Evaluation of 39 medical implants at 7.0 T

OBJECTIVE

With increased signal to noise ratios, 7.0-T MRI has the potential to contribute unique information regarding anatomy and pathophysiology of a disease. However, concerns for the safety of subjects with metallic medical implants have hindered advancement in this field. The purpose of the present research was to evaluate the MRI safety for 39 commonly used medical implants at 7.0 T.

METHODS

Selected metallic implants were tested for magnetic field interactions, radiofrequency-induced heating and artefacts using standardized testing techniques.

RESULTS

5 of the 39 implants tested may be unsafe for subjects undergoing MRI at 7.0 T.

CONCLUSION

Implants were deemed either "MR Conditional" or "MR Unsafe" for the 7.0-T MRI environment. Further research is needed to expand the existing database categorizing implants that are acceptable for patients referred for MRI examinations at 7.0 T.

ADVANCES IN KNOWLEDGE

Lack of MRI testing for common metallic medical implants limits the translational potential of 7.0-T MRI. For safety reasons, patients with metallic implants are not allowed to undergo a 7.0-T MRI scan, precluding part of the population that can benefit from the detailed resolution of ultra-high-field MRIs. This investigation provides necessary MRI testing of common medical implants at 7.0 T.

Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer

The purpose of this work was to determine the relationship between the apparent diffusion coefficient (ADC, from diffusion-weighted (DW) MRI), the extravascular, extracellular volume fraction (ve , from dynamic contrast-enhanced (DCE) MRI), and histological measurement of the extracellular space fraction. Athymic nude mice were injected with either human epidermal growth factor receptor 2 positive (HER2+) BT474 (n = 15) or triple negative MDA-MB-231 (n = 20) breast cancer cells, treated with either Herceptin (n = 8), Abraxane (low dose n = 7, high dose n = 6), or saline (n = 7 for each cell line), and imaged using DW- and DCE-MRI before, during, and after treatment. After the final imaging acquisition, the tissue was resected and evaluated by histological analysis. H&E-stained central slices were scanned using a digital brightfield microscope and evaluated with thresholding techniques to calculate the extracellular space. For both BT474 and MDA-MB-231, the median ADC of the central slice exhibited a significantly positive correlation with the corresponding central slice extracellular space as measured by H&E (p = 0.03, p < 0.01, respectively). Median ve calculated from the central slice showed differing results between the two cell lines. For BT474, a significant correlation between ve and extracellular space was calculated (p = 0.02), while MDA-MB-231 tumors did not demonstrate a significant correlation (p = 0.64). Additionally, there was no correlation discovered between ADC and ve with either whole tumor analysis or central slice analysis (p > 0.05). While ADC correlates well with the histologically determined fraction of extracellular space, these data add to the growing body of literature that suggests that ve derived from DCE-MRI is not a reliable biomarker of extracellular space for a range of physiological conditions.

Evaluating treatment response using DW-MRI and DCE-MRI in trastuzumab responsive and resistant HER2-overexpressing human breast cancer xenografts

We report longitudinal diffusion-weighted magnetic resonance imaging (DW-MRI) and dynamic contrast enhanced (DCE)-MRI (7 T) studies designed to identify functional changes, prior to volume changes, in trastuzumab-sensitive and resistant HER2 + breast cancer xenografts. Athymic mice (N = 33) were subcutaneously implanted with trastuzumab-sensitive (BT474) or trastuzumab-resistant (HR6) breast cancer cells. Tumor-bearing animals were distributed into four groups: BT474 treated and control, HR6 treated and control. DW- and DCE-MRI were conducted at baseline, day 1, and day 4; trastuzumab (10 mg/kg) or saline was administered at baseline and day 3. Animals were sacrificed on day 4 and tumors resected for histology. Voxel-based DW- and DCE-MRI analyses were performed to generate parametric maps of ADC, K^trans, and v_e. On day 1, no differences in tumor size were observed between any of the groups. On day 4, significant differences in tumor size were observed between treated vs. control BT474, treated BT474 vs. treated HR6, and treated vs. control HR6 (P < .0001). On day 1, v_e was significantly higher in the BT474 treated group compared to BT474 control (P = .002) and HR6 treated (P = .004). On day 4, v_e and K^trans were significantly higher in the treated BT474 tumors compared to BT474 controls (P = .0007, P = .02, respectively). A significant decrease in Ki67 staining reinforced response in the BT474 treated group compared to BT474 controls (P = .02). This work demonstrated that quantitative MRI biomarkers have the sensitivity to differentiate treatment response in HER2 + tumors prior to changes in tumor size.

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.

A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results

Accurate quantification of pharmacokinetic parameters in dynamic contrast-enhanced (DCE) MRI may be affected by the passive diffusion of contrast agent (CA) within the tissue. By introducing an additional term into the standard Tofts-Kety (STK) model, we correct for the effects of CA diffusion. We first develop the theory describing a CA diffusion corrected STK model (DTK). The model is then tested in simulation with simple models of diffusion. The DTK model is also fit to 18 in vivo DCE-MRI acquisitions from murine models of cancer and results are compared to those from the STK model. The DTK model returned estimates with significantly lower error than the STK model (p ≪ 0.001). In poorly perfused (i.e., K(trans) ≤ 0.05 min(-1)) regions the STK model returned unphysical ve values, while the DTK model estimated ve with less than 7% error in noise-free simulations. Results in vivo data revealed similar trends. For voxels with low K(trans) values and late peak concentration times the STK model returned ve estimates >1.0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion.

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 (128(2) , n=12) and a 0.2 mm3 (64(2), 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 128(2) acquisition. For the 64(2) 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 128(2) and 64(2) acquisitions, respectively.

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.

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).

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.

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 4T(1) breast cancer xenografts were imaged at 7 T. A precontrast T(1) map was acquired along with dynamic T(1) -weighted gradient echo images before, during, and after a bolus injection of contrast agent delivered via a syringe pump. Each animal's 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, K(trans) values estimated using population-averaged VIF have a high correlation (concordance correlation coefficient > 0.85) with K(trans) 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.

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.

Coregistration of ultrasonography and magnetic resonance imaging with a preliminary investigation of the spatial colocalization of vascular endothelial growth factor receptor 2 expression and tumor perfusion in a murine tumor model

We present an ultrasonography (US)-magnetic resonance imaging (MRI) coregistration technique and examine its application in a preliminary multimodal, multiparametric study in a preclinical model of breast cancer. Nine mice were injected with 67NR breast cancer cells and imaged 6 and 9 days later with 4.7 T MRI and high-frequency US. Tumor volumes from each data set were segmented independently by two investigators and coregistered using an iterative closest point algorithm. In addition to anatomic images, vascular endothelial growth factor receptor 2 (VEGFR2) distribution images from the central tumor slice using VEGFR2-targeted ultrasound contrast agent (UCA) and measurements of perfusion and extravascular-extracellular volume fraction using dynamic contrast-enhanced MRI were acquired from five mice for multiparametric coregistration. Parametric maps from each modality were coregistered and examined for spatial correlation. Average registration root mean square (RMS) error was 0.36 +/- 0.11 mm, less than approximately two voxels. Segmented volumes were compared between investigators to minimize interobserver variability; the average RMS error was 0.23 +/- 0.09 mm. In the preliminary study, VEGFR2-targeted UCA data did not demonstrate direct spatial correlation with magnetic resonance measures of vascular properties. In summary, a method for accurately coregistering small animal US and MRI has been presented that allows for comparison of quantitative metrics provided by the two modalities.

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.