Improved diagnostic accuracy of breast MRI through combined apparent diffusion coefficients and dynamic contrast‐enhanced kinetics

This study investigated the relationship between apparent diffusion coefficient (ADC) measures and dynamic contrast‐enhanced magnetic resonance imaging (MRI) kinetics in breast lesions and evaluated the relative diagnostic value of each quantitative parameter. Seventy‐seven women with 100 breast lesions (27 malignant and 73 benign) underwent both dynamic contrast‐enhanced MRI and diffusion weighted MRI. Dynamic contrast‐enhanced MRI kinetic parameters included peak initial enhancement, predominant delayed kinetic curve type (persistent, plateau, or washout), and worst delayed kinetic curve type (washout > plateau > persistent). Associations between ADC and dynamic contrast‐enhanced MRI kinetic parameters and predictions of malignancy were evaluated. Results showed that ADC was significantly associated with predominant curve type (ADC was higher for lesions exhibiting predominantly persistent enhancement compared with those exhibiting predominantly washout or plateau, P = 0.006), but was not significantly associated with peak initial enhancement or worst curve type (P > 0.05). Univariate analysis showed significant differences between benign and malignant lesions in both ADC (P < 0.001) and worst curve (P = 0.003). In multivariate analysis, worst curve type and ADC were significant independent predictors of benign versus malignant outcome and in combination produced the highest area under the receiver operating characteristic curve (0.85 and 0.78 with 5‐fold cross validation). Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

Timing bolus dynamic contrast‐enhanced (DCE) MRI assessment of hepatic perfusion: Initial experience

Purpose

To assess whether dynamic contrast‐enhanced (DCE) MRI timing bolus data from routine clinical examinations can be postprocessed to obtain hepatic perfusion parameters for diagnosing cirrhosis.

Materials and Methods

We retrospectively identified 57 patients (22 with cirrhosis and 35 without cirrhosis) who underwent abdominal MRI, which included a low‐dose (2 mL gadodiamide) timing bolus using a volumetric spoiled gradient echo T1‐weighted sequence through the abdomen. Using a dual‐input single‐compartment model, the following perfusion parameters were measured: arterial, portal, and total blood flow; arterial fraction; mean transit time; and distribution volume. Those parameters were compared between patients with and without cirrhosis using t‐tests. Receiver operating characteristic (ROC) curve analysis was used to identify the perfusion parameters that can best predict the presence of cirrhosis.

Results

The hepatic arterial fraction, arterial flow, and distribution volume in patients with cirrhosis (27.7 ± 8.3%, 44.8 ± 14.1 mL/minute/100 g, and 16.3 ± 4.5%, respectively) were significantly higher than those without cirrhosis (18.7 ± 4.4%, 28.5 ± 11.7 mL/minute/100 g, and 14.0 ± 4.2%, respectively; P < 0.05 for all). ROC analysis showed arterial fraction as the best predictor of cirrhosis, with sensitivity of 73% and specificity of 86%.

Conclusion

Timing bolus DCE MR images from routine examinations can be postprocessed to yield potentially useful hepatic perfusion parameters. J. Magn. Reson. Imaging 2009;29:1317–1322. © 2009 Wiley‐Liss, Inc.     

Prostate cancer detection with 3 T MRI: Comparison of diffusion‐weighted imaging and dynamic contrast‐enhanced MRI in combination with T2‐weighted imaging

Purpose:

To evaluate the diagnostic ability of diffusion‐weighted imaging (DWI) and dynamic contrast‐enhanced imaging (DCEI) in combination with T2‐weighted imaging (T2WI) for the detection of prostate cancer using 3 T magnetic resonance imaging (MRI) with a phased‐array body coil.

Materials and Methods:

Fifty‐three patients with elevated serum levels of prostate‐specific antigen (PSA) were evaluated by T2WI, DWI, and DCEI prior to needle biopsy. The obtained data from T2WI alone (protocol A), a combination of T2WI and DWI (protocol B), a combination T2WI and DCEI (protocol C), and a combination of T2WI plus DWI and DCEI (protocol D) were subjected to receiver operating characteristic (ROC) curve analysis.

Results:

The sensitivity, specificity, accuracy, and area under the ROC curve (Az) for region‐based analysis were: 61%, 91%, 84%, and 0.8415, respectively, in protocol A; 76%, 94%, 90%, and 0.8931, respectively, in protocol B; 77%, 93%, 89%, and 0.8655, respectively, in protocol C; and 81%, 96%, 92%, and 0.8968, respectively in protocol D. ROC analysis revealed significant differences between protocols A and B (P = 0.0008) and between protocols A and D (P = 0.0004).

Conclusion:

In patients with elevated PSA levels the combination of T2WI, DWI, DCEI using 3 T MRI may be a reasonable approach for the detection of prostate cancer. J. Magn. Reson. Imaging 2010;31:625–631. © 2010 Wiley‐Liss, Inc.     

Uncertainty estimation in dynamic contrast‐enhanced MRI

Using dynamic contrast‐enhanced MRI (DCE‐MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE‐MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T₁ map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for K^trans, v_e, and v_p, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty. Magn Reson Med 69:992–1002, 2013. © 2012 Wiley Periodicals, Inc.     

Evaluation of the tibial tunnel after intraoperatively administered platelet‐rich plasma gel during anterior cruciate ligament reconstruction using diffusion weighted and dynamic contrast‐enhanced MRI

Purpose:

To evaluate effect of platelet‐rich plasma gel (PRPG), locally administered during the anterior cruciate ligament (ACL) reconstruction, with two MRI methods. The proximal tibial tunnel was assessed with diffusion weighted imaging (DWI) and with dynamic contrast‐enhanced imaging (DCE‐MRI).

Materials and Methods:

In 50 patients, standard arthroscopic ACL reconstructions were performed. The patients in the PRPG group (n = 25) received a local application of PRPG. The proximal tibial tunnel was examined by DWI and DCE‐MRI, which were used to calculate apparent diffusion coefficient (ADC) values, as well as the contrast enhancement gradient (G_enh) and enhancement factor (F_enh) values.

Results:

At 1 month, the calculated average ADC value in the PRPG group was significantly lower than in the control group. At 2.5 and at 6 months, G_enh was significantly higher in the PRPG group. There were no significant differences in F_enh between the groups at any control examination.

Conclusion:

DWI and DCE‐MRI measurements indicate a reduced extent of edema during the first postoperative month as well as an increased vascular density and microvessel permeability in the proximal tibial tunnel at 1 and 2.5 postoperative months as the effect of the application of PRPG. J. Magn. Reson. Imaging 2013;37:928–935. © 2012 Wiley Periodicals, Inc.     

Extended graphical model for analysis of dynamic contrast‐enhanced MRI

Kinetic analysis with mathematical models has become increasingly important to quantify physiological parameters in computed tomography (CT), positron emission tomography (PET), and dynamic contrast‐enhanced MRI (DCE‐MRI). The modified Kety/Tofts model and the graphical (Patlak) model have been widely applied to DCE‐MRI results in disease processes such as cancer, inflammation, and ischemia. In this article, an intermediate model between the modified Kety/Tofts and Patlak models is derived from a mathematical expansion of the modified Kety/Tofts model. Simulations and an in vivo experiment involving DCE‐MRI of carotid atherosclerosis were used to compare the new extended graphical model with the modified Kety/Tofts model and the Patlak model. In our simulated circumstances and the carotid artery application, we found that the extended graphical model exhibited lower noise sensitivity and provided more accurate estimates of the volume transfer constant (K^trans) and fractional plasma volume (v_p) than the modified Kety/Tofts model for DCE‐MRI acquisitions of total duration less than 100–300 s, depending on kinetic parameters. In comparison with the Patlak model, we found that the extended graphical model exhibited 74.4–99.8% less bias in estimates of K^trans. Thus, the extended graphical model may allow kinetic modeling of DCE‐MRI results with shortened data acquisition periods, without sacrificing accuracy in estimates of K^trans and v_p. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.     

Prostate cancer detection with multi‐parametric MRI: Logistic regression analysis of quantitative T2, diffusion‐weighted imaging,and dynamic contrast‐enhanced MRI

Purpose

To develop a multi‐parametric model suitable for prospectively identifying prostate cancer in peripheral zone (PZ) using magnetic resonance imaging (MRI).

Materials and Methods

Twenty‐five radical prostatectomy patients (median age, 63 years; range, 44–72 years) had T2‐weighted, diffusion‐weighted imaging (DWI), T2‐mapping, and dynamic contrast‐enhanced (DCE) MRI at 1.5 Tesla (T) with endorectal coil to yield parameters apparent diffusion coefficient (ADC), T2, volume transfer constant (K^trans) and extravascular extracellular volume fraction (v_e). Whole‐mount histology was generated from surgical specimens and PZ tumors delineated. Thirty‐eight tumor outlines, one per tumor, and pathologically normal PZ regions were transferred to MR images. Receiver operating characteristic (ROC) curves were generated using all identified normal and tumor voxels. Step‐wise logistic‐regression modeling was performed, testing changes in deviance for significance. Areas under the ROC curves (A_z) were used to evaluate and compare performance.

Results

The best‐performing single‐parameter was ADC (mean A_z [95% confidence interval]: A_z,ADC: 0.689 [0.675, 0.702]; A_z,T2: 0.673 [0.659, 0.687]; A_z,Ktrans: 0.592 [0.578, 0.606]; A_z,ve: 0.543 [0.528, 0.557]). The optimal multi‐parametric model, LR‐3p, consisted of combining ADC, T2 and K^trans. Mean A_z,LR‐3p was 0.706 [0.692, 0.719], which was significantly higher than A_z,T2, A_z,Ktrans, and A_z,ve (P < 0.002). A_z,LR‐3p tended to be greater than A_z,ADC, however, this result was not statistically significant (P = 0.090).

Conclusion

Using logistic regression, an objective model capable of mapping PZ tumor with reasonable performance can be constructed. J. Magn. Reson. Imaging 2009;30:327–334. © 2009 Wiley‐Liss, Inc.     

Multiple‐bolus dynamic contrast‐enhanced MRI in the pancreas during a glucose challenge

Purpose:

To assess the feasibility of multiple‐bolus dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) in the pancreas; to optimize the analysis; and to investigate application of the method to a glucose challenge in type 2 diabetes.

Materials and Methods:

A 4‐bolus DCE‐MRI protocol was performed on five patients with type 2 diabetes and 11 healthy volunteers during free‐breathing. Motion during the dynamic time series was corrected for using a model‐driven nonlinear registration. A glucose challenge was administered intravenously between the first and second DCE‐MRI acquisition in all patients and in seven of the healthy controls.

Results:

Image registration improved the reproducibility of the DCE‐MRI model parameters across the repeated bolus‐acquisitions in the healthy controls with no glucose challenge (eg, coefficient of variation for K^trans improved from 38% to 28%). Native tissue T₁ was significantly lower in patients (374 ± 68 msec) compared with volunteers (519 ± 41 msec) but there was no significant difference in any of the baseline DCE‐MRI parameters. No effect of glucose challenge was observed in either the patients or healthy volunteers.

Conclusion:

Multiple bolus DCE‐MRI is feasible in the pancreas and is improved by nonlinear image registration but is not sensitive to the effects of an intravenous glucose challenge. J. Magn. Reson. Imaging 2010;32:622–628. © 2010 Wiley‐Liss, Inc.     

Progression of experimental lesions of atherosclerosis: Assessment by kinetic modeling of black‐blood dynamic contrast‐enhanced MRI

Pharmacokinetic modeling of dynamic contrast‐enhanced (DCE) magnetic resonance imaging (MRI) is used to noninvasively characterize neovasculature and inflammation in atherosclerotic vessels by estimating perfusion characteristics, such as fractional plasma volume v_p and transfer constant K^trans. DCE‐MRI has potential to study the evolution of nascent lesions involving early pathological changes. However, currently used bright‐blood DCE‐MRI approaches are difficult to apply to small lesions because of the difficulty in separating the signal in the thin vessel wall from the adjacent lumen. By suppressing the lumen signal, black‐blood DCE‐MRI techniques potentially provide a better tool for early atherosclerotic lesion assessment. However, whether black‐blood DCE‐MRI can detect temporal changes in physiological kinetic parameters has not been investigated for atherosclerosis. This study of balloon‐injured New Zealand White rabbits used a reference‐region‐based pharmacokinetic model of black‐blood DCE‐MRI to evaluate temporal changes in early experimental atherosclerotic lesions of the abdominal aorta. Six rabbits were imaged at 3 and 6 months after injury. K^trans was found to increase from 0.10 ± 0.03 min^?1 to 0.14 ± 0.05 min^?1 (P = 0.01). In histological analysis of all twelve rabbits, K^trans showed a significant correlation with macrophage content (R = 0.70, P =0.01). These results suggest black‐blood DCE‐MRI and a reference‐region kinetic model could be used to study plaque development and therapeutic response in vivo. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.     

Motion compensated generalized reconstruction for free‐breathing dynamic contrast‐enhanced MRI

The analysis of abdominal and thoracic dynamic contrast‐enhanced MRI is often impaired by artifacts and misregistration caused by physiological motion. Breath‐hold is too short to cover long acquisitions. A novel multipurpose reconstruction technique, entitled dynamic contrast‐enhanced generalized reconstruction by inversion of coupled systems, is presented. It performs respiratory motion compensation in terms of both motion artefact correction and registration. It comprises motion modeling and contrast‐change modeling. The method feeds on physiological signals and x‐f space properties of dynamic series to invert a coupled system of linear equations. The unknowns solved for represent the parameters for a linear nonrigid motion model and the parameters for a linear contrast‐change model based on B‐splines. Performance is demonstrated on myocardial perfusion imaging, on six simulated data sets and six clinical exams. The main purpose consists in removing motion‐induced errors from time–intensity curves, thus improving curve analysis and postprocessing in general. This method alleviates postprocessing difficulties in dynamic contrast‐enhanced MRI and opens new possibilities for dynamic contrast‐enhanced MRI analysis. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc.     

Tumor response assessments with diffusion and perfusion MRI

There is an increasing awareness that the evaluation of tumor response to oncologic treatments based solely on anatomic imaging assessments face many limitations, particularly in this era of novel biologic targeted therapies. Functional imaging techniques such as diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have the ability to depict important tumor biologic features and are able to predict therapy response based on assessments of cellularity and tumor vascularity, which often precede morphologic alterations. In this article we focus on DW-MRI and DCE-MRI as response parameters addressing the technologies involved, quantification methods, and validation for each technique and their current role in imaging response to conventional and novel therapies. We also discuss the challenges that lie ahead in the deployment of these imaging methods into the clinical environment.