Sandeep N. Gupta

Ferumoxytol: a new, clinically applicable label for stem-cell tracking in arthritic joints with MRI

AIM To develop a clinically applicable MRI technique for tracking stem cells in matrix-associated stem-cell implants, using the US FDA-approved iron supplement ferumoxytol. MATERIALS & METHODS Ferumoxytol-labeling of adipose-derived stem cells (ADSCs) was optimized in vitro. A total of 11 rats with osteochondral defects of both femurs were implanted with ferumoxytol- or ferumoxides-labeled or unlabeled ADSCs, and underwent MRI up to 4 weeks post matrix-associated stem-cell implant. The signal-to-noise ratio of different matrix-associated stem-cell implant was compared with t-tests and correlated with histopathology. RESULTS An incubation concentration of 500 µg iron/ml ferumoxytol and 10 µg/ml protamine sulfate led to significant cellular iron uptake, T2 signal effects and unimpaired ADSC viability. In vivo, ferumoxytol- and ferumoxides-labeled ADSCs demonstrated significantly lower signal-to-noise ratio values compared with unlabeled controls (p < 0.01). Histopathology confirmed engraftment of labeled ADSCs, with slow dilution of the iron label over time. CONCLUSION Ferumoxytol can be used for in vivo tracking of stem cells with MRI.

Dynamic contrast-enhanced MRI to evaluate the therapeutic response to neoadjuvant chemoradiation therapy in locally advanced rectal cancer

To evaluate the usefulness of perfusion parameters derived from dynamic contrast‐enhanced MR imaging (DCE‐MRI) for assessing the therapeutic response to neoadjuvant chemoradiation therapy (CRT) for locally advanced rectal cancer (LARC).

A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation☆

Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.

Magnetic Resonance Imaging Profile of Blood–Brain Barrier Injury in Patients With Acute Intracerebral Hemorrhage

Background Spontaneous intracerebral hemorrhage (ICH) is associated with blood–brain barrier (BBB) injury, which is a poorly understood factor in ICH pathogenesis, potentially contributing to edema formation and perihematomal tissue injury. We aimed to assess and quantify BBB permeability following human spontaneous ICH using dynamic contrast‐enhanced magnetic resonance imaging (DCE MRI). We also investigated whether hematoma size or location affected the amount of BBB leakage. Methods and Results Twenty‐five prospectively enrolled patients from the Diagnostic Accuracy of MRI in Spontaneous intracerebral Hemorrhage (DASH) study were examined using DCE MRI at 1 week after symptom onset. Contrast agent dynamics in the brain tissue and general tracer kinetic modeling were used to estimate the forward leakage rate (Ktrans) in regions of interest (ROI) in and surrounding the hematoma and in contralateral mirror–image locations (control ROI). In all patients BBB permeability was significantly increased in the brain tissue immediately adjacent to the hematoma, that is, the hematoma rim, compared to the contralateral mirror ROI (P<0.0001). Large hematomas (>30 mL) had higher Ktrans values than small hematomas (P<0.005). Ktrans values of lobar hemorrhages were significantly higher than the Ktrans values of deep hemorrhages (P<0.005), independent of hematoma volume. Higher Ktrans values were associated with larger edema volumes. Conclusions BBB leakage in the brain tissue immediately bordering the hematoma can be measured and quantified by DCE MRI in human ICH. BBB leakage at 1 week is greater in larger hematomas as well as in hematomas in lobar locations and is associated with larger edema volumes.

The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge

Pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI data allows estimation of quantitative imaging biomarkers such as Ktrans (rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (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 T1, 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 Ktrans obtained with unadjusted AIFs being as high as 0.74. AIF-caused variations were larger in Ktrans than ve 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, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs: 0.45 vs. 0.74), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation.

Accurate pharmacokinetic (PK) modeling of dynamic contrast enhanced MRI (DCE-MRI) in prostate cancer (PCa) requires knowledge of the concentration time course of the contrast agent in the feeding vasculature, the so-called arterial input function (AIF). The purpose of this study was to compare AIF choice in differentiating peripheral zone PCa from non-neoplastic prostatic tissue (NNPT), using PK analysis of high temporal resolution prostate DCE-MRI data and whole-mount pathology (WMP) validation. This prospective study was performed in 30 patients who underwent multiparametric endorectal prostate MRI at 3.0T and WMP validation. PCa foci were annotated on WMP slides and MR images using 3D Slicer. Foci ≥0.5cm(3) were contoured as tumor regions of interest (TROIs) on subtraction DCE (early-arterial - pre-contrast) images. PK analyses of TROI and NNPT data were performed using automatic AIF (aAIF) and model AIF (mAIF) methods. A paired t-test compared mean and 90th percentile (p90) PK parameters obtained with the two AIF approaches. Receiver operating characteristic (ROC) analysis determined diagnostic accuracy (DA) of PK parameters. Logistic regression determined correlation between PK parameters and histopathology. Mean TROI and NNPT PK parameters were higher using aAIF vs. mAIF (p<0.05). There was no significant difference in DA between AIF methods: highest for p90 volume transfer constant (K(trans)) (aAIF differences in the area under the ROC curve (Az) = 0.827; mAIF Az=0.93). Tumor cell density correlated with aAIF K(trans) (p=0.03). Our results indicate that DCE-MRI using both AIF methods is excellent in discriminating PCa from NNPT. If quantitative DCE-MRI is to be used as a biomarker in PCa, the same AIF method should be used consistently throughout the study.

Navigated DENSE strain imaging for post-radiofrequency ablation lesion assessment in the swine left atria.

AIMS Prior work has demonstrated that magnetic resonance imaging (MRI) strain can separate necrotic/stunned myocardium from healthy myocardium in the left ventricle (LV). We surmised that high-resolution MRI strain, using navigator-echo-triggered DENSE, could differentiate radiofrequency ablated tissue around the pulmonary vein (PV) from tissue that had not been damaged by radiofrequency energy, similarly to navigated 3D myocardial delayed enhancement (3D-MDE). METHODS AND RESULTS A respiratory-navigated 2D-DENSE sequence was developed, providing strain encoding in two spatial directions with 1.2 × 1.0 × 4 mm(3) resolution. It was tested in the LV of infarcted sheep. In four swine, incomplete circumferential lesions were created around the right superior pulmonary vein (RSPV) using ablation catheters, recorded with electro-anatomic mapping, and imaged 1 h later using atrial-diastolic DENSE and 3D-MDE at the left atrium/RSPV junction. DENSE detected ablation gaps (regions with >12% strain) in similar positions to 3D-MDE (2D cross-correlation 0.89 ± 0.05). Low-strain (<8%) areas were, on average, 33% larger than equivalent MDE regions, so they include both injured and necrotic regions. Optimal DENSE orientation was perpendicular to the PV trunk, with high shear strain in adjacent viable tissue appearing as a sensitive marker of ablation lesions. CONCLUSIONS Magnetic resonance imaging strain may be a non-contrast alternative to 3D-MDE in intra-procedural monitoring of atrial ablation lesions.

Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging

Abstract. Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant (Ktrans) and extravascular extracellular volume fraction (ve) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of Ktrans and ve values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.

4D vessel segmentation and tracking in Ultrasound

In this paper we describe a fast method to segment and track a vessel of interest in 4D (i.e. 3D + time) Ultrasound images. An initial 2D seed is used to initialize a single spatial Kalman-Filter tracker which tracks the vessel center-line in 3D. The 3D vessel is then segmented using a fast area weighted active contour method. This segmented 3D vessel is then tracked across multiple time-points using a set of temporal Kalman- Filters which track the movement of the center of the vessel in each 2D slice. The vessel boundaries are estimated by growing an area weighted active contour outward from the centerline. Based on qualitative as well as quantitative performance measures, the proposed method shows promising tracking results on numerous phantom as well as patient datasets.

Automated determination of arterial input function for DCE-MRI of the prostate

Prostate cancer is one of the commonest cancers in the world. Dynamic contrast enhanced MRI (DCE-MRI) provides an opportunity for non-invasive diagnosis, staging, and treatment monitoring. Quantitative analysis of DCE-MRI relies on determination of an accurate arterial input function (AIF). Although several methods for automated AIF detection have been proposed in literature, none are optimized for use in prostate DCE-MRI, which is particularly challenging due to large spatial signal inhomogeneity. In this paper, we propose a fully automated method for determining the AIF from prostate DCE-MRI. Our method is based on modeling pixel uptake curves as gamma variate functions (GVF). First, we analytically compute bounds on GVF parameters for more robust fitting. Next, we approximate a GVF for each pixel based on local time domain information, and eliminate the pixels with false estimated AIFs using the deduced upper and lower bounds. This makes the algorithm robust to signal inhomogeneity. After that, according to spatial information such as similarity and distance between pixels, we formulate the global AIF selection as an energy minimization problem and solve it using a message passing algorithm to further rule out the weak pixels and optimize the detected AIF. Our method is fully automated without training or a priori setting of parameters. Experimental results on clinical data have shown that our method obtained promising detection accuracy (all detected pixels inside major arteries), and a very good match with expert traced manual AIF.