Matthew R. Orton

Intravoxel Incoherent Motion in Body Diffusion-Weighted MRI: Reality and Challenges

OBJECTIVE Diffusion-weighted MRI is increasingly applied in the body. It has been recognized for some time, on the basis of scientific experiments and studies in the brain, that the calculation of apparent diffusion coefficient by simple monoexponential relationship between MRI signal and b value does not fully account for tissue behavior. However, appreciation of this fact in body diffusion MRI is relatively new, because technologic advancements have only recently enabled high-quality body diffusion-weighted images to be acquired using multiple b values. There is now increasing interest in the radiologic community to apply more sophisticated analytic approaches, such as those based on the principles of intravoxel incoherent motion, which allows quantitative parameters that reflect tissue microcapillary perfusion and tissue diffusivity to be derived. CONCLUSION In this review, we discuss the principles of intravoxel incoherent motion as applied to body diffusion-weighted MRI. The evidence for the technique in measuring tissue perfusion is presented and the emerging clinical utility surveyed. The requisites and challenges of quantitative evaluation beyond simple monoexponential relationships are highlighted.

Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data

Medical image analysis remains a challenging application area for artificial intelligence. When applying machine learning, obtaining ground-truth labels for supervised learning is more difficult than in many more common applications of machine learning. This is especially so for datasets with abnormalities, as tissue types and the shapes of the organs in these datasets differ widely. However, organ detection in such an abnormal dataset may have many promising potential real-world applications, such as automatic diagnosis, automated radiotherapy planning, and medical image retrieval, where new multimodal medical images provide more information about the imaged tissues for diagnosis. Here, we test the application of deep learning methods to organ identification in magnetic resonance medical images, with visual and temporal hierarchical features learned to categorize object classes from an unlabeled multimodal DCE-MRI dataset so that only a weakly supervised training is required for a classifier. A probabilistic patch-based method was employed for multiple organ detection, with the features learned from the deep learning model. This shows the potential of the deep learning model for application to medical images, despite the difficulty of obtaining libraries of correctly labeled training datasets and despite the intrinsic abnormalities present in patient datasets.

Diffusion-weighted Imaging of the Liver with Multiple b Values: Effect of Diffusion Gradient Polarity and Breathing Acquisition on Image Quality and Intravoxel Incoherent Motion Parameters—A Pilot Study

PURPOSE To optimize intravoxel incoherent motion (IVIM) diffusion-weighted (DW) imaging by estimating the effects of diffusion gradient polarity and breathing acquisition scheme on image quality, signal-to-noise ratio (SNR), IVIM parameters, and parameter reproducibility, as well as to investigate the potential of IVIM in the detection of hepatic fibrosis. MATERIALS AND METHODS In this institutional review board-approved prospective study, 20 subjects (seven healthy volunteers, 13 patients with hepatitis C virus infection; 14 men, six women; mean age, 46 years) underwent IVIM DW imaging with four sequences: (a) respiratory-triggered (RT) bipolar (BP) sequence, (b) RT monopolar (MP) sequence, (c) free-breathing (FB) BP sequence, and (d) FB MP sequence. Image quality scores were assessed for all sequences. A biexponential analysis with the Bayesian method yielded true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF) in liver parenchyma. Mixed-model analysis of variance was used to compare image quality, SNR, IVIM parameters, and interexamination variability between the four sequences, as well as the ability to differentiate areas of liver fibrosis from normal liver tissue. RESULTS Image quality with RT sequences was superior to that with FB acquisitions (P = .02) and was not affected by gradient polarity. SNR did not vary significantly between sequences. IVIM parameter reproducibility was moderate to excellent for PF and D, while it was less reproducible for D*. PF and D were both significantly lower in patients with hepatitis C virus than in healthy volunteers with the RT BP sequence (PF = 13.5% ± 5.3 [standard deviation] vs 9.2% ± 2.5, P = .038; D = [1.16 ± 0.07] × 10(-3) mm(2)/sec vs [1.03 ± 0.1] × 10(-3) mm(2)/sec, P = .006). CONCLUSION The RT BP DW imaging sequence had the best results in terms of image quality, reproducibility, and ability to discriminate between healthy and fibrotic liver with biexponential fitting.

Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging.

AbstractMany therapeutic approaches to cancer affect the tumour vasculature, either indirectly or as a direct target. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important means of investigating this action, both pre-clinically and in early stage clinical trials. For such trials, it is essential that the measurement process (i.e. image acquisition and analysis) can be performed effectively and with consistency among contributing centres. As the technique continues to develop in order to provide potential improvements in sensitivity and physiological relevance, there is considerable scope for between-centre variation in techniques. A workshop was convened by the Imaging Committee of the Experimental Cancer Medicine Centres (ECMC) to review the current status of DCE-MRI and to provide recommendations on how the technique can best be used for early stage trials. This review and the consequent recommendations are summarised here. Key Points • Tumour vascular function is key to tumour development and treatment • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumour vascular function • Thus DCE-MRI with pharmacokinetic models can assess novel treatments • Many recent developments are advancing the accuracy of and information from DCE-MRI • Establishing common methodology across multiple centres is challenging and requires accepted guidelines

Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI

A description of the vascular input function is needed to obtain tissue kinetic parameter estimates from dynamic contrast enhanced MRI (DCE-MRI) data. This paper describes a general modelling framework for defining compact functional forms to describe vascular input functions. By appropriately specifying the components of this model it is possible to generate models that are realistic, and that ensure that the tissue concentration curves can be analytically calculated. This means that the computations necessary to estimate parameters from measured data are relatively efficient, which is important if such methods are to become of use in clinical practice. Three models defined by four parameters, using exponential, gamma-variate and cosine descriptions of the bolus, are described and their properties investigated using simulations. The results indicate that if there is no plasma fraction, then the proposed models are indistinguishable. When a small plasma fraction is present the exponential model gives parameter estimates that are biassed by up to 50%, while the other two models give very little bias; up to 10% but less than 5% in most cases. With a larger plasma fraction the exponential model is again biassed, the gamma-variate model has a small bias, but the cosine model has a very little bias and is indistinguishable from the model used to generate the data. The computational speed of the analytic approaches is compared with a fast-Fourier-transform-based numerical convolution approach. The analytic methods are nearly 10 times faster than the numerical methods for the isolated computation of the convolution, and around 4-5 times faster when used in an optimization routine to obtain parameter estimates. These results were obtained from five example data sets, one of which was examined in more detail to compare the estimates obtained using the different models, and with literature values.

Model free approach to kinetic analysis of real-time hyperpolarized 13C magnetic resonance spectroscopy data.

Real-time detection of the rates of metabolic flux, or exchange rates of endogenous enzymatic reactions, is now feasible in biological systems using Dynamic Nuclear Polarization Magnetic Resonance. Derivation of reaction rate kinetics from this technique typically requires multi-compartmental modeling of dynamic data, and results are therefore model-dependent and prone to misinterpretation. We present a model-free formulism based on the ratio of total areas under the curve (AUC) of the injected and product metabolite, for example pyruvate and lactate. A theoretical framework to support this novel analysis approach is described, and demonstrates that the AUC ratio is proportional to the forward rate constant k. We show that the model-free approach strongly correlates with k for whole cell in vitro experiments across a range of cancer cell lines, and detects response in cells treated with the pan-class I PI3K inhibitor GDC-0941 with comparable or greater sensitivity. The same result is seen in vivo with tumor xenograft-bearing mice, in control tumors and following drug treatment with dichloroacetate. An important finding is that the area under the curve is independent of both the input function and of any other metabolic pathways arising from the injected metabolite. This model-free approach provides a robust and clinically relevant alternative to kinetic model-based rate measurements in the clinical translation of hyperpolarized 13C metabolic imaging in humans, where measurement of the input function can be problematic.

Assessment of Treatment Response by Total Tumor Volume and Global Apparent Diffusion Coefficient Using Diffusion-Weighted MRI in Patients with Metastatic Bone Disease: A Feasibility Study

We describe our semi-automatic segmentation of whole-body diffusion-weighted MRI (WBDWI) using a Markov random field (MRF) model to derive tumor total diffusion volume (tDV) and associated global apparent diffusion coefficient (gADC); and demonstrate the feasibility of using these indices for assessing tumor burden and response to treatment in patients with bone metastases. WBDWI was performed on eleven patients diagnosed with bone metastases from breast and prostate cancers before and after anti-cancer therapies. Semi-automatic segmentation incorporating a MRF model was performed in all patients below the C4 vertebra by an experienced radiologist with over eight years of clinical experience in body DWI. Changes in tDV and gADC distributions were compared with overall response determined by all imaging, tumor markers and clinical findings at serial follow up. The segmentation technique was possible in all patients although erroneous volumes of interest were generated in one patient because of poor fat suppression in the pelvis, requiring manual correction. Responding patients showed a larger increase in gADC (median change = +0.18, range = −0.07 to +0.78×10−3 mm2/s) after treatment compared to non-responding patients (median change = −0.02, range = −0.10 to +0.05×10−3 mm2/s, p = 0.05, Mann-Whitney test), whereas non-responding patients showed a significantly larger increase in tDV (median change = +26%, range = +3 to +284%) compared to responding patients (median change = −50%, range = −85 to +27%, p = 0.02, Mann-Whitney test). Semi-automatic segmentation of WBDWI is feasible for metastatic bone disease in this pilot cohort of 11 patients, and could be used to quantify tumor total diffusion volume and median global ADC for assessing response to treatment.

Robust estimation of the apparent diffusion coefficient (ADC) in heterogeneous solid tumors

The least‐squares algorithm is known to bias apparent diffusion coefficient (ADC) values estimated from magnitude MR data, although this effect is commonly assumed to be negligible. In this study the effect of this bias on tumor ADC estimates was evaluated in vivo and was shown to introduce a consistent and significant underestimation of ADC, relative to those given by a robust maximum likelihood approach (on average, a 23.4 ± 12% underestimation). Monte Carlo simulations revealed that the magnitude of the bias increased with ADC and decreasing signal‐to‐noise ratio (SNR). In vivo, this resulted in a much‐reduced ability to resolve necrotic regions from surrounding viable tumor tissue compared with a maximum likelihood approach. This has significant implications for the evaluation of diffusion MR data in vivo, in particular in heterogeneous tumor tissue, when evaluating bi‐ and multiexponential tumor diffusion models for the modeling of data acquired with larger b‐values (b > 1000 s/mm2) and for data with modest SNR. Use of a robust approach to modeling magnitude MR data from tumors is therefore recommended over the least‐squares approach when evaluating data from heterogeneous tumors. Magn Reson Med, 2009.

Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling

In addition to the diffusion coefficient, fitting the intravoxel incoherent motion model to multiple b‐value diffusion‐weighted MR data gives pseudo‐diffusion measures associated with rapid signal attenuation at low b‐values that are of use in the assessment of a number of pathologies. When summary measures are required, such as the average parameter for a region of interest, least‐squares based methods give adequate estimation accuracy. However, using least‐squares methods for pixel‐wise fitting typically gives noisy estimates, especially for the pseudo‐diffusion parameters, which limits the applicability of the approach for assessing spatial features and heterogeneity. In this article, a Bayesian approach using a shrinkage prior model is proposed and is shown to substantially reduce estimation uncertainty so that spatial features in the parameters maps are more clearly apparent. The Bayesian approach has no user‐defined parameters, so measures of parameter variation (heterogeneity) over regions of interest are determined by the data alone, whereas it is shown that for the least‐squares estimates, measures of variation are essentially determined by user‐defined constraints on the parameters. Use of a Bayesian shrinkage prior approach is, therefore, recommended for intravoxel incoherent motion modeling. Magn Reson Med 71:411–420, 2014.

Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion‐weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up‐to‐date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM‐sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. J. Magn. Reson. Imaging 2016;44:521–540.