Dominic Kennedy

Association of imaging markers of myocardial fibrosis with metabolic and functional disturbances in early diabetic cardiomyopathy

Background— Metabolic and vascular disturbances contribute to diabetic cardiomyopathy, but the role of interstitial fibrosis in early disease is unproven. We sought to assess the relationship between imaging markers of diffuse fibrosis and myocardial dysfunction and to link this to possible causes of early diabetic cardiomyopathy. Methods and Results— Hemodynamic and metabolic data were measured in 67 subjects with type 2 diabetes mellitus (age 60±10 years) with no cardiac symptoms. Myocardial function was evaluated with standard echocardiography and myocardial deformation; ischemia was excluded by exercise echocardiography. Calibrated integrated backscatter was calculated from parasternal long-axis views. T1 mapping was performed after contrast with a modified Look-Locker technique using saturation recovery images. Amino-terminal propeptides of procollagens type I and III, as well as the carboxy-terminal propeptide of procollagen type I, were assayed to determine collagen turnover. Subjects with abnormal early diastolic tissue velocity (Em) had shorter postcontrast T1 values (P=0.042) and higher calibrated integrated backscatter (P=0.007). They were heavier (P=0.003) and had worse exercise capacity (P<0.001), lower insulin sensitivity (P=0.003), and blunted systolic tissue velocity (P=0.05). Postcontrast T1 was associated with diastolic dysfunction (Em r=0.28, P=0.020; E/Em r=−0.24, P=0.049), impaired exercise capacity (r=0.30, P=0.016), central adiposity (r=−0.26, P=0.046), blood pressure (systolic r=−0.30, P=0.012; diastolic r=−0.49, P<0.001), and insulin sensitivity (r=0.30, P=0.037). The association of T1 with E/Em (&bgr;=−0.31, P=0.017) was independent of blood pressure and metabolic disturbance. Amino-terminal propeptide of procollagens type III was linked to diastolic dysfunction (Em r=−0.32, P=0.008) and calibrated integrated backscatter (r=0.30, P=0.015) but not T1 values. Conclusions— The association between myocardial diastolic dysfunction, postcontrast T1 values, and metabolic disturbance supports that diffuse myocardial fibrosis is an underlying contributor to early diabetic cardiomyopathy.

Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the ¿ = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the ¿ = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.

Fully Automatic Lesion Segmentation in Breast MRI Using Mean-Shift and Graph-Cuts on a Region Adjacency Graph

To present and evaluate a fully automatic method for segmentation (i.e., detection and delineation) of suspicious tissue in breast MRI.

New Spatiotemporal Features for Improved Discrimination of Benign and Malignant Lesions in Dynamic Contrast-Enhanced-Magnetic Resonance Imaging of the Breast

Objectives: The objective of this study was to measure the efficacy of 7 new spatiotemporal features for discriminating between benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging (MRI) of the breast. Methods: A total of 48 breast lesions from 39 patients were used: 25 malignant and 23 benign. Lesions were acquired using 1.5-T MRI machines in 3 different protocols. Two experiments were performed: (i) selection of the most discriminatory subset of features drawn from the new features and features from the literature and (ii) validation of classification performance of the selected subset of features. Results: Results of the feature selection experiment show that the subset comprising 2 of the new features is the most useful for automatic classification of suspicious lesions in the breast: (i) gradient correlation of maximum intensity and (ii) mean wash-in rate. Results of the validation experiment show that using these 2 features, unseen data can be classified with an area under the receiver operating characteristic curve of 0.91 ± 0.06. Conclusions: Results of the experiments suggest that suspicious lesions in dynamic contrast-enhanced-MRI of the breast can be classified, with high accuracy, using only 2 of the proposed spatiotemporal features. The selected features indicate heterogeneity of enhancement and speed of enhancement in a tissue. High values of these indicators are likely to be correlated with malignancy.

Dynamic breast MRI: Image registration and its impact on enhancement curve estimation

A novel algorithm for performing registration of dynamic contrast-enhanced (DCE) MRI data of the breast is presented. It is based on an algorithm known as iterated dynamic programming originally devised to solve the stereo matching problem. Using artificially distorted DCE-MRI breast images it is shown that the proposed algorithm is able to correct for movement and distortions over a larger range than is likely to occur during routine clinical examination. In addition, using a clinical DCE-MRI data set with an expertly labeled suspicious region, it is shown that the proposed algorithm significantly reduces the variability of the enhancement curves at the pixel level yielding more pronounced uptake and washout phases

A new diagnostic approach to popliteal artery entrapment syndrome

A new method of diagnosing and defining functional popliteal artery entrapment syndrome is described. By combining ultrasonography and magnetic resonance imaging techniques with dynamic plantarflexion of the ankle against resistance, functional entrapment can be demonstrated and the location of the arterial occlusion identified. This combination of imaging modalities will also define muscular anatomy for guiding intervention such as surgery or Botox injection.

Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast

The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fishers linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).

Two Non-linear Parametric Models of Contrast Enhancement for DCE-MRI of the Breast Amenable to Fitting Using Linear Least Squares

This paper proffers two non-linear empirical parametric models—linear slope and Ricker—for use in characterising contrast enhancement in dynamic contrast enhanced (DCE) MRI. The advantage of these models over existing empirical parametric and pharmacokinetic models is that they can be fitted using linear least squares (LS). This means that fitting is quick, there is no need to specify initial parameter estimates, and there are no convergence issues. Furthermore the LS fit can itself be used to provide initial parameter estimates for a subsequent NLS fit (self-starting models). The results of an empirical evaluation of the goodness of fit (GoF) of these two models, measured in terms of both MSE and R^2, relative to a two-compartment pharmacokinetic model and the Hayton model are also presented. The GoF was evaluated using both routine clinical breast MRI data and a single high temporal resolution breast MRI data set. The results demonstrate that the linear slope model fits the routine clinical data better than any of the other models and that the two parameter self-starting Ricker model fits the data nearly as well as the three parameter Hayton model. This is also demonstrated by the results for the high temporal data and for several temporally sub-sampled versions of this data.

Improving the Discrimination of Benign and Malignant Breast MRI Lesions Using the Apparent Diffusion Coefficient

This paper presents an investigation of the apparent diffusion coefficient (ADC) for improving the discrimination of benign and malignant lesions in breast magnetic resonance imaging (MRI). In particular a method is presented for automatically selecting hyper intense tumour voxels in dynamic contrast enhanced (DCE) MRI data and evaluating their average ADC in the corresponding diffusion-weighted (DW) MRI data. The method was applied to ten breast MRI datasets obtained from routine clinical practice. The results demonstrate that the combination of the relative signal increase (DCE-MRI) with the apparent diffusion coefficient (DW-MRI) leads to better discrimination than with either feature alone. The results also suggest that it is important to acquire the DWMRI data in a consistent fashion, i.e. either before or after the acquisition of the DCE-MRI data.

Practical MR mammography – high‐resolution MRI of the breast Fischer U. Thieme, New York, 2012, 300 pp, 1351 figures, ISBN: 978‐3‐13‐132032‐2, Price: ˜A

Practical MR Mammography is the second edition of this book – the first was published 8 years previously. There have been considerable changes in breast magnetic resonance imaging (MRI) over this time, and these changes are reflected in this new edition, with completely rewritten chapters including technique, methodology, lesion analysis, biopsy, quality assurance, and high-quality images. Uwe Fischer is obviously passionate about MR mammography which is reflected in this practical and comprehensive book. The contents of the book are logically and sequentially ordered from patient preparation to imaging to diagnosing the lesions. His summary of the technique and methods used to image the breast is comprehensive. Each section, such as imaging during a menstrual cycle, patient positioning, sequences, and contrast agents, all contain clear and concise explanations. The spatial resolution one should expect with MR mammography (2013 – 0.6 to 1.2 mm slice thickness) is greater than that stated in this book, and an explanation of spatial resolution in terms of pixel size would be better as it takes into account the slice thickness, field of view, and image matrix. In terms of MR imaging approaches, it would be helpful to have other imaging approaches detailed, that is, the dynamic “interview” approach using an ultra–high-resolution sagittal scan interspersed between axial dynamic scans. A limited explanation of imaging the breast with 3T is evident and would be a useful addition in any future editions. Extremely useful are the “exclamation points” in each section throughout the book providing the reader with “take-home” (important) messages. Assessment of each of the MR pulse sequences (T1, T2) is provided and diagnostic criteria is used to help the reader interpret the images and structures within the breast in a logical and clear manner. Explanations throughout the text are accompanied by high-quality images to explain and illustrate the points being made. An excellent explanation of the contrast enhancement patterns of lesions, based on the ACR BI-RADS-MRI Lexicon, is approached and explained systematically. Artefacts seen in breast MR are explained and reasons for their appearance are given, which will help the reader understand problems that can occur when imaging the breast and how to correct for them. Easily accessible sections describing normal findings in breast MR, benign changes, postoperative/posttraumatic changes, borderline lesions, intraductal carcinomas, malignant changes, lymph nodes, and breast prosthesis are examined in the same clear, methodological manner. A brief explanation of the lesion, MR characteristics, image examples together with dynamic curve analysis are provided for each lesion. Reference tables are provided which explain the clinical, mammographic, and sonographic appearance of these lesions to go with the MR explanations. One of the most common uses of breast MR is as a problem solver in ambiguous findings. Other indications such as differentiation between scar and local recurrence after breast conserving therapy; the search for cancers of unknown origin; preoperative local staging; and monitoring for neoadjuvant chemotherapy are detailed, accompanied by the explanation as to why MRI is the preferred imaging solution in these situations. MR-guided biopsy and localization is detailed with a step-by-step checklist to follow when performing these procedures. A brief explanation of the different biopsy coils and the different targeting techniques is provided. However, a more comprehensive explanation of the different targeting techniques, biopsy coils, vacuum biopsy equipment, and the advantages and disadvantages of each would be useful in assisting imaging staff starting interventions to be more confident in choosing the right equipment. The section on differential diagnosis and strategy gives pertinent advice on commonly encountered situations, such as solitary focus, multiple foci, non–mass-like lesions, and outlines strategies for management of these lesions. Of particular relevance is the explanation of when to recommend biopsy and follow-up interval strategies for a variety of lesion types. These strategies are detailed in clear, easy-to-follow tables. Examples of written reports for breast MR are provided to help breast imagers beginning in this dynamic field. This book would be an excellent addition as a resource for both imaging technologists and radiologists. As a practical atlas for radiologists to refer to for image examples of certain pathologies and guidance for differential diagnosis, it would be a beneficial investment.