Mary E. Loveless

Contrast-Enhanced Transvaginal Sonography of Benign Versus Malignant Ovarian Masses : Preliminary Findings

Objective. The aim of this prospective study was to evaluate differences in contrast enhancement and contrast enhancement kinetics in benign versus malignant ovarian masses with pulse inversion harmonic transvaginal sonography. Methods. Seventeen consecutive patients with 23 morphologically abnormal ovarian masses (solid or cystic with papillary excrescences, focally thickened walls, or irregular solid areas) smaller than 10 cm received a microbubble contrast agent intravenously while undergoing pulse inversion harmonic transvaginal sonography. The following parameters were assessed in all tumors: detectable contrast enhancement, time to peak enhancement (wash‐in), peak contrast enhancement, half wash‐out time, and area under the enhancement curve. Tumor histologic analysis was used to distinguish benign from malignant ovarian tumors. Results. Fourteen benign masses and 9 malignancies were studied. There was a statistically significant difference in the peak enhancement (mean ± SD, 23.3 ± 2.8 versus 12.3 ± 3.9 dB; P < .01), half wash‐out time (139.9 ± 43.6 versus 46.3 ± 19.7 seconds; P < .01), and area under the enhancement curve (2012.9 ± 532.9 versus 523.9 ± 318 seconds−1; P < .01) in malignant masses compared with benign disease. There was no statistically significant difference in the time to peak enhancement (26.1 ± 6.3 versus 24.9 ± 7.6 seconds; P = .07). Conclusions. Overall, our data showed a significant difference in the contrast enhancement kinetic parameters between benign and malignant ovarian masses.

A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer.

Quantitative analysis of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data requires the accurate determination of the arterial input function (AIF). A novel method for obtaining the AIF is presented here and pharmacokinetic parameters derived from individual and population-based AIFs are then compared. A Philips 3.0 T Achieva MR scanner was used to obtain 20 DCE-MRI data sets from ten breast cancer patients prior to and after one cycle of chemotherapy. Using a semi-automated method to estimate the AIF from the axillary artery, we obtain the AIF for each patient, AIF(ind), and compute a population-averaged AIF, AIF(pop). The extended standard model is used to estimate the physiological parameters using the two types of AIFs. The mean concordance correlation coefficient (CCC) for the AIFs segmented manually and by the proposed AIF tracking approach is 0.96, indicating accurate and automatic tracking of an AIF in DCE-MRI data of the breast is possible. Regarding the kinetic parameters, the CCC values for K(trans), v(p) and v(e) as estimated by AIF(ind) and AIF(pop) are 0.65, 0.74 and 0.31, respectively, based on the region of interest analysis. The average CCC values for the voxel-by-voxel analysis are 0.76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e).

A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals

For quantitative analysis of dynamic contrast enhanced magnetic resonance imaging data, the time course of the concentration of the contrast agent in the blood plasma, or vascular input function (VIF), is required. We compared pharmacokinetic parameters derived using individual and population‐based VIFs in mice for two different contrast agents, gadopentetate dimeglumine and P846. Eleven mice with subcutaneous 4T1 breast cancer xenografts were imaged at 7 T. A precontrast T1 map was acquired along with dynamic T1‐weighted gradient echo images before, during, and after a bolus injection of contrast agent delivered via a syringe pump. Each animals individual VIF and derived population‐averaged VIF were used to extract parameters from the signal‐time curves of tumor tissue at both the region of interest and voxel level. The results indicate that for both contrast agents, Ktrans values estimated using population‐averaged VIF have a high correlation (concordance correlation coefficient > 0.85) with Ktrans values estimated using individual VIF on both a region of interest and voxel level. This work supports the validity of using of a population‐based VIF with a stringent injection protocol in preclinical dynamic contrast enhanced magnetic resonance imaging studies. Magn Reson Med, 2011.

Quantitative effects of using compressed sensing in dynamic contrast enhanced MRI

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) involves the acquisition of images before, during and after the injection of a contrast agent. In order to perform quantitative modeling on the resulting signal intensity time course, data must be acquired rapidly, which compromises spatial resolution, signal to noise and/or field of view. One approach that may allow for gains in temporal or spatial resolution or signal to noise of an individual image is to use compressed sensing (CS) MRI. In this study, we demonstrate the accuracy of extracted pharmacokinetic parameters from DCE-MRI data obtained as part of pre-clinical and clinical studies in which fully sampled acquisitions have been retrospectively undersampled by factors of 2, 3 and 4 in Fourier space and then reconstructed with CS. The mean voxel-level concordance correlation coefficient for K(trans) (i.e. the volume transfer constant) obtained from the 2× accelerated and the fully sampled data is 0.92 and 0.90 for mouse and human data, respectively; for 3×, the results are 0.79 and 0.79, respectively; for 4×, the results are 0.64 and 0.70, respectively. The mean error in the tumor mean K(trans) for the mouse and human data at 2× acceleration is 1.8% and -4.2%, respectively; at 3×, 3.6% and -10%, respectively; at 4×, 7.8% and -12%, respectively. These results suggest that CS combined with appropriate reduced acquisitions may be an effective approach to improving image quality in DCE-MRI.

Practical Dynamic Contrast Enhanced MRI in Small Animal Models of Cancer: Data Acquisition, Data Analysis, and Interpretation

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) consists of the continuous acquisition of images before, during, and after the injection of a contrast agent. DCE-MRI allows for noninvasive evaluation of tumor parameters related to vascular perfusion and permeability and tissue volume fractions, and is frequently employed in both preclinical and clinical investigations. However, the experimental and analytical subtleties of the technique are not frequently discussed in the literature, nor are its relationships to other commonly used quantitative imaging techniques. This review aims to provide practical information on the development, implementation, and validation of a DCE-MRI study in the context of a preclinical study (though we do frequently refer to clinical studies that are related to these topics).

Earlier detection of tumor treatment response using magnetic resonance diffusion imaging with oscillating gradients

An improved method for detecting early changes in tumors in response to treatment, based on a modification of diffusion-weighted magnetic resonance imaging, has been demonstrated in an animal model. Early detection of therapeutic response in tumors is important both clinically and in pre-clinical assessments of novel treatments. Noninvasive imaging methods that can detect and assess tumor response early in the course of treatment, and before frank changes in tumor morphology are evident, are of considerable interest as potential biomarkers of treatment efficacy. Diffusion-weighted magnetic resonance imaging is sensitive to changes in water diffusion rates in tissues that result from structural variations in the local cellular environment, but conventional methods mainly reflect changes in tissue cellularity and do not convey information specific to microstructural variations at sub-cellular scales. We implemented a modified imaging technique using oscillating gradients of the magnetic field for evaluating water diffusion rates over very short spatial scales that are more specific for detecting changes in intracellular structure that may precede changes in cellularity. Results from a study of orthotopic 9L gliomas in rat brains indicate that this method can detect changes as early as 24 h following treatment with 1,3-bis(2-chloroethyl)-1-nitrosourea, when conventional approaches do not find significant effects. These studies suggest that diffusion imaging using oscillating gradients may be used to obtain an earlier indication of treatment efficacy than previous magnetic resonance imaging methods.

A Method for Assessing the Microvasculature in a Murine Tumor Model Using Contrast-Enhanced Ultrasonography

Objective. The purpose of this study was to develop a method for assessing tumor vascularity in a preclinical model of breast cancer using contrast‐enhanced ultrasonography. Methods. Eight mice were injected with 67NR breast cancer cells on their hind limbs and imaged with ultrasonography 8 days later. Mice were injected with an ultrasound contrast agent (UCA), and a sequence of images of the resultant backscattered echoes was recorded before and after high‐power “destruction” pulses for each of multiple parallel planes. From these, data maps of the maximum contrast enhancement (within each time course) were constructed for each pixel, which enabled reconstruction of high‐resolution coregistered sections into a 3‐dimensional (3D) volume reflecting tumor vascularity. Additional studies were performed to determine the duration and repeatability of image enhancement, and images were correlated with conventional 3D power Doppler measurements. Results. The lifetime of the UCA in vivo was found to be 4.3 ± 1.09 minutes (mean ± SD). The 3D contrast‐enhanced ultrasonographic technique produced images that correlated well with power Doppler images in specific regions but also depicted additional regions of flow surrounding the power Doppler signal. The mean correlation coefficient between voxel measurements of the central slice for each animal was 0.64 ± 0.07 (P < .01). In addition, sequential studies in each animal were reproducible. Conclusions. A method producing high‐resolution volumetric assessments of tumor vascularity in a preclinical model of breast cancer is shown that correlates with other ultrasonographic measures of blood flow, which may provide greater sensitivity to the microvasculature.

Incorporation of diffusion-weighted magnetic resonance imaging data into a simple mathematical model of tumor growth

We build on previous work to show how serial diffusion-weighted MRI (DW-MRI) data can be used to estimate proliferation rates in a rat model of brain cancer. Thirteen rats were inoculated intracranially with 9L tumor cells; eight rats were treated with the chemotherapeutic drug 1,3-bis(2-chloroethyl)-1-nitrosourea and five rats were untreated controls. All animals underwent DW-MRI immediately before, one day and three days after treatment. Values of the apparent diffusion coefficient (ADC) were calculated from the DW-MRI data and then used to estimate the number of cells in each voxel and also for whole tumor regions of interest. The data from the first two imaging time points were then used to estimate the proliferation rate of each tumor. The proliferation rates were used to predict the number of tumor cells at day three, and this was correlated with the corresponding experimental data. The voxel-by-voxel analysis yielded Pearson’s correlation coefficients ranging from −0.06 to 0.65, whereas the region of interest analysis provided Pearson’s and concordance correlation coefficients of 0.88 and 0.80, respectively. Additionally, the ratio of positive to negative proliferation values was used to separate the treated and control animals (p <0.05) at an earlier point than the mean ADC values. These results further illustrate how quantitative measurements of tumor state obtained non-invasively by imaging can be incorporated into mathematical models that predict tumor growth.

Assessing the reproducibility of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer

Quantitative dynamic contrast enhanced magnetic resonance imaging estimates parameters related to tissue vascularity and volume fractions; additionally, semiquantitative parameters such as the initial area under the curve can be utilized to describe tissue behavior. The aim of this study was to establish the reproducibility of quantitative and semiquantitative analysis of dynamic contrast enhanced magnetic resonance imaging in a murine model of breast cancer. For each animal, a T1‐weighted, gradient‐echo sequence was used to acquire two sets of dynamic contrast enhanced magnetic resonance imaging data separated by 5 h. Data were acquired at both a 0.05 mm3 (1282, n = 12) and a 0.2 mm3 (642, n = 12) resolution, and analysis was performed using both the Tofts–Kety (to estimate Ktrans and ve) and extended Tofts–Kety (Ktrans, ve, and vp) models. Reproducibility analysis was performed for both the center slice and the total tumor volume for all parameters. For the total volume analysis, the repeatability index for Ktrans is 0.073 min−1 in the standard model analysis and 0.075 min−1 in the extended model analysis at the 1282 acquisition. For the 642 acquisition, the values are 0.089 and 0.063 min−1 for the standard and extended models, respectively. The repeatability index for initial area under the curve was 0.0039 and 0.0042 mM min for the 1282 and 642 acquisitions, respectively. Magn Reson Med, 2013.

Assessing reproducibility of diffusion-weighted magnetic resonance imaging studies in a murine model of HER2+ breast cancer.

BACKGROUND AND PURPOSE The use of diffusion-weighted magnetic resonance imaging (DW-MRI) as a surrogate biomarker of response in preclinical studies is increasing. However, before a biomarker can be reliably employed to assess treatment response, the reproducibility of the technique must be established. There is a paucity of literature that quantifies the reproducibility of DW-MRI in preclinical studies; thus, the purpose of this study was to investigate DW-MRI reproducibility in a murine model of HER2+ breast cancer. MATERIALS AND METHODS Test-Retest DW-MRI scans separated by approximately six hours were acquired from eleven athymic female mice with HER2+ xenografts using a pulsed gradient spin echo diffusion-weighted sequence with three b values [150, 500, and 800s/mm(2)]. Reproducibility was assessed for the mean apparent diffusion coefficient (ADC) from tumor and muscle tissue regions. RESULTS The threshold to reflect a change in tumor physiology in a cohort of mice is defined by the 95% confidence interval (CI), which was±0.0972×10(-3)mm(2)/s (±11.8%) for mean tumor ADC. The repeatability coefficient defines this threshold for an individual mouse, which was±0.273×10(-3)mm(2)/s. The 95% CI and repeatability coefficient for mean ADC of muscle tissue were±0.0949×10(-3)mm(2)/s (±8.30%) and±0.266×10(-3)mm(2)/s, respectively. CONCLUSIONS Mean ADC of tumors is reproducible and appropriate for detecting treatment-induced changes on both an individual and mouse cohort basis.