Improvement in breast lesion characterization with dynamic contrast-enhanced MRI using pharmacokinetic modeling and bookendT1 measurements

A functional form for a representative individual arterial input function measured from a population using high temporal resolution DCE MRI

PURPOSE

To measure the arterial input function (AIF), an essential component of tracer kinetic analysis, in a population of patients using an optimized dynamic contrast-enhanced (DCE) imaging sequence and to estimate inter- and intrapatient variability. From these data, a representative AIF that may be used for realistic simulation studies can be extracted.

METHODS

Thirty-nine female patients were imaged on multiple visits before and during a course of neoadjuvant chemotherapy for breast cancer. A total of 97 T₁ -weighted DCE studies were analyzed including bookend estimates of T₁ and model-fitting to each individual AIF. Area under the curve and cardiac output were estimated from each first pass peak, and these data were used to assess inter- and intrapatient variability of the AIF.

RESULTS

Interpatient variability exceeded intrapatient variability of the AIF. There was no change in cardiac output as a function of MR visit (mean value 5.6 ± 1.1 L/min) but baseline blood T₁ increased significantly following the start of chemotherapy (which was accompanied by a decrease in hematocrit).

CONCLUSION

The AIF in an individual patient can be measured reproducibly but the variability of AIFs between patients suggests that use of a population AIF will decrease the precision of tracer kinetic analysis performed in cross-patient comparison studies. A representative AIF is presented that is typical of the population but retains the characteristics of an individually measured AIF.

Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

Dynamic myraidpro contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.

Evaluation of T1/T2 ratios in a pilot study as a potential biomarker of biopsy: proven benign and malignant breast lesions in correlation with histopathological disease stage

Aim:

Early breast cancer detection is important for intervention and prognosis. Advances in treatment and outcome require diagnostic tools with highly positive predictive value.

Purpose:

To study the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions.

Methods:

A cross-sectional study of 69 women with 69 known or suspicious breast lesions were scanned with mixed-turbo spin echo pulse sequence. Patients were grouped according to histopathological assessment of disease stage: untreated malignant tumor, treated malignancy and benign disease.

Results & Discussion:

Elevated T1/T2 means were observed for biopsy-proven malignant lesions and for malignant lesions treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. The qMRI-obtained T1/T2 ratios correlated with histopathology. Analysis revealed correlation between elevated T1/T2 ratio and disease stage. This could provide valuable complementary information on tissue properties as an additional diagnostic tool.

Early detection is important for successful intervention in breast cancer. We studied the potential role of quantitative MRI (qMRI) using T1/T2 ratios to differentiate benign from malignant breast lesions. Sixty nine women with breast lesions were scanned with qMRI. Elevated ratios were observed for biopsy-proven malignant lesions and for malignant lesions that were treated prior to qMRI with chemotherapy and/or radiation, as compared with benign lesions. With further studies, this approach could provide valuable information concerning tissue properties in addition to established breast imaging sequences and be an additional diagnostic tool.

Correlation of Tumor Immunohistochemistry with Dynamic Contrast-Enhanced and DSC-MRI Parameters in Patients with Gliomas

BACKGROUND AND PURPOSE

Tumor CBV is a prognostic and predictive marker for patients with gliomas. Tumor CBV can be measured noninvasively with different MR imaging techniques; however, it is not clear which of these techniques most closely reflects histologically-measured tumor CBV. Our aim was to investigate the correlations between dynamic contrast-enhanced and DSC-MR imaging parameters and immunohistochemistry in patients with gliomas.

MATERIALS AND METHODS

Forty-three patients with a new diagnosis of glioma underwent a preoperative MR imaging examination with dynamic contrast-enhanced and DSC sequences. Unnormalized and normalized cerebral blood volume was obtained from DSC MR imaging. Two sets of plasma volume and volume transfer constant maps were obtained from dynamic contrast-enhanced MR imaging. Plasma volume obtained from the phase-derived vascular input function and bookend T1 mapping (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function and bookend T1 mapping (K^trans_Φ) were determined. Plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K^trans_SI) were acquired, without T1 mapping. Using CD34 staining, we measured microvessel density and microvessel area within 3 representative areas of the resected tumor specimen. The Mann-Whitney U test was used to test for differences according to grade and degree of enhancement. The Spearman correlation was performed to determine the relationship between dynamic contrast-enhanced and DSC parameters and histopathologic measurements.

RESULTS

Microvessel area, microvessel density, dynamic contrast-enhanced, and DSC-MR imaging parameters varied according to the grade and degree of enhancement (P < .05). A strong correlation was found between microvessel area and Vp_Φ and between microvessel area and unnormalized blood volume (r_s ≥ 0.61). A moderate correlation was found between microvessel area and normalized blood volume, microvessel area and Vp_SI, microvessel area and K^trans_Φ, microvessel area and K^trans_SI, microvessel density and Vp_Φ, microvessel density and unnormalized blood volume, and microvessel density and normalized blood volume (0.44 ≤ r_s ≤ 0.57). A weaker correlation was found between microvessel density and K^trans_Φ and between microvessel density and K^trans_SI (r_s ≤ 0.41).

CONCLUSIONS

With dynamic contrast-enhanced MR imaging, use of a phase-derived vascular input function and bookend T1 mapping improves the correlation between immunohistochemistry and plasma volume, but not between immunohistochemistry and the volume transfer constant. With DSC-MR imaging, normalization of tumor CBV could decrease the correlation with microvessel area.

Quality assurance in MRI breast screening: comparing signal-to-noise ratio in dynamic contrast-enhanced imaging protocols

MRI has been extensively used in breast cancer staging, management and high risk screening. Detection sensitivity is paramount in breast screening, but variations of signal-to-noise ratio (SNR) as a function of position are often overlooked. We propose and demonstrate practical methods to assess spatial SNR variations in dynamic contrast-enhanced (DCE) breast examinations and apply those methods to different protocols and systems. Four different protocols in three different MRI systems (1.5 and 3.0 T) with receiver coils of different design were employed on oil-filled test objects with and without uniformity filters. Twenty 3D datasets were acquired with each protocol; each dataset was acquired in under 60 s, thus complying with current breast DCE guidelines. In addition to the standard SNR calculated on a pixel-by-pixel basis, we propose other regional indices considering the mean and standard deviation of the signal over a small sub-region centred on each pixel. These regional indices include effects of the spatial variation of coil sensitivity and other structured artefacts. The proposed regional SNR indices demonstrate spatial variations in SNR as well as the presence of artefacts and sensitivity variations, which are otherwise difficult to quantify and might be overlooked in a clinical setting. Spatial variations in SNR depend on protocol choice and hardware characteristics. The use of uniformity filters was shown to lead to a rise of SNR values, altering the noise distribution. Correlation between noise in adjacent pixels was associated with data truncation along the phase encoding direction. Methods to characterise spatial SNR variations using regional information were demonstrated, with implications for quality assurance in breast screening and multi-centre trials.

Technical Note: Clinical translation of the Rapid-Steady-State-T1 MRI method for direct cerebral blood volume quantification

PURPOSE

In preclinical studies, the Rapid-Steady-State-T1 (RSST1) MRI method has advantages over conventional MRI methods for blood volume fraction (BVf) mapping, since after contrast agent administration, the BVf is directly quantifiable from the signal amplitude corresponding to the vascular equilibrium magnetization. This study focuses on its clinical implementation and feasibility.

METHODS

Following sequence implementation on clinical Philips Achieva scanners, the RSST1-method is assessed at 1.5 and 3 T in the follow-up examination of neurooncological patients receiving 0.1-0.2 mmol/kg Gd-DOTA to determine the threshold dose needed for cerebral BVf quantification. Confounding effects on BVf quantification such as transendothelial water exchange, transverse relaxation, and contrast agent extravasation are evaluated.

RESULTS

For a dose≥0.13 mmol/kg at 1.5 T and ≥0.16 mmol/kg at 3 T, the RSST1-signal time course in macrovessels and brain tissue with Gd-DOTA impermeable vasculature reaches a steady state at maximum amplitude for about 8 s. In macrovessels, a BVf of 100% was obtained validating cerebral microvascular BVf quantification (3.5%-4.5% in gray matter and 1.5%-2.0% in white matter). In tumor tissue, a continuously increasing signal is detected, necessitating signal modeling for tumor BVf calculation.

CONCLUSIONS

Using approved doses of Gd-DOTA, the steady state RSST1-signal in brain tissue is reached during the first pass and corresponds to the BVf. The first-pass duration is sufficient to allow accurate BVf quantification. The RSST1-method is appropriate for serial clinical studies since it allows fast and straightforward BVf quantification without arterial input function determination. This quantitative MRI method is particularly useful to assess the efficacy of antiangiogenic agents.

Comparison of the Diagnostic Accuracy of DSC- and Dynamic Contrast-Enhanced MRI in the Preoperative Grading of Astrocytomas

BACKGROUND AND PURPOSE

Dynamic contrast-enhanced MR imaging parameters can be biased by poor measurement of the vascular input function. We have compared the diagnostic accuracy of dynamic contrast-enhanced MR imaging by using a phase-derived vascular input function and "bookend" T1 measurements with DSC MR imaging for preoperative grading of astrocytomas.

MATERIALS AND METHODS

This prospective study included 48 patients with a new pathologic diagnosis of an astrocytoma. Preoperative MR imaging was performed at 3T, which included 2 injections of 5-mL gadobutrol for dynamic contrast-enhanced and DSC MR imaging. During dynamic contrast-enhanced MR imaging, both magnitude and phase images were acquired to estimate plasma volume obtained from phase-derived vascular input function (Vp_Φ) and volume transfer constant obtained from phase-derived vascular input function (K(trans)_Φ) as well as plasma volume obtained from magnitude-derived vascular input function (Vp_SI) and volume transfer constant obtained from magnitude-derived vascular input function (K(trans)_SI). From DSC MR imaging, corrected relative CBV was computed. Four ROIs were placed over the solid part of the tumor, and the highest value among the ROIs was recorded. A Mann-Whitney U test was used to test for difference between grades. Diagnostic accuracy was assessed by using receiver operating characteristic analysis.

RESULTS

Vp_ Φ and K(trans)_Φ values were lower for grade II compared with grade III astrocytomas (P < .05). Vp_SI and K(trans)_SI were not significantly different between grade II and grade III astrocytomas (P = .08-0.15). Relative CBV and dynamic contrast-enhanced MR imaging parameters except for K(trans)_SI were lower for grade III compared with grade IV (P ≤ .05). In differentiating low- and high-grade astrocytomas, we found no statistically significant difference in diagnostic accuracy between relative CBV and dynamic contrast-enhanced MR imaging parameters.

CONCLUSIONS

In the preoperative grading of astrocytomas, the diagnostic accuracy of dynamic contrast-enhanced MR imaging parameters is similar to that of relative CBV.

Sensitivity of quantitative myocardial dynamic contrast-enhanced MRI to saturation pulse efficiency, noise and t1 measurement error: Comparison of nonlinearity correction methods

PURPOSE

To compare methods designed to minimize or correct signal nonlinearity in quantitative myocardial dynamic contrast-enhanced (DCE) MRI.

METHODS

DCE-MRI studies were simulated and data acquired in eight volunteers. Signal nonlinearity was corrected using either a dual-bolus approach or model-based correction using proton-density weighted imaging (conventional or dual-sequence acquisition) or T1 data (native or bookend). Scanning of healthy and infarcted myocardium at 3 T was simulated, including noise, saturation imperfection and T1 measurement error. Data were analyzed using model-based deconvolution with a one-compartment (mono-exponential) model.

RESULTS

Substantial variation between methods was demonstrated in volunteers. In simulations the dual-bolus method proved stable for realistic levels of saturation efficiency but demonstrated bias due to residual nonlinearity. Model-based methods performed ideally in the absence of confounding error sources and were generally robust to noise or saturation imperfection, except for native T1 based correction which was highly sensitive to the latter. All methods demonstrated large variation in accuracy above an over-saturation level where baseline signal was nulled. For the dual-sequence approach this caused substantial bias at the saturation efficiencies observed in volunteers.

CONCLUSION

The choice of nonlinearity correction method in myocardial DCE-MRI impacts on accuracy and precision of estimated parameters, particularly in the presence of nonideal saturation.

Quantitative hepatic perfusion modeling using DCE-MRI with sequential breathholds

PURPOSE

To develop and demonstrate the feasibility of a new formulation for quantitative perfusion modeling in the liver using interrupted DCE-MRI data acquired during multiple sequential breathholds.

MATERIALS AND METHODS

A new mathematical formulation to estimate quantitative perfusion parameters using interrupted data was developed. Using this method, we investigated whether a second degree-of-freedom in the tissue residue function (TRF) improves quality-of-fit criteria when applied to a dual-input single-compartment perfusion model. We subsequently estimated hepatic perfusion parameters using DCE-MRI data from 12 healthy volunteers and 9 cirrhotic patients with a history of hepatocellular carcinoma (HCC); and examined the utility of these estimates in differentiating between healthy liver, cirrhotic liver, and HCC.

RESULTS

Quality-of-fit criteria in all groups were improved using a Weibull TRF (2 degrees-of-freedom) versus an exponential TRF (1 degree-of-freedom), indicating nearer concordance of source DCE-MRI data with the Weibull model. Using the Weibull TRF, arterial fraction was greater in cirrhotic versus normal liver (39 ± 23% versus 15 ± 14%, P = 0.07). Mean transit time (20.6 ± 4.1 s versus 9.8 ± 3.5 s, P = 0.01) and arterial fraction (39 ± 23% versus 73 ± 14%, P = 0.04) were both significantly different between cirrhotic liver and HCC, while differences in total perfusion approached significance.

CONCLUSION

This work demonstrates the feasibility of estimating hepatic perfusion parameters using interrupted data acquired during sequential breathholds.

Perfusion MRI: the five most frequently asked technical questions

OBJECTIVE

This and its companion article address the 10 most frequently asked questions that radiologists face when planning, performing, processing, and interpreting different MR perfusion studies in CNS imaging.

CONCLUSION

Perfusion MRI is a promising tool in assessing stroke, brain tumors, and patients with neurodegenerative diseases. Most of the impediments that have limited the use of perfusion MRI can be overcome to allow integration of these methods into modern neuroimaging protocols.

Quantitative evaluation of dual-flip-angle T1 mapping on DCE-MRI kinetic parameter estimation in head and neck

PURPOSE

To quantitatively evaluate the kinetic parameter estimation for head and neck (HN) dynamic contrast-enhanced (DCE) MRI with dual-flip-angle (DFA) T1 mapping.

MATERIALS AND METHODS

Clinical DCE-MRI datasets of 23 patients with HN tumors were included in this study. T1 maps were generated based on multiple-flip-angle (MFA) method and different DFA combinations. Tofts model parameter maps of k(ep), K(trans) and v(p) based on MFA and DFAs were calculated and compared. Fitted parameter by MFA and DFAs were quantitatively evaluated in primary tumor, salivary gland and muscle.

RESULTS

T1 mapping deviations by DFAs produced remarkable kinetic parameter estimation deviations in head and neck tissues. In particular, the DFA of [2º, 7º] overestimated, while [7º, 12º] and [7º, 15º] underestimated K(trans) and v(p), significantly (P<0.01). [2º, 15º] achieved the smallest but still statistically significant overestimation for K(trans) and v(p) in primary tumors, 32.1% and 16.2% respectively. k(ep) fitting results by DFAs were relatively close to the MFA reference compared to K(trans) and v(p).

CONCLUSIONS

T1 deviations induced by DFA could result in significant errors in kinetic parameter estimation, particularly K(trans) and v(p), through Tofts model fitting. MFA method should be more reliable and robust for accurate quantitative pharmacokinetic analysis in head and neck.

Texture analysis in assessment and prediction of chemotherapy response in breast cancer

PURPOSE

To assess the efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients.

MATERIALS AND METHODS

In all, 100 patients were scanned on a 3.0T HDx scanner immediately prior to neoadjuvant chemotherapy treatment. A software application to use texture features based on co-occurrence matrices was developed. Texture analysis was performed on precontrast and 1-5 minutes postcontrast data. Patients were categorized according to their chemotherapeutic response: partial responders corresponding to a decrease in tumor diameter over 50% (40) and nonresponders corresponding to a decrease of less than 50% (4). Data were also split based on factors that influence response: triple receptor negative phenotype (TNBC) (22) vs. non-TNBC (49); node negative (45) vs. node positive (46); and biopsy grade 1 or 2 (38) vs. biopsy grade 3 (55).

RESULTS

Parameters f2 (contrast), f4 (variance), f10 (difference in variance), f6 (sum average), f7 (sum variance), f8 (sum entropy), f15 (cluster shade), and f16 (cluster prominence) showed significant differences between responders and partial responders of chemotherapy. Differences were mainly seen at 1-3 minutes postcontrast administration. No significant differences were found precontrast administration. Node +ve, high grade, and TNBC are associated with poorer prognosis and appear to be more heterogeneous in appearance according to texture analysis.

CONCLUSION

This work highlights that textural differences between groups (based on response, nodal status, and triple negative groupings) are apparent and appear to be most evident 1-3 minutes postcontrast administration. The fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging.

Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas

BACKGROUND AND PURPOSE

The accuracy of tumor plasma volume and K(trans) estimates obtained with DCE MR imaging may have inaccuracies introduced by a poor estimation of the VIF. In this study, we evaluated the diagnostic accuracy of a novel technique by using a phase-derived VIF and "bookend" T1 measurements in the preoperative grading of patients with suspected gliomas.

MATERIALS AND METHODS

This prospective study included 46 patients with a new pathologically confirmed diagnosis of glioma. Both magnitude and phase images were acquired during DCE MR imaging for estimates of K(trans)_φ and V(p_)φ (calculated from a phase-derived VIF and bookend T1 measurements) as well as K(trans)_SI and V(p_)SI (calculated from a magnitude-derived VIF without T1 measurements).

RESULTS

Median K(trans)_φ values were 0.0041 minutes(-1) (95 CI, 0.00062-0.033), 0.031 minutes(-1) (0.011-0.150), and 0.088 minutes(-1) (0.069-0.110) for grade II, III, and IV gliomas, respectively (P ≤ .05 for each). Median V(p_)φ values were 0.64 mL/100 g (0.06-1.40), 0.98 mL/100 g (0.34-2.20), and 2.16 mL/100 g (1.8-3.1) with P = .15 between grade II and III gliomas and P = .015 between grade III and IV gliomas. In differentiating low-grade from high-grade gliomas, AUCs for K(trans)_φ, V(p_φ), K(trans)_SI, and V(p_)SI were 0.87 (0.73-1), 0.84 (0.69-0.98), 0.81 (0.59-1), and 0.84 (0.66-0.91). The differences between the AUCs were not statistically significant.

CONCLUSIONS

K(trans)_φ and V(p_)φ are parameters that can help in differentiating low-grade from high-grade gliomas.

Heuristic linear mapping of physiological parameters in dynamic contrast-enhanced MRI without T₁ measurement and contrast agent concentration

PURPOSE

To present a novel heuristic linear mapping method to individually estimate physiological parameters for Tofts model without T(1) measurement and contrast agent concentration.

MATERIALS AND METHODS

A linear relationship was used for k(ep) mapping through a heuristic time intensity curve (TIC) shape factor (TSF). K(trans) maps were subsequently estimated using k(ep) maps and another approximate linear model derived from the Tofts model. Twenty-seven patients with head-and-neck squamous cell carcinoma received dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Physiological parameters maps were obtained using this heuristic linear mapping method and compared to the maps obtained by the normal nonlinear least-square fitting with T(1) measurement.

RESULTS

High linearity (R(2) >0.95) between k(ep) and TSF was found in all patients for k(ep) <5/min. This linearity is robust for TSF timepoint selection. The k(ep) maps generated by this linear fitting were highly consistent with those by the normal nonlinear approach (P > 0.05). The K(trans) maps were consistent with the normally derived maps in pattern distribution but the absolute value might be scaled due to the assumption of the reference K(trans) value.

CONCLUSION

This novel method generates reliable and consistent physiological parameter maps with significantly lower computation complexity than the multiparameter nonlinear fitting. The DCE-MRI scan time can be greatly shortened without T(1) mapping.

Supine breast MRI

PURPOSE

To achieve high-quality unilateral supine breast magnetic resonance imaging (MRI) as a step to facilitate image aiding of clinical applications, which are often performed in the supine position. Contrast-enhanced breast MRI is a powerful tool for the diagnosis of cancer. However, prone patient positioning typically used for breast MRI hinders its use for image aiding.

MATERIALS AND METHODS

A fixture and a flexible four-element receive coil were designed for patient-specific shaping and placement of the coil in close conformity to the supine breast. A 3D spoiled gradient sequence was modified to incorporate compensation of respiratory motion. The entire setup was tested in volunteer experiments and in a pilot patient study.

RESULTS

The flexible coil design and the motion compensation produced supine breast MR images of high diagnostic value. Variations in breast shape and in tissue morphology within the breast were observed between a supine and a diagnostic prone MRI of a patient.

CONCLUSION

The presented supine breast MRI achieved an image quality comparable to diagnostic breast MRI. Since supine positioning is common in many clinical applications such as ultrasound-guided breast biopsy or breast-conserving surgery, the registration of the supine images will aid these applications.

Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer

Dynamic contrast-enhanced MRI (DCE-MRI) is an evolving tool for determining breast disease, which benefits from the move to imaging at 3 T. It has major capabilities for the diagnosis, detection and monitoring of malignancy. It benefits from being non-invasive and three-dimensional, allowing visualisation of the extent of disease and its angiogenic properties, visualisation of lesion heterogeneity, detection of changes in angiogenic properties before morphological alterations, and the potential to predict the overall response either before the start of therapy or early during treatment. In addition, DCE-MRI is emerging as a powerful tool for screening high-risk patients and for detecting high-grade ductal carcinoma in situ. However, there are also a number of limitations, including the overlap in enhancement patterns between malignant and benign disease, the failure to resolve microscopic disease particularly in the neoadjuvant setting, and the inconsistent predictive value of the enhancement pattern for clinical outcome. Careful consideration should be given to the technical requirements of individual examinations and the need for automation of post-processing techniques to appropriately handle the growing volume of data acquired. Research continues, focusing on the use of higher field strengths with improved spatial and temporal resolution data, improving understanding of the mechanism of contrast enhancement at the cellular level, and developing macromolecular and targeted contrast agents.

DCEMRI of breast lesions: is kinetic analysis equally effective for both mass and nonmass-like enhancement?

To perform a pilot study investigating whether the sensitivity and specificity of kinetic parameters can be improved by considering mass and nonmass breast lesions separately. The contrast media uptake and washout kinetics in benign and malignant breast lesions were analyzed using an empirical mathematical model (EMM), and model parameters were compared in lesions with mass-like and nonmass-like enhancement characteristics. 34 benign and 78 malignant breast lesions were selected for review. Dynamic MR protocol: 1 pre and 5 postcontrast images acquired in the coronal plane using a 3D T1-weighted SPGR with 68 s timing resolution. An experienced radiologist classified the type of enhancement as mass, nonmass, or focus, according to the BI-RADS lexicon. The kinetic curve obtained from a radiologist-drawn region within the lesion was analyzed quantitatively using a three parameter EMM. Several kinetic parameters were then derived from the EMM parameters: the initial slope (Slope(ini)), curvature at the peak (kappa(peak)), time to peak (T(peak)), initial area under the curve at 30 s (iAUC30), and the signal enhancement ratio (SER). The BI-RADS classification of the lesions yielded: 70 mass lesions, 38 nonmass, 4 focus. For mass lesions, the contrast uptake rate (alpha), contrast washout rate (beta), iAUC30, SER, Slope(ini), T(peak) and kappa(peak) differed substantially between benign and malignant lesions, and after correcting for multiple tests of significance SER and T(peak) demonstrated significance (p < 0.007). For nonmass lesions, we did not find statistically significant differences in any of the parameters for benign vs. malignant lesions (p > 0.5). Kinetic parameters could distinguish benign and malignant mass lesions effectively, but were not quite as useful in discriminating benign from malignant nonmass lesions. If the results of this pilot study are validated in a larger trial, we expect that to maximize diagnostic utility, it will be better to classify lesion morphology as mass or nonmass-like enhancement prior to kinetic analysis.

Differentiation between benign and malignant breast lesions detected by bilateral dynamic contrast-enhanced MRI: a sensitivity and specificity study

The purpose of this study was to apply an empirical mathematical model (EMM) to kinetic data acquired under a clinical protocol to determine if the sensitivity and specificity can be improved compared with qualitative BI-RADS descriptors of kinetics. 3D DCE-MRI data from 100 patients with 34 benign and 79 malignant lesions were selected for review under an Institutional Review Board (IRB)-approved protocol. The sensitivity and specificity of the delayed phase classification were 91% and 18%, respectively. The EMM was able to accurately fit these curves. There was a statistically significant difference between benign and malignant lesions for several model parameters: the uptake rate, initial slope, signal enhancement ratio, and curvature at the peak enhancement (at most P=0.04). These results demonstrated that EMM analysis provided at least the diagnostic accuracy of the kinetic classifiers described in the BI-RADS lexicon, and offered a few key advantages. It can be used to standardize data from institutions with different dynamic protocols and can provide a more objective classification with continuous variables so that thresholds can be set to achieve desired sensitivity and specificity. This suggests that the EMM may be useful for analysis of routine clinical data.

Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples

Dynamic contrast enhanced MRI (DCE-MRI) enables the quantitative assessment of tumor status and has found application in both pre-clinical tumor models as well as clinical oncology. DCE-MRI requires the serial acquisition of images before and after the injection of a paramagnetic contrast agent so that the variation of MR signal intensity with time can be recorded for each image voxel. As the agent enters into a tissue, it changes the MR signal intensity from the tissue to a degree that depends on the local concentration. After the agent is transported out of the tissue, the MR signal intensity returns to its' baseline value. By analyzing the associated signal intensity time course using an appropriate mathematical model, physiological parameters related to blood flow, vessel permeability, and tissue volume fractions can be extracted for each voxel or region of interest.In this review we first discuss the basic physics of this methodology, and then present technical aspects of how DCE-MRI data are acquired and analyzed. We also discuss appropriate models of contrast agent kinetics and how these can be used to elucidate tissue characteristics of importance in cancer biology. We conclude by briefly summarizing some future goals and demands of DCE-MRI.

Bayesian estimation of pharmacokinetic parameters for DCE-MRI with a robust treatment of enhancement onset time

When applying pharmacokinetic (PK) models to dynamic contrast enhanced MRI (DCE-MRI) data it is important to appropriately deal with the enhancement onset time, because errors in the onset time will affect the PK parameter estimates. This paper presents a Bayesian approach to the estimation of the PK parameters k(ep) and K(trans) that robustly treats the onset time. This approach involves the computation of an analytically intractable integral, so two approximate methods are developed. The first uses adaptive numerical quadrature, which produces results accurate to a given tolerance, and the other a simple approximation with a summation. These approaches are compared with each other, and with the standard least-squares (LS) approach. The results of a Monte Carlo experiment show that the LS approach produces biased estimates when k(ep) is large and K(trans) is small, whereas both the Bayesian methods are unbiased. The two Bayesian methods produce very similar estimates, but the simple summation method requires less than half the computation time of either the LS, or the quadrature approximation. The standard deviation of the LS estimates is shown to be larger than either of the Bayesian estimates, while uncertainty estimates based around a Hessian approximation are shown to be too small for all three methods. A more detailed method of assessing the uncertainty of the Bayesian approach is described, and the results show that this is a more accurate description of the estimation uncertainty.

Comparison of a reference region model with direct measurement of an AIF in the analysis of DCE-MRI data

Models have been developed for analyzing dynamic contrast-enhanced (DCE)-MRI data that do not require measurements of the arterial input function (AIF). In this study, experimental results obtained from a reference region (RR) analysis are compared with results of an AIF analysis in the same set of five animals (four imaged twice, yielding nine data sets), returning estimates of the volume transfer constant (Ktrans) and the extravascular extracellular volume fraction (ve). Student's t-test values for comparisons of Ktrans and ve between the two models were 0.14 (P=0.88) and 0.85 (P>0.4), respectively (where the high P-values indicate no significant difference between values derived from the two models). Linear regression analysis indicated there was a correlation between Ktrans extracted by the two methods: r2=0.80, P=0.001 (where the low P-value indicates a significant linear correlation). For ve there was no such correlation (r2=0.02). The mean (absolute) percent difference between the models was 22.0% for Ktrans and 28.1% for ve. However, the RR parameter values were much less precise than the AIF method. The mean SDs for Ktrans and ve for the RR analysis were 0.024 min-1 and 0.06, respectively, vs. 0.002 min-1 and 0.03 for AIF analysis.

Reproducibility of reference tissue quantification of dynamic contrast-enhanced data: comparison with a fixed vascular input function

Reference tissues are currently used to analyse dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data. The assessment of tumour response to treatment with anti-cancer drugs is a particularly important application of this type of analysis and requires a measure of reproducibility to define a level above which a significant change due to therapy can be inferred. This study compares the reproducibility of such quantification strategies with that found using a published, group-averaged uptake curve. It is shown that reference tissue quantification gives poorer reproducibility for most parameters than that found using a group-averaged plasma curve (a change in K(trans) of greater than 41.8% and 16.4% would be considered significant in the two approaches, respectively), but successfully incorporates some of the variability observed in plasma kinetics between visits and provides vascular input functions that, across the group, are comparable with the group-averaged curve. This study therefore provides an indirect validation of the methodology.

Quantitative diffusion imaging in breast cancer: a clinical prospective study

PURPOSE

To study the correlation between apparent diffusion coefficient (ADC) and pathology in patients with undefined breast lesion, to validate how accurately ADC is related to histology, and to define a threshold value of ADC to distinguish malignant from benign lesions.

MATERIALS AND METHODS

Seventy-eight patients (110 lesions) were referred for positive or dubious findings. Three-dimensional fast low-angle shot (3D-FLASH) with contrast injection was applied. EPI diffusion-weighted imaging (DWI) with fat saturation was performed, and ROIs were selected on subtraction 3D-FLASH images before and after contrast injection, and copied on an ADC map. Inter- and intraobserver analyses were performed.

RESULTS

At pathology 22 lesions were benign, 65 were malignant, and 23 were excluded. The ADCs of malignant and benign lesions were statistically different. In malignant tumors the ADC was (mean +/- SEM) 0.95 +/- 0.027 x 10(-3)mm(2)/second, and in benign tumors it was 1.51 +/- 0.068 x 10(-3)mm(2)/second. According to receiver operating characteristic (ROC) curves, we found a threshold between malignant and benign lesions for highest sensitivity and specificity (both 86%) around 1.13 +/- 0.10 x 10(-3)mm(2)/second. For a threshold of 0.95 +/- 0.10 x 10(-3)mm(2)/second, specificity was 100% but sensitivity was very low. Inter- and intraobserver studies showed good reproducibility.

CONCLUSION

The ADC may help to differentiate benign and malignant lesions with good specificity, and may increase the overall specificity of breast MRI.

On the Selection of Optimal Flip Angles for$T_1$Mapping of Breast Tumors With Dynamic Contrast-Enhanced Magnetic Resonance Imaging

We present a method for selecting optimal flip angles for both precontrast and postcontrast T1 mapping of breast tumors using dynamic contrast-enhanced magnetic resonance imaging; and with the aim of improving accuracy in the estimation of contrast medium concentration. The proposed method can appropriately account for the different ranges of precontrst and postcontrast T1 values by the use of weighting functions, which also allow the flexibility to enhance the accuracy of certain T1 values, corresponding to the tissues of interest. Results of Monte Carlo simulations show that the proposed method could yield significantly lower errors in the estimation of contrast concentration, as compared with an existing approach.