Suitability of pharmacokinetic models for dynamic contrast-enhanced MRI of abdominal aortic aneurysm vessel wall: a comparison

Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements

DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.

Dynamic Contrast-Enhanced MRI in Abdominal Aortic Aneurysms as a Potential Marker for Disease Progression

BACKGROUND

Abdominal aortic aneurysms (AAAs) may rupture before reaching maximum diameter (Dmax ) thresholds for repair. Aortic wall microvasculature has been associated with elastin content and rupture sites in specimens, but its relation to progression is unknown.

PURPOSE

To investigate whether dynamic contrast-enhanced (DCE) MRI of AAA is associated with Dmax or growth.

STUDY TYPE

Prospective.

POPULATION

A total of 27 male patients with infrarenal AAA (mean age ± standard deviation = 75 ± 5 years) under surveillance with DCE MRI and 2 years of prior follow-up intervals with computed tomography (CT) or MRI.

FIELD STRENGTH/SEQUENCE

A 3-T, dynamic three-dimensional (3D) fast gradient-echo stack-of-stars volumetric interpolated breath-hold examination (Star-VIBE).

ASSESSMENT

Wall voxels were manually segmented in two consecutive slices at the level of Dmax . We measured slope to 1-minute and area under the curve (AUC) to 1 minute and 4 minutes of the signal intensity change postcontrast relative to that precontrast arrival, and, Ktrans , a measure of microvascular permeability, using the Patlak model. These were averaged over all wall voxels for association to Dmax and growth rate, and, over left/right and anterior/posterior quadrants for testing circumferential homogeneity. Dmax was measured orthogonal to the aortic centerline and growth rate was calculated by linear fit of Dmax measurements.

STATISTICAL TESTS

Pearson correlation and linear mixed effects models. A P value <0.05 was considered statistically significant.

RESULTS

In 44 DCE MRIs, mean Dmax was 45 ± 7 mm and growth rate in 1.5 ± 0.4 years of prior follow-up was 1.7 ± 1.2 mm per year. DCE measurements correlated with each other (Pearson r = 0.39-0.99) and significantly differed between anterior/posterior versus left/right quadrants. DCE measurements were not significantly associated with Dmax (P = 0.084, 0.289, 0.054 and 0.255 for slope, AUC at 1 minute and 4 minutes, and Ktrans , respectively). Slope and 4 minutes AUC significantly associated with growth rate after controlling for Dmax .

CONCLUSION

Contrast uptake may be increased in lateral aspects of the AAA. Contrast enhancement 1-minute slope and 4-minutes AUC may be associated with a period of recent AAA growth that is independent of Dmax .

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

An automatic framework for evaluating the vascular permeability of bone metastases from prostate cancer

Objectives.Vascular permeability can reflect tumorigenesis and metastasis. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess microvascular permeability by pharmacokinetic parameter estimation. Most estimation methods require manually selected arterial input function (AIF) or reference regions. However, the result will be unstable due to the annotation, which relies on personal experience. In this study, we propose an automatic framework for evaluating vascular permeability of bone metastases from prostate cancer without selecting AIF.Materials and methods.This retrospective study comprised of 15 prostate cancer patients with bone metastases. Based on clinical consensus for three typical DCE-MRI curve patterns, three characteristic curves as regularization constraints were introduced to the extended Tofts model (ETM) using clustering strategy, and the clustering-based blind identification of multichannel (CBM) framework was then proposed for pharmacokinetic parameter estimation. With automatic segmentation of the whole bone area, we obtained the estimation of the pharmacokinetic parameters in the bone area and quantified for bone metastases. Two experienced radiologists compared the CBM estimations with the diagnostic results and we compared the estimations with those of the ETM in bone metastasis regions to evaluate the feasibility of the CBM framework.Results.The higher signal regions ofK^transandK_epindicated the metastasis of prostate cancer, which is consistent with the cancer area marked by the radiologists. In addition, theK^transandK_epin bone metastasis regions were significantly higher than in normal bone regions (P < 0.001,P < 0.001). The consistency of estimation by using the CBM framework and conventional ETM method was confirmed by Bland-Altman analysis.Conclusion.The proposed CBM framework can provide a fully automatic and reliable quantitative estimation of vascular permeability for bone metastases in prostate cancer patients.

In vivo noninvasive preclinical tumor hypoxia imaging methods: a review

Tumors exhibit areas of decreased oxygenation due to malformed blood vessels. This low oxygen concentration decreases the effectiveness of radiation therapy, and the resulting poor perfusion can prevent drugs from reaching areas of the tumor. Tumor hypoxia is associated with poorer prognosis and disease progression, and is therefore of interest to preclinical researchers. Although there are multiple different ways to measure tumor hypoxia and related factors, there is no standard for quantifying spatial and temporal tumor hypoxia distributions in preclinical research or in the clinic. This review compares imaging methods utilized for the purpose of assessing spatio-temporal patterns of hypoxia in the preclinical setting. Imaging methods provide varying levels of spatial and temporal resolution regarding different aspects of hypoxia, and with varying advantages and disadvantages. The choice of modality requires consideration of the specific experimental model, the nature of the required characterization and the availability of complementary modalities as well as immunohistochemistry.

Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study

We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.

Dynamic Contrast-Enhanced MRI to Study Atherosclerotic Plaque Microvasculature

Rupture of a vulnerable atherosclerotic plaque of the carotid artery is an important underlying cause of clinical ischemic events, such as stroke. Abundant microvasculature has been identified as an important aspect contributing to plaque vulnerability. Plaque microvasculature can be studied non-invasively with dynamic contrast-enhanced (DCE-)MRI in animals and patients. In recent years, several DCE-MRI studies have been published evaluating the association between microvasculature and other key features of plaque vulnerability (e.g., inflammation and intraplaque hemorrhage), as well as the effects of novel therapeutic interventions. The present paper reviews this literature, focusing on DCE-MRI methods of acquisition and analysis of atherosclerotic plaques, the current state and future potential of DCE-MRI in the evaluation of plaque microvasculature in clinical and preclinical settings.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

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

Three-dimensional dynamic contrast-enhanced MRI for the accurate, extensive quantification of microvascular permeability in atherosclerotic plaques

Atherosclerotic plaques that cause stroke and myocardial infarction are characterized by increased microvascular permeability and inflammation. Dynamic contrast-enhanced MRI (DCE-MRI) has been proposed as a method to quantify vessel wall microvascular permeability in vivo. Until now, most DCE-MRI studies of atherosclerosis have been limited to two-dimensional (2D) multi-slice imaging. Although providing the high spatial resolution required to image the arterial vessel wall, these approaches do not allow the quantification of plaque permeability with extensive anatomical coverage, an essential feature when imaging heterogeneous diseases, such as atherosclerosis. To our knowledge, we present the first systematic evaluation of three-dimensional (3D), high-resolution, DCE-MRI for the extensive quantification of plaque permeability along an entire vascular bed, with validation in atherosclerotic rabbits. We compare two acquisitions: 3D turbo field echo (TFE) with motion-sensitized-driven equilibrium (MSDE) preparation and 3D turbo spin echo (TSE). We find 3D TFE DCE-MRI to be superior to 3D TSE DCE-MRI in terms of temporal stability metrics. Both sequences show good intra- and inter-observer reliability, and significant correlation with ex vivo permeability measurements by Evans Blue near-infrared fluorescence (NIRF). In addition, we explore the feasibility of using compressed sensing to accelerate 3D DCE-MRI of atherosclerosis, to improve its temporal resolution and therefore the accuracy of permeability quantification. Using retrospective under-sampling and reconstructions, we show that compressed sensing alone may allow the acceleration of 3D DCE-MRI by up to four-fold. We anticipate that the development of high-spatial-resolution 3D DCE-MRI with prospective compressed sensing acceleration may allow for the more accurate and extensive quantification of atherosclerotic plaque permeability along an entire vascular bed. We foresee that this approach may allow for the comprehensive and accurate evaluation of plaque permeability in patients, and may be a useful tool to assess the therapeutic response to approved and novel drugs for cardiovascular disease.

Visualization, imaging and new preclinical diagnostics in radiation oncology

Innovative strategies in cancer radiotherapy are stimulated by the growing knowledge on cellular and molecular tumor biology, tumor pathophysiology, and tumor microenvironment. In terms of tumor diagnostics and therapy monitoring, the reliable delineation of tumor boundaries and the assessment of tumor heterogeneity are increasingly complemented by the non-invasive characterization of functional and molecular processes, moving preclinical and clinical imaging from solely assessing tumor morphology towards the visualization of physiological and pathophysiological processes. Functional and molecular imaging techniques allow for the non-invasive characterization of tissues in vivo, using different modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET) and optical imaging (OI). With novel therapeutic concepts combining optimized radiotherapy with molecularly targeted agents focusing on tumor cell proliferation, angiogenesis, and cell death, the non-invasive assessment of tumor microcirculation and tissue water diffusion, together with strategies for imaging the mechanisms of cellular injury and repair is of particular interest. Characterizing the tumor microenvironment prior to and in response to irradiation will help to optimize the outcome of radiotherapy. These novel concepts of personalized multi-modal cancer therapy require careful pre-treatment stratification as well as a timely and efficient therapy monitoring to maximize patient benefit on an individual basis. Functional and molecular imaging techniques are key in this regard to open novel opportunities for exploring and understanding the underlying mechanisms with the perspective to optimize therapeutic concepts and translate them into a personalized form of radiotherapy in the near future.