Purpose: To present a review of most commonly used techniques to analyze dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.Methods: DCE‐MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well‐established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion‐related parameters (indexes) from signal‐ or concentration–time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.Results: Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE‐MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.Conclusions: Both nonparametric and parametric approaches for DCE‐MRI analysis possess the ability to quantify tissue perfusion.

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

To define prognostic factors that affect the success rate after extracorporeal shock-wave lithotripsy (ESWL) of renal calculi and to estimate the probability of stone-free status using a regression analysis model. Between February 1992 and February 2002, 2954 patients with single or multiple radiopaque renal stones (<30 mm) underwent ESWL monotherapy. The results of treatment were evaluated after 3 months of follow-up. Treatment success was defined as complete clearance of the stones with no residual fragments. The stone-free rate was correlated with stone features and patient characteristics using the chi2 test. Factors found to be significant using the chi2 test were further analyzed using multivariate regression analysis. At 3-month follow-up, the overall stone-free rate using ESWL monotherapy was 86.7%. Failure to disintegrate the stones was observed in 7.3% of cases (n = 216) and failure to clear the fragmented stones occurred in 6% (n = 177). Repeat ESWL was needed in 53% of cases. Static steinstrasse occurred in 4.9% of cases (n = 146) and post-ESWL auxiliary procedures were required in 4% (n = 118). Using the chi2 test, patient age (p < 0.001), stone size (p < 0.001), location (p < 0.001), number (p < 0.001) and nature (p = 0.003), radiological renal picture (p < 0.001) and congenital renal anomalies (p < 0.001) had a significant impact on the stone-free rate. Multivariate analysis excluded stone nature from the logistic regression model while other factors maintained their statistically significant effect on success rate, indicating that they were independent predictors. A regression analysis model was designed to estimate the probability of stone-free status after ESWL. The sensitivity of the model was 83%, the specificity 91% and the overall accuracy 87%. Patient age, stone size, location and number, radiological renal features and congenital renal anomalies are prognostic factors determining stone clearance after ESWL of renal calculi. Our regression model can predict the probability of the success of ESWL with an accuracy of 87%.

Accurate automatic extraction of a 3-D cerebrovascular system from images obtained by time-of-flight (TOF) or phase contrast (PC) magnetic resonance angiography (MRA) is a challenging segmentation problem due to the small size objects of interest (blood vessels) in each 2-D MRA slice and complex surrounding anatomical structures (e.g., fat, bones, or gray and white brain matter). We show that due to the multimodal nature of MRA data, blood vessels can be accurately separated from the background in each slice using a voxel-wise classification based on precisely identified probability models of voxel intensities. To identify the models, an empirical marginal probability distribution of intensities is closely approximated with a linear combination of discrete Gaussians (LCDG) with alternate signs, using our previous EM-based techniques for precise linear combination of Gaussian-approximation adapted to deal with the LCDGs. The high accuracy of the proposed approach is experimentally validated on 85 real MRA datasets (50 TOF and 35 PC) as well as on synthetic MRA data for special 3-D geometrical phantoms of known shapes.

To prospectively evaluate the usefulness of diffusion-weighted (DW) magnetic resonance (MR) imaging for the detection of bladder neoplasms in patients with gross hematuria of lower urinary tract origin. The study protocol received institutional ethical committee approval, and informed consent was obtained. Between April 2007 and March 2008, 130 consecutive patients with gross hematuria whose upper urinary tract had a normal appearance at ultrasonographic examination were prospectively enrolled. Mean age was 59.4 years (range, 45-75 years). All patients were evaluated by using T2-weighted high-spatial-resolution MR imaging of the urinary bladder, followed by DW MR imaging. Two radiologists independently interpreted the T2-weighted and DW images, and discrepancies were resolved by consensus. Agreement was evaluated by using the kappa statistic. All patients underwent conventional cystoscopy. With cystoscopy and the final histopathologic findings as the reference standards, a comparison with imaging findings was performed by using the McNemar test. The consensus diagnostic performance of DW MR imaging for identification of bladder tumors was: sensitivity, 98.1% (104 of 106); specificity, 92.3% (24 of 26); PPV, 100% (104 of 104); negative predictive value, 92.3% (24 of 26); and accuracy, 97.0% (128 of 132). Two cases were falsely negative on T2-weighted MR images but were correctly diagnosed by using DW MR images. The agreement between DW MR imaging results and cystoscopic findings was excellent (kappa = 0.94) for identification of bladder neoplasm. DW MR imaging had a sensitivity and PPV of 98.5% (128 of 130) and 100% (128 of 128), respectively, for determining the cause of hematuria. DW MR imaging is a highly reliable imaging approach for identification of bladder tumors in patients with gross hematuria.