Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

The aim of this work was to evaluate the clinical feasibility of diffusion-weighted (DW) MRI in detection and staging of urinary bladder tumour and to compare DW MRI with the T(2)-weighted technique. One hundred and six patients with bladder tumour were prospectively included in our study. All patients were evaluated with MR imaging. We started with axial T(2)-weighted high resolution MR of the urinary bladder, then DW MRI. Two radiologists independently interpreted the MR images, and discrepancies were resolved by consensus. The accuracy of DW MRI in staging of bladder tumour was evaluated using the final histopathological findings. In DW imaging (DWI) staging accuracy was 63.6% and 69.6% in differentiating superficial from invasive tumours and organ-confined from non-organ-confined tumours, respectively. On a stage by a stage basis, DWI accuracy was 63.6% (21/33), 75.7% (25/33), 93.7% (30/32) and 87.5% (7/8) for stages T1, T2, T3 and T4, respectively. In the T(2)-weighted technique, the overall staging accuracy was only 39.6% and accuracy for differentiating superficial from invasive tumours and organ-confined from non-organ-confined tumours was 6.1% and 15.1%, respectively. DW is superior to T(2)-weighted MRI in staging of organ-confined tumours (< or =T2) and both techniques are comparable in the evaluation of higher-stage tumours.

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 accurately segment pathological and healthy lungs for reliable computer-aided disease diagnostics, a stack of chest CT scans is modeled as a sample of a spatially inhomogeneous joint 3D Markov-Gibbs random field (MGRF) of voxel-wise lung and chest CT image signals (intensities). The proposed learnable MGRF integrates two visual appearance sub-models with an adaptive lung shape submodel. The first-order appearance submodel accounts for both the original CT image and its Gaussian scale space (GSS) filtered version to specify local and global signal properties, respectively. Each empirical marginal probability distribution of signals is closely approximated with a linear combination of discrete Gaussians (LCDG), containing two positive dominant and multiple sign-alternate subordinate DGs. The approximation is separated into two LCDGs to describe individually the lungs and their background, i.e., all other chest tissues. The second-order appearance submodel quantifies conditional pairwise intensity dependencies in the nearest voxel 26-neighborhood in both the original and GSS-filtered images. The shape submodel is built for a set of training data and is adapted during segmentation using both the lung and chest appearances. The accuracy of the proposed segmentation framework is quantitatively assessed using two public databases (ISBI VESSEL12 challenge and MICCAI LOLA11 challenge) and our own database with, respectively, 20, 55, and 30 CT images of various lung pathologies acquired with different scanners and protocols. Quantitative assessment of our framework in terms of Dice similarity coefficients, 95-percentile bidirectional Hausdorff distances, and percentage volume differences confirms the high accuracy of our model on both our database (98.4%, 2.2 mm, 0.42%) and the VESSEL12 database (99.0%, 2.1 mm, 0.39%), respectively. Similarly, the accuracy of our approach is further verified via a blind evaluation by the organizers of the LOLA11 competition, where an average overlap of 98.0% with the expert's segmentation is yielded on all 55 subjects with our framework being ranked first among all the state-of-the-art techniques compared.

DW MR imaging is a highly reliable imaging approach for identification of bladder tumors in patients with gross hematuria.

Objective: The aim was to evaluate the effects of diagnostic performance of diffusionweighted (DW) MRI in the assessment of acute impairment of transplanted kidneys. Methods: From January 2009 to January 2010, 49 patients with stable renal allograft function (Group 1) and 21 patients with acute graft impairment (Group 2) were included in the study. All patients were evaluated with coronal T 2 weighted (T 2 W) and DW MRI of the kidney. Patients in Group 2 underwent graft biopsy to determine the underlying histopathological aetiology. Apparent diffusion coefficient (ADC) was calculated and the kidneys were studied for any areas of diffusion restriction. Two radiologists, who were blinded to the results of histopathology, independently interpreted the T 2 W and DW images. Results: The histopathological diagnosis ofGroup 2 (21 patients) was acute cellular rejection (ACR) in 10, acute tubular necrosis (ATN) in 7 and immunosuppressive toxicity in 4 patients. ADC values in Group 1 were significantly higher compared with Group 2 (p,0.001), patients with ACR (p,0.001), patients with ATN (p,0.001) and patients with drug toxicity (p,0.001). Using 2610 23 mm 2 s 21 as a cut-off, there was no overlap between the ADC values of patients with normal graft function and those with ATN. Both ACR and ATN had a low ADC value, but on the ADC map the kidney in cases of ATN appears heterogeneous with a characteristic mosaic pattern resembling the Tiger skin. There was no significant T 2 W morphological difference between the two groups. Conclusion: These results show how DW MRI is a promising new technique for the diagnosis of acute renal transplant dysfunction.

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.

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.