Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom.

Purpose: Validation of quantitative imaging biomarkers is a challenging task, due to the difficulty in measuring the ground truth of the target biological process. A digital phantom-based framework is established to systematically validate the quantitative characterization of tumor-associated vascular morphology and hemodynamics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Approach: A digital phantom is employed to provide a ground-truth vascular system within which 45 synthetic tumors are simulated. Morphological analysis is performed on high-spatial resolution DCE-MRI data (spatial/temporal resolution = 30 to 300 µm/60 s) to determine the accuracy of locating the arterial inputs of tumor-associated vessels (TAVs). Hemodynamic analysis is then performed on the combination of high-spatial resolution and high-temporal resolution (spatial/temporal resolution = 60 to 300 µm/1 to 10 s) DCE-MRI data, determining the accuracy of estimating tumor-associated blood pressure, vascular extraction rate, interstitial pressure, and interstitial flow velocity. Results: The observed effects of acquisition settings demonstrate that, when optimizing the DCE-MRI protocol for the morphological analysis, increasing the spatial resolution is helpful but not necessary, as the location and arterial input of TAVs can be recovered with high accuracy even with the lowest investigated spatial resolution. When optimizing the DCE-MRI protocol for hemodynamic analysis, increasing the spatial resolution of the images used for vessel segmentation is essential, and the spatial and temporal resolutions of the images used for the kinetic parameter fitting require simultaneous optimization. Conclusion: An in silico validation framework was generated to systematically quantify the effects of image acquisition settings on the ability to accurately estimate tumor-associated characteristics.

Bilateral asymmetry of quantitative parenchymal kinetics at ultrafast DCE-MRI predict response to neoadjuvant chemotherapy in patients with HER2+ breast cancer.

PURPOSE: To assess whether measurement of the bilateral asymmetry of semiquantitative and quantitative perfusion parameters from ultrafast dynamic contrast-enhanced MRI (DCE-MRI), allows early prediction of pathologic response after neoadjuvant chemotherapy (NAC) in patients with HER2+ breast cancer. MATERIALS AND METHODS: Twenty-eight female patients with HER2+ breast cancer treated with NAC who underwent pre-NAC ultrafast DCE-MRI (3-9 s/phase) were enrolled for this study. Four semiquantitative and two quantitative parenchymal parameters were calculated for each patient. Ipsilateral/contralateral (I/C) ratio (for four parameters) and the difference between (for two parameters) ipsi- and contra-lateral parenchymal kinetic parameters (kBPE) were compared for patients with pathologic complete response (pCR) and those having residual disease. Lasso regression with leave-one-out cross validation was used to determine the optimal combination of parameters for a regression model and multivariable logistic regression was used to identify independent predictors for pCR. Chi-squared test, two-sided t-test and Kruskal-Wallis test were used. RESULTS: The Ktrans I/C ratio cutoff value of 1.11 had a sensitivity of 83.3% and specificity of 75% for pCR. The ve I/C ratio cutoff value of 1.1 had a sensitivity of 75% and specificity of 81.3% for pCR. The area under the receiver operating characteristic curve of the three-kBPE parameter model, including initial area under the enhancement curve (AUC30) I/C ratio, KtransI/C ratio and ve I/C ratio, was 0.89 with sensitivity of 91.7% at specificity of 81.3%. CONCLUSION: Quantitative assessment of bilateral asymmetry kBPE from pre-NAC ultrafast DCE-MRI can predict pCR in patients with HER2+ breast cancer.

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI.

The high spatial and temporal resolution of dynamic contrast-enhanced MRI (DCE-MRI) can improve the diagnostic accuracy of breast cancer screening in patients who have dense breasts or are at high risk of breast cancer. However, the spatiotemporal resolution of DCE-MRI is limited by technical issues in clinical practice. Our earlier work demonstrated the use of image reconstruction with enhancement-constrained acceleration (ECA) to increase temporal resolution. ECA exploits the correlation in k-space between successive image acquisitions. Because of this correlation, and due to the very sparse enhancement at early times after contrast media injection, we can reconstruct images from highly under-sampled k-space data. Our previous results showed that ECA reconstruction at 0.25 seconds per image (4 Hz) can estimate bolus arrival time (BAT) and initial enhancement slope (iSlope) more accurately than a standard inverse fast Fourier transform (IFFT) when k-space data is sampled following a Cartesian based sampling trajectory with adequate signal-to-noise ratio (SNR). In this follow-up study, we investigated the effect of different Cartesian based sampling trajectories, SNRs and acceleration rates on the performance of ECA reconstruction in estimating contrast media kinetics in lesions (BAT, iSlope and Ktrans) and in arteries (Peak signal intensity of first pass, time to peak, and BAT). We further validated ECA reconstruction with a flow phantom experiment. Our results show that ECA reconstruction of k-space data acquired with 'Under-sampling with Repeated Advancing Phase' (UnWRAP) trajectories with an acceleration factor of 14, and temporal resolution of 0.5 s/image and high SNR (SNR ≥ 30 dB, noise standard deviation (std) < 3%) ensures minor errors (5% or 1 s error) in lesion kinetics. Medium SNR (SNR ≥ 20 dB, noise std ≤ 10%) was needed to accurately measure arterial enhancement kinetics. Our results also suggest that accelerated temporal resolution with ECA with 0.5 s/image is practical.

Towards Patient-Specific Optimization of Neoadjuvant Treatment Protocols for Breast Cancer Based on Image-Guided Fluid Dynamics.

OBJECTIVE: This study establishes a fluid dynamics model personalized with patient-specific imaging data to optimize neoadjuvant therapy (i.e., doxorubicin) protocols for breast cancers. METHODS: Ten patients recruited at the University of Chicago were included in this study. Quantitative dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging data are leveraged to estimate patient-specific hemodynamic properties, which are then used to constrain the mechanism-based drug delivery model. Then, computer simulations of this model yield the subsequent drug distribution throughout the breast. By systematically varying the dosing schedule, we identify an optimized regimen for each patient using the maximum safe therapeutic duration (MSTD), which is a metric balancing treatment efficacy and toxicity. RESULTS: With an individually optimized dose (range = 12.11-15.11 mg/m2 per injection), a 3-week regimen consisting of a uniform daily injection significantly outperforms all other scheduling strategies (P < 0.001). In particular, the optimal protocol is predicted to significantly outperform the standard protocol (P < 0.001), improving the MSTD by an average factor of 9.93 (range = 6.63 to 14.17). CONCLUSION: A clinical-mathematical framework was developed by integrating quantitative MRI data, advanced image processing, and computational fluid dynamics to predict the efficacy and toxicity of neoadjuvant therapy protocols, thus enabling the rational identification of an optimal therapeutic regimen on a patient-specific basis. SIGNIFICANCE: Our clinical-computational approach has the potential to enable optimization of therapeutic regimens on a patient-specific basis and provide guidance for prospective clinical trials aimed at refining neoadjuvant therapy protocols for breast cancers.

Differences Between Ipsilateral and Contralateral Early Parenchymal Enhancement Kinetics Predict Response of Breast Cancer to Neoadjuvant Therapy.

RATIONALE AND OBJECTIVES: To determine whether kinetics measured with ultrafast dynamic contrast-enhanced magnetic resonance imaging in tumor and normal parenchyma pre- and post-neoadjuvant therapy (NAT) can predict the response of breast cancer to NAT. MATERIALS AND METHODS: Twenty-four patients with histologically confirmed invasive breast cancer were enrolled. They were scanned with ultrafast dynamic contrast-enhanced magnetic resonance imaging (3-7 seconds/frame) pre- and post-NAT. Four kinetic parameters were calculated in the segmented tumors, and ipsi- and contra-lateral normal parenchyma: (1) tumor (tSE30) or background parenchymal relative enhancement at 30 seconds (BPE30), (2) maximum relative enhancement slope (MaxSlope), (3) bolus arrival time (BAT), and (4) area under relative signal enhancement curve for the initial 30 seconds (AUC30). The tumor kinetics and the differences between ipsi- and contra-lateral parenchymal kinetics were compared for patients achieving pathologic complete response (pCR) vs those who had residual disease after NAT. The chi-squared test and two-sided t-test were used for baseline demographics. The Wilcoxon rank sum test and one-way analysis of variance were used for differential responses to therapy. RESULTS: Patients with similar pre-NAT mean BPE30, median BAT and mean AUC30 in the ipsi- and contralateral normal parenchyma were more likely to achieve pCR following NAT (p < 0.02). Patients classified as having residual cancer burden (RCB) II after NAT showed higher post-NAT tSE30 and tumor AUC30 and higher post-NAT MaxSlope in ipsilateral normal parenchyma compared to those classified as RCB I or pCR (p < 0.05). CONCLUSION: Bilateral asymmetry in normal parenchyma could predict treatment outcome prior to NAT. Post-NAT tumor kinetics could evaluate the aggressiveness of residual tumor.

Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI.

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom.

Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the "ground truth" data to which the output of a given processing method must be compared. One way to address this issue is via "digital phantoms", which are numerical models that provide known biophysical properties of a particular object of interest.  In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 µm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 µm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 µm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 µm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.

Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent.

There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n = 6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4 T scanner. DCE-MRI data were acquired with 1.5 s temporal resolution before and after a LD (0.04 mmol/kg), then again after 30 min followed by a HD (0.2 mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (Ktrans and ve) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (ß and γ). There were moderate to strong correlations (r = 0.69-0.97, p < 0001) for parameters Ktrans, ve, A, α and ß from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p < 0.05) than those from HD data. The parameter ratios, Ktrans, ve, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p < 0.003) for tumor. T2* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.

Patient-Specific Characterization of Breast Cancer Hemodynamics Using Image-Guided Computational Fluid Dynamics.

The overall goal of this study is to employ quantitative magnetic resonance imaging (MRI) data to constrain a patient-specific, computational fluid dynamics (CFD) model of blood flow and interstitial transport in breast cancer. We develop image processing methodologies to generate tumor-related vasculature-interstitium geometry and realistic material properties, using dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) data. These data are used to constrain CFD simulations for determining the tumor-associated blood supply and interstitial transport characteristics unique to each patient. We then perform a proof-of-principle statistical comparison between these hemodynamic characteristics in 11 malignant and 5 benign lesions from 12 patients. Significant differences between groups (i.e., malignant versus benign) were observed for the median of tumor-associated interstitial flow velocity ( P = 0.028 ), and the ranges of tumor-associated blood pressure (P = 0.016) and vascular extraction rate (P = 0.040). The implication is that malignant lesions tend to have larger magnitude of interstitial flow velocity, and higher heterogeneity in blood pressure and vascular extraction rate. Multivariable logistic models based on combinations of these hemodynamic data achieved excellent differentiation between malignant and benign lesions with an area under the receiver operator characteristic curve of 1.0, sensitivity of 1.0, and specificity of 1.0. This image-based model system is a fundamentally new way to map flow and pressure fields related to breast tumors using only non-invasive, clinically available imaging data and established laws of fluid mechanics. Furthermore, the results provide preliminary evidence for this methodology's utility for the quantitative characterization of breast cancer.

Low-dose imaging technique (LITE) MRI: initial experience in breast imaging.

OBJECTIVES: To compare a low-dose dynamic contrast-enhanced breast MRI protocol (LITE MRI) to standard-dosage using a dual-dose injection technique. METHODS: 8 females with a total of 10 lesions with imaging features compatible with fibroadenoma were imaged using a dual-dose dynamic contrast-enhanced-MRI (DCE-MRI) technique. After pre-contrast scans, 15% of a standard dose of contrast was administered; approximately 10 min later, the remaining 85% of the standard dose was administered. Enhancement kinetic parameters, conspicuity and signal-to-noise ratio were measured quantitatively. RESULTS: One lesion showed no enhancement in either DCE series. All nine of the enhancing lesions were visualized in both the low-dose and standard-dose images. While the (low-to-standard) ratio of contrast doses was roughly 0.18, this did not match the ratios of kinetic parameters. Lesion conspicuity and enhancement rate were both higher in the low-dose images, with (low-to-standard) ratios 1.5 ± 0.1 and 1.2 ± 0.4, respectively. The upper limit of enhancement (ratio 0.3 ± 0.1) and signal-to-noise ratio (ratio 0.5 ± 0.1) were higher in the standard-dose images, but less than expected based on the ratio of the doses. CONCLUSIONS: This preliminary study demonstrates that LITE MRI has the potential to match standard DCE-MRI in the detection of enhancing lesions. Additionally, LITE MRI may enhance sensitivity to contrast media dynamics. ADVANCES IN KNOWLEDGE: Lower doses of MRI contrast media may be equally effective in the detection of breast lesions, and increase sensitivity to contrast media dynamics. LITE MRI may help increase screening compliance and long-term patient safety.

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.

Ultrafast Dynamic Contrast-Enhanced Breast MRI: Kinetic Curve Assessment Using Empirical Mathematical Model Validated with Histological Microvessel Density.

RATIONALE AND OBJECTIVES: To evaluate whether parameters from empirical mathematical model (EMM) for ultrafast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) correlate with histological microvessel density (MVD) in invasive breast cancer. MATERIALS AND METHODS: Ninety-eight consecutive patients with invasive breast cancer underwent an institutional review board-approved ultrafast DCE-MRI including a pre- and 18 postcontrast whole breast ultrafast scans (3 seconds) followed by four standard scans (60 seconds) using a 3T system. Region of interest was placed within each lesion where the highest signal increase was observed on ultrafast DCE-MRI, and the increase rate of enhancement was calculated as follows: ΔS = (SIpost - SIpre)/SIpre. The kinetic curve obtained from ultrafast DCE-MRI was analyzed using a truncated EMM: ΔS(t) = A(1 - e-αt), where A is the upper limit of the signal intensity, α (min-1) is the rate of signal increase. The initial slope of the kinetic curve is given by Aα. Initial area under curve (AUC30) and time of initial enhancement was calculated. From the standard DCE-MRI, the initial enhancement rate (IER) and the signal enhancement ratio (SER) were calculated as follows: IER = (SIearly - SIpre)/SIpre, SER = (SIearly - SIpre)/(SIdelayed - SIpre). The parameters were compared to MVD obtained from surgical specimens. RESULTS: A, α, Aα, AUC30, and time of initial enhancement significantly correlated with MVD (r = 0.29, 0.40, 0.51, 0.43, and -0.32 with p = 0.0027, p < 0.0001, p < 0.0001, p < 0.0001, and p = 0.0012, respectively), whereas IER and SER from standard DCE-MRI did not. CONCLUSION: The parameters of the EMM, especially the initial slope or Aα, for ultrafast DCE-MRI correlated with MVD in invasive breast cancer.

Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors.

PURPOSE: We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions. THEORY AND METHODS: Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability. RESULTS: A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans , and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91). CONCLUSION: This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.

Fast Temporal Resolution Dynamic Contrast-Enhanced MRI: Histogram Analysis Versus Visual Analysis for Differentiating Benign and Malignant Breast Lesions.

OBJECTIVE: The purpose of this study was to validate a kinetic assessment based on visually identified peak enhancement, which is routinely used in clinical practice, for differentiating benign from malignant lesions during fast dynamic contrast-enhanced MRI. MATERIALS AND METHODS: Between January 2015 and December 2016, 90 consecutively registered patients with 105 breast lesions (40 benign, 65 malignant) underwent dynamic contrast-enhanced 1.5-T MRI that included one unenhanced and eight contrast-enhanced fast temporal resolution (10 seconds) whole-breast acquisitions. Histogram analysis was performed to measure the voxel-based enhancement of the entire lesion to obtain 90th, 75th, and 50th percentile values at each time point and to generate kinetic curves. Two observers selected visually identified peak enhancement within the lesions to generate the kinetic curves. The kinetic curves from histogram and visually identified peak enhancement analyses were fitted by means of an empiric mathematic model (EMM): ΔS(t) = A × (1 - e-αt), where A is the upper limit of signal intensity, e indicates the exponential function, and α (min-1) is the rate of increase in signal intensity. The initial slope of the kinetic curve (A × α) and the initial AUC (AUC30) were calculated. These parameters were compared between benign and malignant lesions, and results from visually identified peak enhancement analysis were compared with those from histogram analysis. RESULTS: Benign lesions were successfully differentiated from malignant lesions in both visually identified peak enhancement and histogram analyses (90th and 75th percentile values) on the basis of α, A × α, and AUC30 from the EMM. There was no significant difference in ROC AUC in these EMM parameters between visually identified peak enhancement and histogram analyses (p = 0.21). CONCLUSION: Kinetic assessment with visually identified peak enhancement was acceptable for differentiating benign from malignant lesions.

Dynamic field-of-view imaging to increase temporal resolution in the early phase of contrast media uptake in breast DCE-MRI: A feasibility study.

PURPOSE: To increase diagnostic accuracy of breast MRI by increasing temporal resolution and more accurately sampling the early kinetics of contrast media uptake. We tested the feasibility of accelerating bilateral breast DCE-MRI by reducing the FOV, allowing aliasing, and unfolding the resulting images. METHODS: Previous experience with an "ultrafast" protocol for bilateral breast DCE-MRI (6-10 s temporal resolution) showed that the number of significantly enhancing voxels is very low in the first 30-45 s after contrast media injection. This suggests that overlap of enhancing voxels in aliased images will be very infrequent. Therefore, aliased images can be acquired during the first 30-45 s after contrast media injection and unfolded to produce full-FOV images with few errors. In a proof-of-principle test, aliased images were simulated from the first 30 s of full-FOV acquisitions. Cases with relatively dense early enhancement were selected to test this method in a worst-case scenario. In an initial test, an FOV of 60% the size of the full FOV was simulated. To reduce the probability of errors due to overlapping voxels in aliased images, we then tested a dynamic FOV approach. The FOV was progressively increased so that enhancing voxels could not overlap at multiple time-points, and areas where enhancing voxels overlapped at a given time-point could be unfolded by interpolating between the preceding and subsequent time-points (acquired with different FOVs). The simulated FOV sizes for each of the time-points were 31%, 44%, and 77% of the full FOV. Subtraction images (post- minus precontrast) were generated for aliased images and filtered to select significantly enhancing voxels. Comparison of early, highly aliased images, with later, less aliased images then helped to identify the true locations of enhancing voxels. RESULTS: In the initial aliasing simulations, an average of 2.9% of the enhancing voxels above the chest wall overlapped in the aliased images (range 0.1%-6.7%). The similarity between simulated unfolded images and the correct full-FOV images, evaluated using CW-SSIM (complex wavelet similarity index), was 0.50 ± 0.26, 0.76 ± 0.09, and 0.80 ± 0.10 for the first, second, and third time-point, respectively (numbers closer to 1 indicate more similar images). For the dynamic FOV tests, an average of 11% of the enhancing voxels above the chest wall overlapped (range 0%-40%) due to greater aliasing at early time-points. Despite more voxels overlapping, the CW-SSIM values for the data acquired with dynamic FOVs were 0.64 ± 0.25, 0.93 ± 0.04, and 0.97 ± 0.02 for the first, second, and third time-points, respectively. CONCLUSIONS: Dynamic FOV imaging allows accelerated bilateral breast DCE-MRI during the early contrast media uptake phase. This method relies on the sparsity of enhancement at the early phases of DCE-MRI of the breast. The results of simulations suggest that dynamic FOV imaging and unfolding produces images that are very close to fully sampled images, and allows temporal resolution as high as 2 s per image.

Comparison of arterial input functions measured from ultra-fast dynamic contrast enhanced MRI and dynamic contrast enhanced computed tomography in prostate cancer patients.

The purpose of this study was to evaluate the accuracy of arterial input functions (AIFs) measured from dynamic contrast enhanced (DCE) MRI following a low dose of contrast media injection. The AIFs measured from DCE computed tomography (CT) were used as 'gold standard'. A total of twenty patients received CT and MRI scans on the same day. Patients received 120 ml Iohexol in DCE-CT and a low dose of (0.015 mM kg-1) of gadobenate dimeglumine in DCE-MRI. The AIFs were measured in the iliac artery and normalized to the CT and MRI contrast agent doses. To correct for different temporal resolution and sampling periods of CT and MRI, an empirical mathematical model (EMM) was used to fit the AIFs first. Then numerical AIFs (AIFCT and AIFMRI) were calculated based on fitting parameters. The AIFMRI was convolved with a 'contrast agent injection' function ([Formula: see text]) to correct for the difference between MRI and CT contrast agent injection times (~1.5 s versus 30 s). The results show that the EMMs accurately fitted AIFs measured from CT and MRI. There was no significant difference (p > 0.05) between the maximum peak amplitude of AIFs from CT (22.1 ± 4.1 mM/dose) and MRI after convolution (22.3 ± 5.2 mM/dose). The shapes of the AIFCT and [Formula: see text] were very similar. Our results demonstrated that AIFs can be accurately measured by MRI following low dose contrast agent injection.

Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer.

RATIONALE AND OBJECTIVES: This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI. MATERIALS AND METHODS: Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (ß), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones. RESULTS: Cancer (α = 6.45 ± 4.71 s-1, ß = 0.067 ± 0.042 s-1, slope = 3.78 ± 1.90 s-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s-1, ß = 0.034 ± 0.050 s-1, slope = 1.9 ± 1.4 s-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection. CONCLUSION: Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.

Gadolinium-Functionalized Peptide Amphiphile Micelles for Multimodal Imaging of Atherosclerotic Lesions.

The leading causes of morbidity and mortality globally are cardiovascular diseases, and nanomedicine can provide many improvements including disease-specific targeting, early detection, and local delivery of diagnostic agents. To this end, we designed fibrin-binding, peptide amphiphile micelles (PAMs), achieved by incorporating the targeting peptide cysteine-arginine-glutamic acid-lysine-alanine (CREKA), with two types of amphiphilic molecules containing the gadoliniuim (Gd) chelator diethylenetriaminepentaacetic acid (DTPA), DTPA-bis(stearylamide)(Gd), and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[(poly(ethylene glycol) (PEG))-2000]-DTPA(Gd) (DSPE-PEG2000-DTPA(Gd)). The material characteristics of the resulting nanoparticle diagnostic probes, clot-binding properties in vitro, and contrast enhancement and safety for dual, optical imaging-magnetic resonance imaging (MRI) were evaluated in the atherosclerotic mouse model. Transmission electron micrographs showed a homogenous population of spherical micelles for formulations containing DSPE-PEG2000-DTPA(Gd), whereas both spherical and cylindrical micelles were formed upon mixing DTPA-BSA(Gd) and CREKA amphiphiles. Clot-binding assays confirmed DSPE-PEG2000-DTPA(Gd)-based CREKA micelles targeted clots over 8-fold higher than nontargeting (NT) counterpart micelles, whereas no difference was found between CREKA and NT, DTPA-BSA(Gd) micelles. However, in vivo MRI and optical imaging studies of the aortas and hearts showed fibrin specificity was conferred by the peptide ligand without much difference between the nanoparticle formulations or shapes. Biodistribution studies confirmed that all micelles were cleared through both the reticuloendothelial system and renal clearance, and histology showed no signs of necrosis. In summary, these studies demonstrate the successful synthesis, and the molecular imaging capabilities of two types of CREKA-Gd PAMs for atherosclerosis. Moreover, we demonstrate the differences in micelle formulations and shapes and their outcomes in vitro versus in vivo for site-specific, diagnostic strategies, and provide the groundwork for the detection of thrombosis via contrast-enhancing agents and concurrent therapeutic delivery for theranostic applications.