A microscopic model of the dose distribution in hepatocellular carcinoma after selective internal radiation therapy.

The dosimetry evaluation for the selective internal radiation therapy is currently performed assuming a uniform activity distribution, which is in contrast with literature findings. A 2D microscopic model of the perfused liver was developed to evaluate the effect of two different 90Y microspheres distributions: i) homogeneous partitioning with the microspheres equally distributed in the perfused liver, and ii) tumor-clustered partitioning where the microspheres distribution is inferred from the patient specific images. METHODS: Two subjects diagnosed with liver cancer were included in this study. For each subject, abdominal CT scans acquired prior to the SIRT and post-treatment 90Y positron emission tomography were considered. Two microspheres partitionings were simulated namely homogeneous and tumor-clustered partitioning. The homogeneous and tumor-clustered partitionings were derived starting from CT images. The microspheres radiation is simulated by means of Russell's law. RESULTS: In homogenous simulations, the dose delivery is uniform in the whole liver while in the tumor-clustered simulations a heterogeneous distribution of the delivered dose is visible with higher values in the tumor regions. In addition, in the tumor-clustered simulation, the delivered dose is higher in the viable tumor than in the necrotic tumor, for all patients. In the tumor-clustered case, the dose delivered in the non-tumoral tissue (NTT) was considerably lower than in the perfused liver. CONCLUSIONS: The model proposed here represents a proof-of-concept for personalized dosimetry assessment based on preoperative CT images.

Towards a patient-specific hepatic arterial modeling for microspheres distribution optimization in SIRT protocol.

Selective internal radiation therapy (SIRT) using Yttrium-90 loaded glass microspheres injected in the hepatic artery is an emerging, minimally invasive therapy of liver cancer. A personalized intervention can lead to high concentration dose in the tumor, while sparing the surrounding parenchyma. We propose a computational model for patient-specific simulation of entire hepatic arterial tree, based on liver, tumors, and arteries segmentation on patient's tomography. Segmentation of hepatic arteries down to a diameter of 0.5 mm is semi-automatically performed on 3D cone-beam CT angiography. The liver and tumors are extracted from CT-scan at portal phase by an active surface method. Once the images are registered through an automatic multimodal registration, extracted data are used to initialize a numerical model simulating liver vascular network. The model creates successive bifurcations from given principal vessels, observing Poiseuille's and matter conservation laws. Simulations provide a coherent reconstruction of global hepatic arterial tree until vessel diameter of 0.05 mm. Microspheres distribution under simple hypotheses is also quantified, depending on injection site. The patient-specific character of this model may allow a personalized numerical approximation of microspheres final distribution, opening the way to clinical optimization of catheter placement for tumor targeting.

In silico modeling of magnetic resonance flow imaging in complex vascular networks.

The paper presents a computational model of magnetic resonance (MR) flow imaging. The model consists of three components. The first component is used to generate complex vascular structures, while the second one provides blood flow characteristics in the generated vascular structures by the lattice Boltzmann method. The third component makes use of the generated vascular structures and flow characteristics to simulate MR flow imaging. To meet computational demands, parallel algorithms are applied in all the components. The proposed approach is verified in three stages. In the first stage, experimental validation is performed by an in vitro phantom. Then, the simulation possibilities of the model are shown. Flow and MR flow imaging in complex vascular structures are presented and evaluated. Finally, the computational performance is tested. Results show that the model is able to reproduce flow behavior in large vascular networks in a relatively short time. Moreover, simulated MR flow images are in accordance with the theoretical considerations and experimental images. The proposed approach is the first such an integrative solution in literature. Moreover, compared to previous works on flow and MR flow imaging, this approach distinguishes itself by its computational efficiency. Such a connection of anatomy, physiology and image formation in a single computer tool could provide an in silico solution to improving our understanding of the processes involved, either considered together or separately.

ViP MRI: virtual phantom magnetic resonance imaging.

OBJECT: The ability to generate reference signals is of great benefit for quantitation of the magnetic resonance (MR) signal. The aim of the present study was to implement a dedicated experimental set-up to generate MR images of virtual phantoms. MATERIALS AND METHODS: Virtual phantoms of a given shape and signal intensity were designed and the k-space representation was generated. A waveform generator converted the k-space lines into a radiofrequency (RF) signal that was transmitted to the MR scanner bore by a dedicated RF coil. The k-space lines of the virtual phantom were played line-by-line in synchronization with the magnetic resonance imaging data acquisition. RESULTS: Virtual phantoms of complex patterns were reproduced well in MR images without the presence of artifacts. Time-series measurements showed a coefficient of variation below 1% for the signal intensity of the virtual phantoms. An excellent linearity (coefficient of determination r (2) = 0.997 as assessed by linear regression) was observed in the signal intensity of virtual phantoms. CONCLUSION: Virtual phantoms represent an attractive alternative to physical phantoms for providing a reference signal. MR images of virtual phantoms were here generated using a stand-alone, independent unit that can be employed with MR scanners from different vendors.

Computational modeling of MR flow imaging by the lattice Boltzmann method and Bloch equation.

In this work, a computational model of magnetic resonance (MR) flow imaging is proposed. The first model component provides fluid dynamics maps by applying the lattice Boltzmann method. The second one uses the flow maps and couples MR imaging (MRI) modeling with a new magnetization transport algorithm based on the Eulerian coordinate approach. MRI modeling is based on the discrete time solution of the Bloch equation by analytical local magnetization transformations (exponential scaling and rotations). Model is validated by comparison of experimental and simulated MR images in two three-dimensional geometries (straight and U-bend tubes) with steady flow under comparable conditions. Two-dimensional geometries, presented in literature, were also tested. In both cases, a good agreement is observed. Quantitative analysis shows in detail the model accuracy. Computational time is noticeably lower to prior works. These results demonstrate that the discrete time solution of Bloch equation coupled with the new magnetization transport algorithm naturally incorporates flow influence in MRI modeling. As a result, in the proposed model, no additional mechanism (unlike in prior works) is needed to consider flow artifacts, which implies its easy extensibility. In combination with its low computational complexity and efficient implementation, the model could have a potential application in study of flow disturbances (in MRI) in various conditions and in different geometries.

Vascular system modeling in parallel environment - distributed and shared memory approaches.

This paper presents two approaches in parallel modeling of vascular system development in internal organs. In the first approach, new parts of tissue are distributed among processors and each processor is responsible for perfusing its assigned parts of tissue to all vascular trees. Communication between processors is accomplished by passing messages, and therefore, this algorithm is perfectly suited for distributed memory architectures. The second approach is designed for shared memory machines. It parallelizes the perfusion process during which individual processing units perform calculations concerning different vascular trees. The experimental results, performed on a computing cluster and multicore machines, show that both algorithms provide a significant speedup.

Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity.

The use of quantitative imaging for the characterization of hepatic tumors in magnetic resonance imaging (MRI) can improve the diagnosis and therefore the treatment of these life-threatening tumors. However, image parameters remain difficult to interpret because they result from a mixture of complex processes related to pathophysiology and to acquisition. These processes occur at variable spatial and temporal scales. We propose a multiscale model of liver dynamic contrast-enhanced (DCE) MRI in order to better understand the tumor complexity in images. Our design couples a model of the organ (tissue and vasculature) with a model of the image acquisition. At the macroscopic scale, vascular trees take a prominent place. Regarding the formation of MRI images, we propose a distributed model of parenchymal biodistribution of extracellular contrast agents. Model parameters can be adapted to simulate the tumor development. The sensitivity of the multiscale model of liver DCE-MRI was studied through observations of the influence of two physiological parameters involved in carcinogenesis (arterial flow and capillary permeability) on its outputs (MRI images at arterial and portal phases). Finally, images were simulated for a set of parameters corresponding to the five stages of hepatocarcinogenesis (from regenerative nodules to poorly differentiated HepatoCellular Carcinoma).

Physiological modeling of tumor-affected renal circulation.

One way of gaining insight into what can be observed in medical images is through physiological modeling. For instance, anatomical and functional modifications occur in the organ during the appearance and the growth of a tumor. Some of these changes concern the vascularization. We propose a computational model of tumor-affected renal circulation that represents the local heterogeneity of different parts of the kidney (cortex, medulla). We present a simulation of vascular modifications related to vessel structure, geometry, density, and blood flow in case of renal cell carcinoma. We also use our model to simulate computed tomography scans of a kidney affected by the renal cell carcinoma, at two acquisition times after injection of a contrast product. This framework, based on a physiological model of the organ and physical model of medical image acquisition, offers an opportunity to help radiologists in their diagnostic tasks. This includes the possibility of linking image descriptors with physiological perturbations and markers of pathological processes.

Coupling texture analysis and physiological modeling for liver dynamic MRI interpretation.

We coupled our physiological model of the liver, to a MRI simulator (SIMRI) in order to find image markers of the tumor growth. Some pathological modifications related to the development of Hepatocellular carcinoma are simulated (flows, permeability, vascular density). Corresponding images simulated at typical acquisition phases (arterial, portal) are compared to real images. The evolution of some textural features with arterial flow is also presented.

A physiologically based pharmacokinetic model of vascular-extravascular exchanges during liver carcinogenesis: application to MRI contrast agents.

The extraction of physiological parameters by non-invasive imaging techniques such as dynamic magnetic resonance imaging (MRI) or positron emission tomography requires a knowledge of molecular distribution and exchange between microvascularization and extravascular tissues. These phenomena not only depend on the physicochemical characteristics of the injected molecules but also the pathophysiological state of the targeted organ. We developed a five-compartment physiologically based pharmacokinetic model focused on hepatic carcinogenesis and MRI contrast agents. This model includes physical characteristics of the contrast agent, dual specific liver supply, microvessel wall properties and transport parameters that are compatible with hepatocarcinoma development. The evolution of concentrations in the five compartments showed significant differences in the distribution of three molecules (differentiated by their diameters and diffusion coefficients ranging, respectively, from 0.9 to 62 nm and from 68.10(-9) to 47.10(-7) cm(2) s(-1)) in simulated regeneration nodules and dysplastic nodules, as well as in medium- and poorly differentiated hepatocarcinoma. These results are in agreement with known vascular modifications such as arterialization that occur during hepatocarcinogenesis. This model can be used to study the pharmacokinetics of contrast agents and consequently to extract parameters that are characteristic of the tumor development (like permeability), after fitting simulated to in vivo data.

Simulation of biphasic CT findings in hepatic cellular carcinoma by a two-level physiological model.

In this paper, we present a two-level physiological model that is able to reflect morphology and function of vascular networks, in clinical images. Our approach results from the combination of a macroscopic model, providing simulation of the growth and pathological modifications of vascular network, and a microvascular model, based on compartmental approach, which simulates blood and contrast medium transfer through capillary walls. The two-level model is applied to generate biphasic computed tomography of hepatocellular carcinoma. A contrast-enhanced sequence of simulated images is acquired, and enhancement curves extracted from normal and tumoral regions are compared to curves obtained from in vivo images. The model offers the potential of finding early indicators of disease in clinical vascular images.

Physiologically based modeling of 3-D vascular networks and CT scan angiography.

In this paper, a model-based approach to medical image analysis is presented. It is aimed at understanding the influence of the physiological (related to tissue) and physical (related to image modality) processes underlying the image content. This methodology is exemplified by modeling first, the liver and its vascular network, and second, the standard computed tomography (CT) scan acquisition. After a brief survey on vascular modeling literature, a new method, aimed at the generation of growing three-dimensional vascular structures perfusing the tissue, is described. A solution is proposed in order to avoid intersections among vessels belonging to arterial and/or venous trees, which are physiologically connected. Then it is shown how the propagation of contrast material leads to simulate time-dependent sequences of enhanced liver CT slices.

Fast algorithm for 3-D vascular tree modeling.

In this short paper, accelerated three-dimensional computer simulations of vascular trees development, preserving physiological and haemodynamic features, are reported. The new computation schemes deal: (i). with the geometrical optimization of each newly created bifurcation; and (ii). with the recalculation of blood pressures and radii of vessels in the whole tree. A significant decrease of the computation time is obtained by replacing the global optimization by the fast updating algorithm allowing more complex structure to be simulated. A comparison between the new algorithms and the previous one is illustrated through the hepatic arterial tree.

Hepatic tumor enhancement in computed tomography: combined models of liver perfusion and dynamic imaging.

The objective of this study is to show how computational modeling can be used to increase our understanding of liver enhancement in dynamic computer tomography. It relies on two models: (1). a vascular model, based on physiological rules, is used to generate the 3D hepatic vascular network; (2). the physical process of CT acquisition allows to synthesize timed-stamped series of images, aimed at tracking the propagation of a contrast material through the vessel network and the parenchyma. The coupled models are used to simulate the enhancement of a hyper-vascular tumor at different acquisition times, showing a maximum conspicuity during the arterial phase.