Classic models for dynamic contrast-enhanced MRI

An overview of deep learning in medical imaging focusing on MRI

What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.

Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer

The volume transfer constant K^trans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported K^trans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. K^trans , k_ep and v_e ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T₁ -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. K^trans , k_ep and v_e were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in K^trans , k_ep or v_e for the studied injection durations. The study demonstrates that the TKA parameters K^trans , k_ep and v_e , measured in the prostate, do not show a significant change as a function of injection duration.

Intravoxel incoherent motion MRI in neurological and cerebrovascular diseases

Intravoxel Incoherent Motion (IVIM) is a recently rediscovered noninvasive magnetic resonance imaging (MRI) method based on diffusion-weighted imaging. It enables the separation of the intravoxel signal into diffusion due to Brownian motion and perfusion-related contributions and provides important information on microperfusion in the tissue and therefore it is a promising tool for applications in neurological and neurovascular diseases. This review focuses on the basic principles and outputs of IVIM and details it major applications in the brain, such as stroke, tumor, and cerebral small vessel disease. A bi-exponential model that considers two different compartments, namely capillaries, and medium-sized vessels, has been frequently used for the description of the IVIM signal and may be important in those clinical applications cited before. Moreover, the combination of IVIM and arterial spin labeling MRI enables the estimation of water permeability across the blood-brain barrier (BBB), suggesting a potential imaging biomarker for disrupted-BBB diseases.

Simultaneous multislice acquisition with multi-contrast segmented EPI for separation of signal contributions in dynamic contrast-enhanced imaging

We present a method to efficiently separate signal in magnetic resonance imaging (MRI) into a base signal S0, representing the mainly T1-weighted component without T2*-relaxation, and its T2*-weighted counterpart by the rapid acquisition of multiple contrasts for advanced pharmacokinetic modelling. This is achieved by incorporating simultaneous multislice (SMS) imaging into a multi-contrast, segmented echo planar imaging (EPI) sequence to allow extended spatial coverage, which covers larger body regions without time penalty. Simultaneous acquisition of four slices was combined with segmented EPI for fast imaging with three gradient echo times in a preclinical perfusion study. Six female domestic pigs, German-landrace or hybrid-form, were scanned for 11 minutes respectively during administration of gadolinium-based contrast agent. Influences of reconstruction methods and training data were investigated. The separation into T1- and T2*-dependent signal contributions was achieved by fitting a standard analytical model to the acquired multi-echo data. The application of SMS yielded sufficient temporal resolution for the detection of the arterial input function in major vessels, while anatomical coverage allowed perfusion analysis of muscle tissue. The separation of the MR signal into T1- and T2*-dependent components allowed the correction of susceptibility related changes. We demonstrate a novel sequence for dynamic contrast-enhanced MRI that meets the requirements of temporal resolution (Δt < 1.5 s) and image quality. The incorporation of SMS into multi-contrast, segmented EPI can overcome existing limitations of dynamic contrast enhancement and dynamic susceptibility contrast methods, when applied separately. The new approach allows both techniques to be combined in a single acquisition with a large spatial coverage.

Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocol

Tumor angiogenesis is considered an important prognostic factor. With an increasing emphasis on imaging evaluation of the tumor microenvironment, dynamic contrast enhanced-computed tomography (DCE-CT) has evolved as an important functional technique in this setting. Yet many questions remain as to how and when these functional measurements should be performed for each agent and tumor type, and what quantitative models should be used in the fitting process. In this study, we evaluated the variations of perfusion measurement on DCE-CT for rectal cancer patients from (1) different tracer kinetic models, (2) different scan acquisition lengths, and (3) different scan intervals. A total of seven commonly used models were studied: the adiabatic approximation to the tissue homogeneity (AATH) model, adiabatic approximation to the homogeneity tissue with fixed transit time (AATHFT) model, the Tofts model (TM), the extended Tofts model (ETM), Patlak model, Logan model, and the model-free deconvolution method. Akaike's information criterion was used to identify the best fitting model. The interchangeability of different models was further evaluated using Bland-Altman analysis. All models gave comparable blood volume (BV) measurements except the Patlak method. While for the volume transfer constant (K_trans) estimation, AATHFT, AATH, and ETM generated reasonable agreement among each other but not for the other models. Regarding the blood flow (BF) measurement, no two models were interchangeable. In addition, the perfusion parameters were compared with four acquisition times (45, 65, 85, and 105 s) and four temporal intervals (1, 2, 3, and 4 s). No significant difference was observed in the volume transfer constant (K_trans), BV, and BF measurements when comparing data acquired over 65 s with data acquired over 105 s using any of the DCE models in this study. Yet increasing the temporal interval led to a significant overestimation of BF in the deconvolution method. In conclusion, the perfusion measurement is indeed model dependent and the image acquisition/processing technique is dependent. The radiation dose of DCE-CT was an average of 1.5-2 times an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique.

Estimating the discretization dependent accuracy of perfusion in coupled capillary flow measurements

One-compartment models are widely used to quantify hemodynamic parameters such as perfusion, blood volume and mean transit time. These parameters are routinely used for clinical diagnosis and monitoring of disease development and are thus of high relevance. However, it is known that common estimation techniques are discretization dependent and values can be erroneous. In this paper we present a new model that enables systematic quantification of discretization errors. Specifically, we introduce a continuous flow model for tracer propagation within the capillary tissue, used to evaluate state-of-the-art one-compartment models. We demonstrate that one-compartment models are capable of recovering perfusion accurately when applied to only one compartment, i.e. the whole region of interest. However, substantial overestimation of perfusion occurs when applied to fractions of a compartment. We further provide values of the estimated overestimation for various discretization levels, and also show that overestimation can be observed in real-life applications. Common practice of using compartment models for fractions of tissue violates model assumptions and careful interpretation is needed when using the computed values for diagnosis and treatment planning.

Use of the NESMA Filter to Improve Myelin Water Fraction Mapping with Brain MRI

BACKGROUND AND PURPOSE

Myelin water fraction (MWF) mapping permits direct visualization of myelination patterns in the developing brain and in pathology. MWF is conventionally measured through multiexponential T₂ analysis which is very sensitive to noise, leading to inaccuracies in derived MWF estimates. Although noise reduction filters may be applied during postprocessing, conventional filtering can introduce bias and obscure small structures and edges. Advanced nonblurring filters, while effective, exhibit a high level of complexity and the requirement for supervised implementation for optimal performance. The purpose of this paper is to demonstrate the ability of the recently introduced nonlocal estimation of multispectral magnitudes (NESMA) filter to greatly improve the determination of MWF parameter estimates from gradient and spin echo (GRASE) imaging data.

METHODS

We evaluated the performance of the NESMA filter for MWF mapping from clinical GRASE imaging data of the human brain, and compared the results to those calculated from unfiltered images. Numerical and in vivo analyses of the brains of three subjects, representing different ages, were conducted.

RESULTS

Our results demonstrated the potential of the NESMA filter to permit high-quality in vivo MWF mapping. Indeed, NESMA permits substantial reduction of random variation in derived MWF estimates while preserving accuracy and detail.

CONCLUSIONS

In vivo estimation of MWF in the human brain from GRASE imaging data was markedly improved through use of the NESMA filter. The use of NESMA may contribute to the goal of high-quality MWF mapping in clinically feasible imaging times.

An extended reference region model for DCE-MRI that accounts for plasma volume

The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated K^trans by over 10% at v_p  = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible v_p where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.

MRI measurements of Blood-Brain Barrier function in dementia: A review of recent studies

Blood-brain barrier (BBB) separates the systemic circulation and the brain, regulating transport of most molecules to protect the brain microenvironment. Multiple structural and functional components preserve the integrity of the BBB. Several imaging modalities are available to study disruption of the BBB. However, the subtle changes in BBB leakage that occurs in vascular cognitive impairment and Alzheimer's disease have been less well studied. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is the most widely adopted non-invasive imaging technique for evaluating BBB breakdown. It is used as a significant marker for a wide variety of diseases with large permeability leaks, such as brain tumors and multiple sclerosis, to more subtle disruption in chronic vascular disease and dementia. DCE-MRI analysis of BBB includes both model-free parameters and quantitative parameters using pharmacokinetic modelling. We review MRI studies of BBB breakdown in dementia. The challenges in measuring subtle BBB changes and the state of the art techniques are initially examined. Subsequently, a systematic review comparing methodologies from recent in-vivo MRI studies is presented. Various factors related to subtle BBB permeability measurement such as DCE-MRI acquisition parameters, arterial input assessment, T1 mapping and data analysis methods are reviewed with the focus on finding the optimal technique. Finally, the reported BBB permeability values in dementia are compared across different studies and across various brain regions. We conclude that reliable measurement of low-level BBB permeability across sites remains a difficult problem and a standardization of the methodology for both data acquisition and quantitative analysis is required. This article is part of the Special Issue entitled 'Cerebral Ischemia'.

Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study

PURPOSE

To determine the in vitro accuracy, test-retest repeatability, and interplatform reproducibility of T₁ quantification protocols used for dynamic contrast-enhanced MRI at 1.5 and 3 T.

METHODS

A T₁ phantom with 14 samples was imaged at eight centers with a common inversion-recovery spin-echo (IR-SE) protocol and a variable flip angle (VFA) protocol using seven flip angles, as well as site-specific protocols (VFA with different flip angles, variable repetition time, proton density, and Look-Locker inversion recovery). Factors influencing the accuracy (deviation from reference NMR T₁ measurements) and repeatability were assessed using general linear mixed models. Interplatform reproducibility was assessed using coefficients of variation.

RESULTS

For the common IR-SE protocol, accuracy (median error across platforms = 1.4-5.5%) was influenced predominantly by T₁ sample (P < 10^-6 ), whereas test-retest repeatability (median error = 0.2-8.3%) was influenced by the scanner (P < 10^-6 ). For the common VFA protocol, accuracy (median error = 5.7-32.2%) was influenced by field strength (P = 0.006), whereas repeatability (median error = 0.7-25.8%) was influenced by the scanner (P < 0.0001). Interplatform reproducibility with the common VFA was lower at 3 T than 1.5 T (P = 0.004), and lower than that of the common IR-SE protocol (coefficient of variation 1.5T: VFA/IR-SE = 11.13%/8.21%, P = 0.028; 3 T: VFA/IR-SE = 22.87%/5.46%, P = 0.001). Among the site-specific protocols, Look-Locker inversion recovery and VFA (2-3 flip angles) protocols showed the best accuracy and repeatability (errors < 15%).

CONCLUSIONS

The VFA protocols with 2 to 3 flip angles optimized for different applications achieved acceptable balance of extensive spatial coverage, accuracy, and repeatability in T₁ quantification (errors < 15%). Further optimization in terms of flip-angle choice for each tissue application, and the use of B₁ correction, are needed to improve the robustness of VFA protocols for T₁ mapping. Magn Reson Med 79:2564-2575, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Imaging techniques to study drug transporter function in vivo

Transporter systems involved in the permeation of drugs and solutes across biological membranes are recognized as key determinants of pharmacokinetics. Typically, the action of membrane transporters on drug exposure to tissues in living organisms is inferred from invasive procedures, which cannot be applied in humans. In recent years, imaging methods have greatly progressed in terms of instruments, synthesis of novel imaging probes as well as tools for data analysis. Imaging allows pharmacokinetic parameters in different tissues and organs to be obtained in a non-invasive or minimally invasive way. The aim of this overview is to summarize the current status in the field of molecular imaging of drug transporters. The overview is focused on human studies, both for the characterization of transport systems for imaging agents as well as for the determination of drug pharmacokinetics, and makes reference to animal studies where necessary. We conclude that despite certain methodological limitations, imaging has a great potential to study transporters at work in humans and that imaging will become an important tool, not only in drug development but also in medicine. Imaging allows the mechanistic aspects of transport proteins to be studied, as well as elucidating the influence of genetic background, pathophysiological states and drug-drug interactions on the function of transporters involved in the disposition of drugs.

Contrast agents in dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive method to assess angiogenesis, which is widely used in clinical applications including diagnosis, monitoring therapy response and prognosis estimation in cancer patients. Contrast agents play a crucial role in DCE-MRI and should be carefully selected in order to improve accuracy in DCE-MRI examination. Over the past decades, there was much progress in the development of optimal contrast agents in DCE-MRI. In this review, we describe the recent research advances in this field and discuss properties of contrast agents, as well as their advantages and disadvantages. Finally, we discuss the research perspectives for improving this promising imaging method.

Functional morphology of the blood-brain barrier in health and disease

The adult quiescent blood-brain barrier (BBB), a structure organised by endothelial cells through interactions with pericytes, astrocytes, neurons and microglia in the neurovascular unit, is highly regulated but fragile at the same time. In the past decade, there has been considerable progress in understanding not only the molecular pathways involved in BBB development, but also BBB breakdown in neurological diseases. Specifically, the Wnt/β-catenin, retinoic acid and sonic hedgehog pathways moved into the focus of BBB research. Moreover, angiopoietin/Tie2 signalling that is linked to angiogenic processes has gained attention in the BBB field. Blood vessels play an essential role in initiation and progression of many diseases, including inflammation outside the central nervous system (CNS). Therefore, the potential influence of CNS blood vessels in neurological diseases associated with BBB alterations or neuroinflammation has become a major focus of current research to understand their contribution to pathogenesis. Moreover, the BBB remains a major obstacle to pharmaceutical intervention in the CNS. The complications may either be expressed by inadequate therapeutic delivery like in brain tumours, or by poor delivery of the drug across the BBB and ineffective bioavailability. In this review, we initially describe the cellular and molecular components that contribute to the steady state of the healthy BBB. We then discuss BBB alterations in ischaemic stroke, primary and metastatic brain tumour, chronic inflammation and Alzheimer's disease. Throughout the review, we highlight common mechanisms of BBB abnormalities among these diseases, in particular the contribution of neuroinflammation to BBB dysfunction and disease progression, and emphasise unique aspects of BBB alteration in certain diseases such as brain tumours. Moreover, this review highlights novel strategies to monitor BBB function by non-invasive imaging techniques focussing on ischaemic stroke, as well as novel ways to modulate BBB permeability and function to promote treatment of brain tumours, inflammation and Alzheimer's disease. In conclusion, a deep understanding of signals that maintain the healthy BBB and promote fluctuations in BBB permeability in disease states will be key to elucidate disease mechanisms and to identify potential targets for diagnostics and therapeutic modulation of the BBB.

Histogram analysis of T2*-based pharmacokinetic imaging in cerebral glioma grading

BACKGROUND AND OBJECTIVE

To investigate the feasibility of histogram analysis of the T2*-based permeability parameter volume transfer constant (K^trans) for glioma grading and to explore the diagnostic performance of the histogram analysis of K^trans and blood plasma volume (v_p).

METHODS

We recruited 31 and 11 patients with high- and low-grade gliomas, respectively. The histogram parameters of K^trans and v_p, derived from the first-pass pharmacokinetic modeling based on the T2* dynamic susceptibility-weighted contrast-enhanced perfusion-weighted magnetic resonance imaging (T2* DSC-PW-MRI) from the entire tumor volume, were evaluated for differentiating glioma grades.

RESULTS

Histogram parameters of K^trans and v_p showed significant differences between high- and low-grade gliomas and exhibited significant correlations with tumor grades. The mean K^trans derived from the T2* DSC-PW-MRI had the highest sensitivity and specificity for differentiating high-grade gliomas from low-grade gliomas compared with other histogram parameters of K^trans and v_p.

CONCLUSIONS

Histogram analysis of T2*-based pharmacokinetic imaging is useful for cerebral glioma grading. The histogram parameters of the entire tumor K^trans measurement can provide increased accuracy with additional information regarding microvascular permeability changes for identifying high-grade brain tumors.

A DCE-MRI Driven 3-D Reaction-Diffusion Model of Solid Tumor Growth

Predicting tumor growth and its response to therapy remains a major challenge in cancer research and strongly relies on tumor growth models. In this paper, we introduce, calibrate, and verify a novel image-driven reaction-diffusion model of avascular tumor growth. The model allows for proliferation, death and spread of tumor cells, and accounts for nutrient distribution and hypoxia. It is constrained by longitudinal time series of dynamic contrast-enhancement-MRI images. Tumor specific parameters are estimated from two early time points and used to predict the spatio-temporal evolution of the tumor volume and cell densities at later time points. We first test our parameter estimation approach on synthetic data from 15 generated tumors. Our in silico study resulted in small volume errors (<5%) and high Dice overlaps (>97%), showing that model parameters can be successfully recovered and used to accurately predict the tumor growth. Encouraged by these results, we apply our model to seven pre-clinical cases of breast carcinoma. We are able to show promising preliminary results, especially for the estimation for early time points. Processes like angiogenesis and apoptosis should be included to further improve predictions for later time points.

A multiparametric analysis combining DCE-MRI- and IVIM -derived parameters to improve differentiation of parotid tumors: a pilot study

AIM

To evaluate dynamic contrast-enhanced (DCE)-MRI and diffusion weighted (DW)-MRI diagnostic value to differentiate Warthin tumors (WT) by pleomorphic adenomas (PA).

MATERIALS & METHODS

Seven WT and seven PA were examined. DCE- and  DW-MRI parameters were extracted from volumes of interest; volume of interest-based averages and standard deviations were calculated. Statistical analysis included: linear discriminant analysis, receiver operating characteristic curves, sensitivity and specificity.

RESULTS

No single feature was able to differentiate WT by PA (p > 0.05); linear discriminant analysis analysis showed that a combination of all features or combinations of feature pairs (namely: K^trans(std) & f(std), K^trans(std) & D(std), k_ep(std) & D(std), MRE(av) & TTP(av)) might achieve sensitivity (SENS), specificity (SPEC) = 100%, with a slight reduction after cross-validation analysis (SENS = 0.875; SPEC = 1).

CONCLUSION

Although preliminary and not conclusive, our results suggest that differentiation between WT and PA is possible through a multiparametric approach based on combination of DCE- and DW-MRI parameters.

Meningioma assessment: Kinetic parameters in dynamic contrast-enhanced MRI appear independent from microvascular anatomy and VEGF expression

BACKGROUND AND PURPOSE

Kinetic parameters of T1-weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are considered to be influenced by microvessel environment. This study was performed to explore the extent of this association for meningiomas.

MATERIALS AND METHODS

DCE-MRI kinetic parameters (contrast agent transfer constants K^trans and k_ep, volume fractions v_p and v_e) were determined in pre-operative 3T MRI of meningioma patients for later biopsy sites (19 patients; 15 WHO I^o, no previous radiation, and 4 WHO III^o pre-radiated recurrent tumors). Sixty-three navigated biopsies were consecutively retrieved. Biopsies were immunohistochemically investigated with endothelial marker CD34 and VEGF antibodies, stratified in a total of 4383 analysis units and computationally assessed for VEGF expression and vascular parameters (vessel density, vessel quantity, vascular fraction within tissue [vascular area ratio], vessel wall thickness). Derivability of kinetic parameters from VEGF expression or microvascularization was determined by mixed linear regression analysis. Tissue kinetic and microvascular parameters were tested for their capacity to identify the radiation status in a subanalysis.

RESULTS

Kinetic parameters were neither significantly related to the corresponding microvascular parameters nor to tissue VEGF expression. There was no significant association between microvessel density and its presumed correlate v_p (P=0.07). The subgroup analysis of high-grade radiated meningiomas showed a significantly reduced microvascular density (AUC 0.91; P<0.0001) and smaller total vascular fraction (AUC 0.73; P=0.01).

CONCLUSIONS

In meningioma, DCE-MRI kinetic parameters neither allow for a reliable prediction of tumor microvascularization, nor for a prediction of VEGF expression. Kinetic parameters seem to be determined from different independent factors.

Improved Evaluation of Antivascular Cancer Therapy Using Constrained Tracer-Kinetic Modeling for Multiagent Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI (DCE-MRI) is a promising technique for assessing the response of tumor vasculature to antivascular therapies. Multiagent DCE-MRI employs a combination of low and high molecular weight contrast agents, which potentially improves the accuracy of estimation of tumor hemodynamic and vascular permeability parameters. In this study, we used multiagent DCE-MRI to assess changes in tumor hemodynamics and vascular permeability after vascular-disrupting therapy. Multiagent DCE-MRI (sequential injection of G5 dendrimer, G2 dendrimer, and Gd-DOTA) was performed in tumor-bearing mice before, 2 and 24 hours after treatment with vascular disrupting agent DMXAA or placebo. Constrained DCE-MRI gamma capillary transit time modeling was used to estimate flow F, blood volume fraction v_b, mean capillary transit time t_c, bolus arrival time t_d, extracellular extravascular fraction v_e, vascular heterogeneity index α^-1 (all identical between agents) and extraction fraction E (reflective of permeability), and transfer constant K^trans (both agent-specific) in perfused pixels. F, v_b, and α^-1 decreased at both time points after DMXAA, whereas t_c increased. E (G2 and G5) showed an initial increase, after which, both parameters restored. K^trans (G2 and Gd-DOTA) decreased at both time points after treatment. In the control, placebo-treated animals, only F, t_c, and K^trans Gd-DOTA showed significant changes. Histologic perfused tumor fraction was significantly lower in DMXAA-treated versus control animals. Our results show how multiagent tracer-kinetic modeling can accurately determine the effects of vascular-disrupting therapy by separating simultaneous changes in tumor hemodynamics and vascular permeability.Significance: These findings describe a new approach to measure separately the effects of antivascular therapy on tumor hemodynamics and vascular permeability, which could help more rapidly and accurately assess the efficacy of experimental therapy of this class. Cancer Res; 78(6); 1561-70. ©2018 AACR.

Non-invasive Amide Proton Transfer Imaging and ZOOM Diffusion-Weighted Imaging in Differentiating Benign and Malignant Thyroid Micronodules

Background: Pre-operative non-invasive differentiation of benign and malignant thyroid nodules is difficult for doctors. This study aims to determine whether amide proton transfer (APT) imaging and zonally oblique multi-slice (ZOOM) diffusion-weighted imaging (DWI) can provide increased accuracy in differentiating benign and malignant thyroid nodules. Methods: This retrospective study was approved by the institutional review board and included 60 thyroid nodules in 50 patients. All of the nodules were classified as malignant (n = 21) or benign (n = 39) based on pathology. It was meaningful to analyze the APT and apparent diffusion coefficient (ADC) values of the two groups by independent t-test to identify the benign and malignant thyroid nodules. The relationship between APT and ZOOM DWI was explored through Pearson correlation analysis. The diagnostic efficacy of APT and ZOOM DWI in determining if thyroid nodules were benign or malignant was compared using receiver operating characteristic (ROC) curve analysis. Results: The mean APTw value of the benign nodules was 2.99 ± 0.79, while that of the malignant nodules was 2.14 ± 0.73. Additionally, there was a significant difference in the APTw values of the two groups (P < 0.05). The mean ADC value of the benign nodules was 1.84 ± 0.41, and was significantly different from that of the malignant nodules, which was 1.21 ± 0.19 (P < 0.05). Scatter point and Pearson test showed a moderate positive correlation between the APT and ADC values (P < 0.05). The ROC curve showed that the area under the curve (AUC) value of ZOOM DWI (AUC = 0.937) was greater than that of APT (AUC = 0.783) (P = 0.028). Conclusion: APT and ZOOM DWI imaging improved the accuracy of distinguishing between benign and malignant thyroid nodules. ZOOM DWI is superior to APTw imaging (Z = 2.198, P < 0.05).

Dynamic contrast-enhanced magnetic resonance imaging of prostate cancer: A review of current methods and applications

In many areas of oncology, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has proven to be a clinically useful, non-invasive functional imaging technique to quantify tumor vasculature and tumor perfusion characteristics. Tumor angiogenesis is an essential process for tumor growth, proliferation, and metastasis. Malignant lesions demonstrate rapid extravasation of contrast from the intravascular space to the capillary bed due to leaky capillaries associated with tumor neovascularity. DCE-MRI has the potential to provide information regarding blood flow, areas of hypoperfusion, and variations in endothelial permeability and microvessel density to aid treatment selection, enable frequent monitoring during treatment and assess response to targeted therapy following treatment. This review will discuss the current status of DCE-MRI in cancer imaging, with a focus on its use in imaging prostate malignancies as well as weaknesses that limit its widespread clinical use. The latest techniques for quantification of DCE-MRI parameters will be reviewed and compared.

Microvascular perfusion of the placenta, developing fetal liver, and lungs assessed with intravoxel incoherent motion imaging

BACKGROUND

In utero intravoxel incoherent motion magnetic resonance imaging (IVIM-MRI) provides a novel method for examining microvascular perfusion fraction and diffusion in the developing human fetus.

PURPOSE

To characterize gestational changes in the microvascular perfusion fraction of the placenta, fetal liver, and lungs using IVIM-MRI.

STUDY TYPE

Retrospective, cross-sectional study.

SUBJECTS

Fifty-five datasets from 33 singleton pregnancies were acquired (17-36 gestational weeks).

FIELD STRENGTH/SEQUENCE

In utero diffusion-weighted echo-planar imaging at 1.5T and 3.0T with b-factors ranging from 0 to 900 s/mm² in 16 steps.

ASSESSMENT

Using the IVIM principle, microvascular perfusion fraction (f), pseudodiffusion (D*), and diffusion coefficients (d) were estimated for the placenta, liver, and lungs with a biexponential model. A free-form nonlinear deformation algorithm was used to correct for the frame-by-frame motion of the fetal organs and the placenta. The IVIM parameters were then compared to a Doppler ultrasound-based assessment of the umbilical artery resistance index.

STATISTICAL TESTS

Pearson product-moment correlation coefficient (PMCC) to reveal outlier corrected correlations between Doppler and IVIM parameters. Gestational age-related changes were assessed using linear regression analysis (LR).

RESULTS

Placental f (0.29 ± 0.08) indicates high blood volume in the microvascular compartment, moderately increased during gestation (LR, R = 0.338), and correlated negatively with the umbilical artery resistance index (PMCC, R = -0.457). The f of the liver decreased sharply during gestation (LR, R = -0.436). Lung maturation was characterized by increasing perfusion fraction (LR, R = 0.547), and we found no gestational changes in d and D* values (LR, R = -0.013 and R = 0.051, respectively). The Doppler measurements of the umbilical artery and middle cerebral artery did not correlate with the IVIM parameters of the lungs and liver.

DATA CONCLUSION

Gestational age-associated changes of the placental, liver, and lung IVIM parameters likely reflect changes in placental and fetal circulation, and characterize the trajectory of microstructural and functional maturation of the fetal vasculature.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017.

Assessment of Correlation between Intravoxel Incoherent Motion Diffusion Weighted MR Imaging and Dynamic Contrast-Enhanced MR Imaging of Sacroiliitis with Ankylosing Spondylitis

The relationships between IVIM and DCE-MRI parameters in AS are not clear. We explore the correlation between intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) and dynamic contrast-enhanced (DCE) parameters obtained on MR images in patients with ankylosing spondylitis (AS). Forty-four patients with AS were prospectively examined using a 1.5-T MR system. IVIM DWI was performed with 11 b values (range, 0–800 s/mm²) for all patients. The correlation coefficients between IVIM and DCE-MRI parameters were analyzed using Spearman's method. Our results showed that intra- and interobserver reproducibility were excellent to relatively good (ICC = 0.804–0.981; narrow width of 95% limits of agreement). Moderate positive correlations were observed between pure molecular diffusion (Ds) and maximum enhancement (ME) and relative enhancement (RE) (r  =  0.700, P < 0.001; r = 0.607, P < 0.001, resp.). Perfusion-related diffusion (Df) showed negative moderate correlation with ME (r  =  −0.608, P < 0.001). However, no correlation was observed between perfusion fraction (f) and any parameters of ME, RE, TTP, and BE (r = −0.093–0.213; P > 0.165). In conclusion, the IVIM parameters, especially f, might play a critical role in detecting the progression of AS, because it can provide more perfusion information compared with DCE-MRI; besides the IVIM MRI is a noninvasive method.

The impact of injector-based contrast agent administration in time-resolved MRA

OBJECTIVES

Time-resolved contrast-enhanced MR angiography (4D-MRA), which allows the simultaneous visualization of the vasculature and blood-flow dynamics, is widely used in clinical routine. In this study, the impact of two different contrast agent injection methods on 4D-MRA was examined in a controlled, standardized setting in an animal model.

METHODS

Six anesthetized Goettingen minipigs underwent two identical 4D-MRA examinations at 1.5 T in a single session. The contrast agent (0.1 mmol/kg body weight gadobutrol, followed by 20 ml saline) was injected using either manual injection or an automated injection system. A quantitative comparison of vascular signal enhancement and quantitative renal perfusion analyses were performed.

RESULTS

Analysis of signal enhancement revealed higher peak enhancements and shorter time to peak intervals for the automated injection. Significantly different bolus shapes were found: automated injection resulted in a compact first-pass bolus shape clearly separated from the recirculation while manual injection resulted in a disrupted first-pass bolus with two peaks. In the quantitative perfusion analyses, statistically significant differences in plasma flow values were found between the injection methods.

CONCLUSIONS

The results of both qualitative and quantitative 4D-MRA depend on the contrast agent injection method, with automated injection providing more defined bolus shapes and more standardized examination protocols.

KEY POINTS

• Automated and manual contrast agent injection result in different bolus shapes in 4D-MRA. • Manual injection results in an undefined and interrupted bolus with two peaks. • Automated injection provides more defined bolus shapes. • Automated injection can lead to more standardized examination protocols.

Quantitative Evaluation of Vertebral Microvascular Permeability and Fat Fraction in Alloxan-induced Diabetic Rabbits

Purpose To determine longitudinal relationships between lumbar vertebral bone marrow permeability and marrow adipose tissue in a rabbit diabetes model by using quantitative dynamic contrast agent-enhanced (DCE) magnetic resonance (MR) imaging and iterative decomposition of water and fat with the echo asymmetry and least-squares estimation quantitation (IDEAL IQ) sequence. Materials and Methods Twenty rabbits were randomly assigned to the diabetic (n = 10) or control (n = 10) group. All rabbits underwent sagittal MR imaging of the lumbar region at fixed time points (0, 4, 8, 12, and 16 weeks after alloxan injection). A linear mixed-effects model was used to analyze fat fraction (FF) and permeability parameter changes for 16 months after baseline. These parameters were compared between the two groups by using an independent-samples t test. Correlation of DCE MR imaging parameters with FF and with microvessel density (MVD) was analyzed by using the Spearman correlation coefficient. All statistical analyses were performed with software. Results Twelve weeks after injection, transfer constant (K^trans) and rate constant (K_ep) were markedly and significantly increased, while fractional plasma volume (V_p) significantly decreased. The volume of extravascular extracellular space (V_e) decreased significantly after 16 weeks in the diabetic group. MVD was negatively correlated with K^trans and K_ep and positively correlated with V_e and V_p, while FF was positively correlated with K^trans and K_ep and negatively correlated with V_e and V_p (P < .05 for all). Conclusion DCE MR imaging and the IDEAL IQ sequence can be used for quantitative evaluation of changes in vertebral microvascular permeability and vertebral fat deposition in alloxan-induced diabetic rabbits. This variation is highly associated with increased vertebral fat deposition. ^© RSNA, 2017 Online supplemental material is available for this article.

Comparison of semi-quantitative and quantitative dynamic contrast-enhanced MRI evaluations of vertebral marrow perfusion in a rat osteoporosis model

BACKGROUND

This study aims to investigate the technical feasibility of semi-quantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the assessment of longitudinal changes of marrow perfusion in a rat osteoporosis model, using bone mineral density (BMD) measured by micro-computed tomography (micro-CT) and histopathology as the gold standards.

METHODS

Fifty rats were randomly assigned to the control group (n=25) and ovariectomy (OVX) group whose bilateral ovaries were excised (n=25). Semi-quantitative and quantitative DCE-MRI, micro-CT, and histopathological examinations were performed on lumbar vertebrae at baseline and 3, 6, 9, and 12 weeks after operation. The differences between the two groups in terms of semi-quantitative DCE-MRI parameter (maximum enhancement, E_max), quantitative DCE-MRI parameters (volume transfer constant, K^trans; interstitial volume, V_e; and efflux rate constant, K_ep), micro-CT parameter (BMD), and histopathological parameter (microvessel density, MVD) were compared at each of the time points using an independent-sample t test. The differences in these parameters between baseline and other time points in each group were assessed via Bonferroni's multiple comparison test. A Pearson correlation analysis was applied to assess the relationships between DCE-MRI, micro-CT, and histopathological parameters.

RESULTS

In the OVX group, the E_max values decreased significantly compared with those of the control group at weeks 6 and 9 (p=0.003 and 0.004, respectively). The K^trans values decreased significantly compared with those of the control group from week 3 (p<0.05). However, the V_e values decreased significantly only at week 9 (p=0.032), and no difference in the K_ep was found between two groups. The BMD values of the OVX group decreased significantly compared with those of the control group from week 3 (p<0.05). Transmission electron microscopy showed tighter gaps between vascular endothelial cells with swollen mitochondria in the OVX group from week 3. The MVD values of the OVX group decreased significantly compared with those of the control group only at week 12 (p=0.023). A weak positive correlation of E_max and a strong positive correlation of K^trans with MVD were found.

CONCLUSIONS

Compared with semi-quantitative DCE-MRI, the quantitative DCE-MRI parameter K^trans is a more sensitive and accurate index for detecting early reduced perfusion in osteoporotic bone.

Quantitative Assessment of Liver Function Using Gadoxetate-Enhanced Magnetic Resonance Imaging: Monitoring Transporter-Mediated Processes in Healthy Volunteers

Objective

The objective of this study was to use noninvasive dynamic contrast-enhanced magnetic resonance imaging (MRI) techniques to study, in vivo, the distribution and elimination of the hepatobiliary contrast agent gadoxetate in the human body and characterize the transport mechanisms involved in its uptake into hepatocytes and subsequent efflux into the bile using a novel tracer kinetic model in a group of healthy volunteers.

Materials and Methods

Ten healthy volunteers (age range, 18–29 years), with no history of renal or hepatic impairment, were recruited via advertisement. Participants attended 2 MRI visits (at least a week apart) with gadoxetate as the contrast agent. Dynamic contrast-enhanced MRI data were acquired for approximately 50 minutes with a 3-dimensional gradient-echo sequence in the axial plane, at a temporal resolution of 6.2 seconds. Data from regions of interest drawn in the liver were analyzed using the proposed 2-compartment uptake and efflux model to provide estimates for the uptake rate of gadoxetate in hepatocytes and its efflux rate into the bile. Reproducibility statistics for the 2 visits were obtained to examine the robustness of the technique and its dependence in acquisition time.

Results

Eight participants attended the study twice and were included into the analysis. The resulting images provided the ability to simultaneously monitor the distribution of gadoxetate in multiple organs including the liver, spleen, and kidneys as well as its elimination through the common bile duct, accumulation in the gallbladder, and excretion in the duodenum. The mean uptake (k_i) and efflux (k_ef) rates in hepatocytes, for the 2 visits using the 50-minute acquisition, were 0.22 ± 0.05 and 0.017 ± 0.006/min, respectively. The hepatic extraction fraction was estimated to be 0.19 ± 0.04/min. The variability between the 2 visits within the group level (95% confidence interval; k_i: ±0.02/min, k_ef: ±0.004/min) was lower compared with the individual variability (repeatability; k_i: ±0.06/min, k_ef: ±0.012/min). Data truncation demonstrated that the uptake rate estimates retained their precision as well as their group and individual reproducibility down to approximately 10 minutes of acquisition. Efflux rate estimates were underestimated (compared with the 50-minute acquisition) as the duration of the acquisition decreased, although these effects were more pronounced for acquisition times shorter than approximately 30 minutes.

Conclusions

This is the first study that reports estimates for the hepatic uptake and efflux transport process of gadoxetate in healthy volunteers in vivo. The results highlight that dynamic contrast-enhanced MRI with gadoxetate can provide novel quantitative insights into liver function and may therefore prove useful in studies that aim to monitor liver pathology, as well as being an alternative approach for studying hepatic drug-drug interactions.

Detection of cartilage invasion in laryngeal carcinoma with dynamic contrast-enhanced CT

Objective

Staging of laryngeal cancer largely depends on cartilage invasion. Presence of cartilage invasion affects treatment choice and prognosis. On MRI and contrast‐enhanced CT (CECT) it may be challenging to differentiate cartilage invasion from inflammation. The purpose of this study is to compare the diagnostic properties of dynamic contrast‐enhanced CT (DCECT) and CECT for visual detection of cartilage invasion in laryngeal cancer.

Study Design

Prospective cohort study.

Methods

Patients with T3 or T4 laryngeal squamous cell carcinoma treated with total laryngectomy were evaluated using 0.625 mm slice CT. DCECT derived permeability and blood volume maps and CECT images were visually evaluated for the presence of invasion of the cartilaginous T‐stage subsites of laryngeal cancer, by detecting continuity with the tumor‐bulk of increased permeability, increased blood volume, and enhancement. Histological evaluation of the surgical total laryngectomy specimen served as the gold standard. Sensitivity, specificity, negative predictive value, and positive predictive value were calculated and compared using the McNemar and Chi‐squared test.

Results

From 14 included patients, a total of 462 subsites were available for T‐stage analysis, of which 84 were cartilage. The median time between CT imaging and total laryngectomy was 1 day (range 1–34 days). There was no significant difference in the detection of cartilage invasion between DCECT and CECT. The sensitivity of CECT was better for all subsites combined (0.85 vs. 0.75; p < 0.01).

Conclusion

DCECT does not improve visual detection of cartilage invasion in T3 and T4 laryngeal cancer compared to CECT.

Level of Evidence

2b, individual cohort study.

Comparison of perfusion models for quantitative T1 weighted DCE-MRI of rectal cancer

In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T₁ weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and [Formula: see text] were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF_2CX = 68 ± 44 ml/100 ml/min, PF_2CU = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, [Formula: see text]:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and [Formula: see text] values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.

Focal Low and Global High Permeability Predict the Possibility, Risk, and Location of Hemorrhagic Transformation following Intra-Arterial Thrombolysis Therapy in Acute Stroke

BACKGROUND AND PURPOSE

The contrast volume transfer coefficient (K^trans), which reflects blood-brain barrier permeability, is influenced by circulation and measurement conditions. We hypothesized that focal low BBB permeability values can predict the spatial distribution of hemorrhagic transformation and global high BBB permeability values can predict the likelihood of hemorrhagic transformation.

MATERIALS AND METHODS

We retrospectively enrolled 106 patients with hemispheric stroke who received intra-arterial thrombolytic treatment. K^trans maps were obtained with first-pass perfusion CT data. The K^trans values at the region level, obtained with the Alberta Stroke Program Early CT Score system, were compared to determine the differences between the hemorrhagic transformation and nonhemorrhagic transformation regions. The K^trans values of the whole ischemic region based on baseline perfusion CT were obtained as a variable to hemorrhagic transformation possibility at the global level.

RESULTS

Forty-eight (45.3%) patients had hemorrhagic transformation, and 21 (19.8%) had symptomatic intracranial hemorrhage. At the region level, there were 82 ROIs with hemorrhagic transformation and parenchymal hemorrhage with a mean K^trans, 0.5 ± 0.5/min, which was significantly lower than that in the nonhemorrhagic transformation regions (P < .01). The mean K^trans value of 615 nonhemorrhagic transformation ROIs was 0.7 ± 0.6/min. At the global level, there was a significant difference (P = .01) between the mean K^trans values of patients with symptomatic intracranial hemorrhage (1.3 ± 0.9) and those without symptomatic intracranial hemorrhage (0.8 ± 0.4). Only a high K^trans value at the global level could predict the occurrence of symptomatic intracranial hemorrhage (P < .01; OR = 5.04; 95% CI, 2.01-12.65).

CONCLUSIONS

Global high K^trans values can predict the likelihood of hemorrhagic transformation or symptomatic intracranial hemorrhage at the patient level, whereas focal low K^trans values can predict the spatial distributions of hemorrhagic transformation at the region level.

Advanced MRI Measures of Cerebral Perfusion and Their Clinical Applications

Cerebral blood flow measurement by magnetic resonance imaging perfusion (MRP) techniques is broadly applied to patients with acute ischemic stroke, vasospasm following aneurysmal subarachnoid hemorrhage, chronic arterial steno-occlusive disease, cervical atherosclerotic disease, and primary brain neoplasms. MRP may be performed using an exogenous tracer, most commonly gadolinium-based intravenous contrast, or an endogenous tracer, such as arterial spin labeling (ASL) or intravoxel incoherent motion (IVIM). Here, we review the technical basis of commonly performed MRP techniques, the interpretation of MRP imaging maps, and how MRP provides valuable clinical information in the triage of patients with cerebral disease.

Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain

The assessment of the free water fraction in the brain provides important information about extracellular processes such as atrophy and neuroinflammation in various clinical conditions as well as in normal development and aging. Free water estimates from diffusion MRI are assumed to account for freely diffusing water molecules in the extracellular space, but may be biased by other pools of molecules in rapid random motion, such as the intravoxel incoherent motion (IVIM) of blood, where water molecules perfuse in the randomly oriented capillary network. The goal of this work was to separate the signal contribution of the perfusing blood from that of free-water and of other brain diffusivities. The influence of the vascular compartment on the estimation of the free water fraction and other diffusivities was investigated by simulating perfusion in diffusion MRI data. The perfusion effect in the simulations was significant, especially for the estimation of the free water fraction, and was maintained as long as low b-value data were included in the analysis. Two approaches to reduce the perfusion effect were explored in this study: (i) increasing the minimal b-value used in the fitting, and (ii) using a three-compartment model that explicitly accounts for water molecules in the capillary blood. Estimation of the model parameters while excluding low b-values reduced the perfusion effect but was highly sensitive to noise. The three-compartment model fit was more stable and additionally, provided an estimation of the volume fraction of the capillary blood compartment. The three-compartment model thus disentangles the effects of free water diffusion and perfusion, which is of major clinical importance since changes in these components in the brain may indicate different pathologies, i.e., those originating from the extracellular space, such as neuroinflammation and atrophy, and those related to the vascular space, such as vasodilation, vasoconstriction and capillary density. Diffusion MRI data acquired from a healthy volunteer, using multiple b-shells, demonstrated an expected non-zero contribution from the blood fraction, and indicated that not accounting for the perfusion effect may explain the overestimation of the free water fraction evinced in previous studies. Finally, the applicability of the method was demonstrated with a dataset acquired using a clinically feasible protocol with shorter acquisition time and fewer b-shells.

Automation of pattern recognition analysis of dynamic contrast-enhanced MRI data to characterize intratumoral vascular heterogeneity

PURPOSE

To automate dynamic contrast-enhanced MRI (DCE-MRI) data analysis by unsupervised pattern recognition (PR) to enable spatial mapping of intratumoral vascular heterogeneity.

METHODS

Three steps were automated. First, the arrival time of the contrast agent at the tumor was determined, including a calculation of the precontrast signal. Second, four criteria-based algorithms for the slice-specific selection of number of patterns (NP) were validated using 109 tumor slices from subcutaneous flank tumors of five different tumor models. The criteria were: half area under the curve, standard deviation thresholding, percent signal enhancement, and signal-to-noise ratio (SNR). The performance of these criteria was assessed by comparing the calculated NP with the visually determined NP. Third, spatial assignment of single patterns and/or pattern mixtures was obtained by way of constrained nonnegative matrix factorization.

RESULTS

The determination of the contrast agent arrival time at the tumor slice was successfully automated. For the determination of NP, the SNR-based approach outperformed other selection criteria by agreeing >97% with visual assessment. The spatial localization of single patterns and pattern mixtures, the latter inferring tumor vascular heterogeneity at subpixel spatial resolution, was established successfully by automated assignment from DCE-MRI signal-versus-time curves.

CONCLUSION

The PR-based DCE-MRI analysis was successfully automated to spatially map intratumoral vascular heterogeneity. Magn Reson Med 79:1736-1744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A semi-automatic method for the extraction of the portal venous input function in quantitative dynamic contrast-enhanced CT of the liver

OBJECTIVE

To aid the extraction of the portal venous input function (PVIF) from axial dynamic contrast-enhanced CT images of the liver, eliminating the need for full manual outlining of the vessel across time points.

METHODS

A cohort of 20 patients undergoing perfusion CT imaging of the liver was examined. Dynamic images of the liver were reformatted into contiguous thin slices. A region of interest was defined within a transverse section of the portal vein on a single contrast-enhanced image. This region of interest was then computationally projected across all thin slices for all time points to yield a semi-automated PVIF curve. This was compared against the "gold-standard" PVIF curve obtained by conventional manual outlining.

RESULTS

Bland-Altman plots of curve characteristics indicated no substantial difference between automated and manual PVIF curves [concordance correlation coefficient in the range (0.66, 0.98)]. No substantial differences were shown by Bland-Altman plots of derived pharmacokinetic parameters when a suitable kinetic model was applied in each case [concordance correlation coefficient in range (0.92, 0.95)].

CONCLUSION

This semi-automated method of extracting the PVIF performed equivalently to a "gold-standard" manual method for assessing liver function. Advances in knowledge: This technique provides a quick, simple and effective solution to the problems incurred by respiration motion and partial volume factors in the determination of the PVIF in liver dynamic contrast-enhanced CT.

An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images

One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T₁ ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (K^trans , v_p and K_b ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF

PURPOSE

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF.

PROCEDURES

Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data.

RESULTS

When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach.

CONCLUSIONS

The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

The prognostic value of dynamic contrast-enhanced MRI contrast agent transfer constant Ktrans in cervical cancer is explained by plasma flow rather than vessel permeability

BACKGROUND

The microvascular contrast agent transfer constant Ktrans has shown prognostic value in cervical cancer patients treated with chemoradiotherapy. This study aims to determine whether this is explained by the contribution to Ktrans of plasma flow (Fp), vessel permeability surface-area product (PS), or a combination of both.

METHODS

Pre-treatment dynamic contrast-enhanced MRI (DCE-MRI) data from 36 patients were analysed using the two-compartment exchange model. Estimates of Fp, PS, Ktrans, and fractional plasma and interstitial volumes (vp and ve) were made and used in univariate and multivariate survival analyses adjusting for clinicopathologic variables tumour stage, nodal status, histological subtype, patient age, tumour volume, and treatment type (chemoradiotherapy vs radiotherapy alone).

RESULTS

In univariate analyses, Fp (HR=0.25, P=0.0095) and Ktrans (HR=0.20, P=0.032) were significantly associated with disease-free survival while PS, vp and ve were not. In multivariate analyses adjusting for clinicopathologic variables, Fp and Ktrans significantly increased the accuracy of survival predictions (P=0.0089).

CONCLUSIONS

The prognostic value of Ktrans in cervical cancer patients treated with chemoradiotherapy is explained by microvascular plasma flow (Fp) rather than vessel permeability surface-area product (PS).

Workflow sensitivity of post-processing methods in renal DCE-MRI

OBJECTIVE

Estimation of renal filtration using dynamic contrast-enhanced imaging (DCE-MRI) requires a series of analysis steps. The possible number of distinct post-processing chains is large and grows rapidly with increasing number of processing steps or options. In this study we introduce a framework for systematic evaluation of the post-processing chains. The framework is later used to highlight the workflow processing chain sensitivity towards accuracy in estimation of glomerular filtration rate (GFR).

METHODS

Twenty healthy volunteers underwent DCE-MRI examinations as well as iohexol clearance for reference GFR measurements. In total, 692 different combinations of post-processing steps were explored for analysis, including options for kidney segmentation, B1 inhomogeneity correction, placement of arterial input function, gadolinium concentration estimation as well as handling of motion-corrupted volumes and breathing motion. The evaluation of various processing chains is presented using a classification tree framework and random forest ensemble learning.

RESULTS

Among the processing steps subject to testing, methods for calculating the gadolinium concentration as well as B1 inhomogeneity correction had the largest impact on accuracy of GFR estimations. Different segmentation methods did not play an important role in the post-processing of the MR data except from one processing chain where the automated segmentation outperformed the manual segmentation.

CONCLUSION

The proposed classification trees were efficiently used as a statistical tool for visualization and communication of results to distinguish between important and less influential processing steps in renal DCE-MRI. We also identified several crucial factors in the processing chain.

Correlation between quantitative and semiquantitative parameters in DCE-MRI with a blood pool agent in rectal cancer: can semiquantitative parameters be used as a surrogate for quantitative parameters?

PURPOSE

The aim of this study was to assess correlation between quantitative and semiquantitative parameters in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in rectal cancer patients, both in a primary staging and restaging setting.

MATERIALS AND METHODS

Nineteen patients were included with DCE-MRI before and/or after neoadjuvant therapy. DCE-MRI was performed with gadofosveset trisodium (Ablavar^®, Lantheus Medical Imaging, North Billerica, Massachusetts, USA). Regions of interest were placed in the tumor and quantitative parameters were extracted with Olea Sphere 2.2 software permeability module using the extended Tofts model. Semiquantitative parameters were calculated on a pixel-by-pixel basis. Spearman rank correlation tests were used for assessment of correlation between parameters. A p value ≤0.05 was considered statistically significant.

RESULTS

Strong positive correlations were found between mean peak enhancement and mean K _trans: 0.79 (all patients, p<0.0001), 0.83 (primary staging, p = 0.003), and 0.81 (restaging, p = 0.054). Mean wash-in correlated significantly with mean V _p and K _ep (0.79 and 0.58, respectively, p<0.0001 and p = 0.009) in all patients. Mean wash-in showed a significant correlation with mean K _ep (0.67, p = 0.033) in the primary staging group. On the restaging MRI, mean wash-in only strongly correlated with mean V _p (0.81, p = 0.054).

CONCLUSION

This study shows a strong correlation between quantitative and semiquantitative parameters in DCE-MRI for rectal cancer. Peak enhancement correlates strongly with K _trans and wash-in showed strong correlation with V _p and K _ep. These parameters have been reported to predict tumor aggressiveness and response in rectal cancer. Therefore, semiquantitative analyses might be a surrogate for quantitative analyses.

Comparison of DCE-MRI kinetic parameters and FMISO-PET uptake parameters in head and neck cancer patients

Purpose

Tumor hypoxia is a major cause of radiation resistance, often present in various solid tumors. Dynamic [¹⁸F]‐fluoromisonidazole (FMISO) PET imaging is able to reliably assess tumor hypoxia. Comprehensive characterization of tumor microenvironment through FMISO‐PET and dynamic contrast enhanced (DCE) MR multimodality imaging might be a valuable alternative to the dynamic FMISO‐PET acquisition. The aim of this work was to explore the correlation between the FMISO‐PET and DCE‐MRI kinetic parameters.

Methods

This study was done on head and neck cancer patients (N = 6), who were imaged dynamically with FMISO‐PET and DCE‐MRI on the same day. Images were registered and analyzed for kinetics on a voxel basis. FMISO‐PET images were analyzed with the two‐tissue compartment three rate‐constant model. Additionally, tumor‐to‐muscle ratio (TMR) maps were evaluated. DCE‐MRI was analyzed with the extended Tofts model. Voxel‐wise Pearson's coefficients were calculated for each patient to assess pairwise parameter correlations.

Results

Median correlations between FMISO uptake parameters and DCE‐MRI kinetic parameters varied across the parameter pairs in the range from −0.05 to 0.71. The highest median correlation of r = 0.71 was observed for the pair V_b−v_p, while the K₁−K^trans median correlation was r = 0.45. Median correlation coefficients for the K₁−v_p and the K_i−K^trans pairs were r = 0.42 and r = 0.32, respectively. Correlations between FMISO uptake rate parameter K_i and DCE‐MRI kinetic parameters varied substantially across the patients, whereas correlations between the FMISO and DCE‐MRI vascular parameters were consistently high. Median TMR‐K₁ and TMR‐K^trans correlations were r = 0.52 and r = 0.46, respectively, but varied substantially across the patients.

Conclusions

Based on this clinical evidence, we can conclude that the vascular fraction parameters obtained through DCE‐MRI kinetic analysis or FMISO kinetic analysis measure the same biological property, while other kinetic parameters are unrelated. These results might be useful in the design of future clinical trials involving FMISO‐PET/DCE‐MR multimodality imaging for the assessment of tumor microenvironment.

Biopsy targeting with dynamic contrast-enhanced versus standard neuronavigation MRI in glioma: a prospective double-blinded evaluation of selection benefits

Current biopsy planning based on contrast-enhanced T1W (CET1W) or FLAIR sequences frequently delivers biopsy samples that are not in concordance with the gross tumor diagnosis. This study investigates whether the quantitative information of transfer constant K^trans maps derived from T1W dynamic contrast-enhanced MRI (DCE-MRI) can help enhance the quality of biopsy target selection in glioma. 28 patients with suspected glioma received MRI including DCE-MRI and a standard neuronavigation protocol of 3D FLAIR- and CET1W data sets (0.1 mmol/kg gadobutrol) at 3.0 T. After exclusion of five cases with no K^trans-elevation, 2-6 biopsy targets were independently selected by a neurosurgeon (samples based on standard imaging) and a neuroradiologist (samples based on kinetic parameter K^trans) per case and tissue samples corresponding to these targets were collected by a separate independent neurosurgeon. Standard technique and K^trans-based samples were rated for diagnostic concordance with the gross tumor resection reference diagnosis (67 WHO IV; 24 WHO III and II) by a neuropathologist blinded for selection mode. K^trans-based sample targets differed from standard technique sample targets in 90/91 cases. More K^trans-based than standard imaging-based samples could be extracted. Diagnoses from K^trans-based samples were more frequently concordant with the reference gross tumor diagnoses than those from standard imaging-based samples (WHO IV: 30/39 vs. 11/20; p = 0.08; WHO III/II: 12/13 vs. 6/11; p = 0.06). In 4/5 non-contrast-enhancing gliomas, K^trans-based selection revealed significantly more accurate samples than standard technique sample-selection (10/12 vs. 2/8 samples; p = 0.02). If K^trans elevation is present, K^trans-based biopsy targeting provides significantly more diagnostic tissue samples in non-contrast-enhancing glioma than selection based on CET1W and FLAIR-weighted images alone.

Clinical Applications of Contrast-Enhanced Perfusion MRI Techniques in Gliomas: Recent Advances and Current Challenges

Gliomas possess complex and heterogeneous vasculatures with abnormal hemodynamics. Despite considerable advances in diagnostic and therapeutic techniques for improving tumor management and patient care in recent years, the prognosis of malignant gliomas remains dismal. Perfusion-weighted magnetic resonance imaging techniques that could noninvasively provide superior information on vascular functionality have attracted much attention for evaluating brain tumors. However, nonconsensus imaging protocols and postprocessing analysis among different institutions impede their integration into standard-of-care imaging in clinic. And there have been very few studies providing a comprehensive evidence-based and systematic summary. This review first outlines the status of glioma theranostics and tumor-associated vascular pathology and then presents an overview of the principles of dynamic contrast-enhanced MRI (DCE-MRI) and dynamic susceptibility contrast-MRI (DSC-MRI), with emphasis on their recent clinical applications in gliomas including tumor grading, identification of molecular characteristics, differentiation of glioma from other brain tumors, treatment response assessment, and predicting prognosis. Current challenges and future perspectives are also highlighted.

Multiresolution imaging using golden angle stack-of-stars and compressed sensing for dynamic MR urography

PURPOSE

To develop a novel multiresolution MRI methodology for accurate estimation of glomerular filtration rate (GFR) in vivo.

MATERIALS AND METHODS

A three-dimensional golden-angle radial stack-of-stars (SoS) trajectory was used for data acquisition on a 3 Tesla MRI scanner. Multiresolution reconstruction and analysis was performed using arterial input function reconstructed at 1-s. temporal resolution and renal dynamic data reconstructed using compressed sensing (CS) with 4-s temporal resolution. The method was first validated using simulations and the clinical utility of the technique was evaluated by comparing the GFR estimates from the proposed method to the estimated GFR (eGFR) obtained from serum creatinine for 10 subjects.

RESULTS

The 4-s temporal resolution CS images minimized streaking artifacts and noise while the 1-s temporal resolution AIF minimized errors in GFR estimates. A paired t-test showed that there was no statistically significant difference between MRI based total GFR values and serum creatinine based eGFR estimates (P = 0.92).

CONCLUSION

We have demonstrated the feasibility of multiresolution MRI using a golden angle radial stack-of-stars scheme to accurately estimate GFR as well as produce diagnostic quality dynamic images in vivo.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:303-311.

High-resolution whole-brain DCE-MRI using constrained reconstruction: Prospective clinical evaluation in brain tumor patients

PURPOSE

To clinically evaluate a highly accelerated T1-weighted dynamic contrast-enhanced (DCE) MRI technique that provides high spatial resolution and whole-brain coverage via undersampling and constrained reconstruction with multiple sparsity constraints.

METHODS

Conventional (rate-2 SENSE) and experimental DCE-MRI (rate-30) scans were performed 20 minutes apart in 15 brain tumor patients. The conventional clinical DCE-MRI had voxel dimensions 0.9 × 1.3 × 7.0 mm(3), FOV 22 × 22 × 4.2 cm(3), and the experimental DCE-MRI had voxel dimensions 0.9 × 0.9 × 1.9 mm(3), and broader coverage 22 × 22 × 19 cm(3). Temporal resolution was 5 s for both protocols. Time-resolved images and blood-brain barrier permeability maps were qualitatively evaluated by two radiologists.

RESULTS

The experimental DCE-MRI scans showed no loss of qualitative information in any of the cases, while achieving substantially higher spatial resolution and whole-brain spatial coverage. Average qualitative scores (from 0 to 3) were 2.1 for the experimental scans and 1.1 for the conventional clinical scans.

CONCLUSIONS

The proposed DCE-MRI approach provides clinically superior image quality with higher spatial resolution and coverage than currently available approaches. These advantages may allow comprehensive permeability mapping in the brain, which is especially valuable in the setting of large lesions or multiple lesions spread throughout the brain.