias less than 0.20 min^-1 and 0.10 for K ^trans and v _e, respectively. The compact solution may decrease systematic errors and error propagation, and could increase the efficiency of clinical workflow. The compact solution requires high temporal resolution DCE-MRI due to the need to adequately sample the early phase of contrast media uptake.