Xcat Phantom
Alice Palecek
The site is secure.
The ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
Methods: Highly detailed whole-body anatomies for the adult male and female were defined in the XCAT using nonuniform rational B-spline (NURBS) and subdivision surfaces based on segmentation of the Visible Male and Female anatomical datasets from the National Library of Medicine as well as patient datasets. Using the flexibility of these surfaces, the Visible Human anatomies were transformed to match body measurements and organ volumes for a 50th percentile (height and weight) male and female. The desired body measurements for the models were obtained using the PEOPLESIZE program that contains anthropometric dimensions categorized from 1st to the 99th percentile for US adults. The desired organ volumes were determined from ICRP Publication 89 [ICRP, "Basic anatomical and physiological data for use in radiological protection: reference values," ICRP Publication 89 (International Commission on Radiological Protection, New York, NY, 2002)]. The male and female anatomies serve as standard templates upon which anatomical variations may be modeled in the XCAT through user-defined parameters. Parametrized models for the cardiac and respiratory motions were also incorporated into the XCAT based on high-resolution cardiac- and respiratory-gated multislice CT data. To demonstrate the usefulness of the phantom, the authors show example simulation studies in PET, SPECT, and CT using publicly available simulation packages.
Results: As demonstrated in the pilot studies, the 4D XCAT (which includes thousands of anatomical structures) can produce realistic imaging data when combined with accurate models of the imaging process. With the flexibility of the NURBS surface primitives, any number of different anatomies, cardiac or respiratory motions or patterns, and spatial resolutions can be simulated to perform imaging research.
Conclusions: With the ability to produce realistic, predictive 3D and 4D imaging data from populations of normal and abnormal patients under various imaging parameters, the authors conclude that the XCAT provides an important tool in imaging research to evaluate and improve imaging devices and techniques. In the field of x-ray CT, the phantom may also provide the necessary foundation with which to optimize clinical CT applications in terms of image quality versus radiation dose, an area of research that is becoming more significant with the growing use of CT.
Standardised performance assessment of image acquisition, reconstruction and processing methods is limited by the absence of images paired with ground truth reference values. To this end, we propose MRXCAT2.0 to generate synthetic data, covering healthy and pathological function, using a biophysical model. We exemplify the approach by generating cardiovascular magnetic resonance (CMR) images of healthy, infarcted, dilated and hypertrophic left-ventricular (LV) function.
In MRXCAT2.0, the XCAT torso phantom is coupled with a statistical shape model, describing population (patho)physiological variability, and a biophysical model, providing known and detailed functional ground truth of LV morphology and function. CMR balanced steady-state free precession images are generated using MRXCAT2.0 while realistic image appearance is ensured by assigning texturized tissue properties to the phantom labels.
MRXCAT2.0 offers synthesis of realistic images embedding population-based anatomical and functional variability and associated ground truth parameters to facilitate a standardized assessment of CMR acquisition, reconstruction and processing methods.
In-silico phantoms of human cardiovascular anatomy and function provide a versatile tool for the testing and validation of image acquisition, reconstruction and post-processing strategies in cardiovascular magnetic resonance (CMR) [1]. Producing synthetic images from a phantom has the benefit that the resulting images have corresponding anatomical labels and functional ground-truth data, which are useful for the evaluation of the performance of a CMR pipeline. For example, the availability of a paired image-ground truth dataset is essential for a standardized evaluation of image processing tools, such as those for automatic left-ventricular (LV) segmentation, shape and strain analysis.
Available phantoms can be classified into three categories: voxel-based, analytical and hybrid. Voxel-based phantoms consist of labeled voxelised anatomical representations obtained from patients [2, 3]. These are realistic, but do not generalize to population statistics and pathological cases [4]. Analytical phantoms are based on a mathematical description of tissue structures [5]. While they are less realistic, they are more flexible in terms of definition of anatomical variations. Hybrid phantoms have been proposed to overcome the limitations of the previous two categories [6]. Although hybrid and analytical phantoms allow for morphological variation, anatomical and functional variability is mostly limited to healthy cases and function. Veress et al. [7] proposed to couple a hybrid phantom to a biophysical model of the LV to simulate both healthy and infarct conditions. However, as stated by the authors, the fitting process is time consuming and it cannot account for other pathological scenarios, such as cardiomyopathy. More recently, Segars et al. [8] proposed a methodology to couple a full heart functional model to the XCAT phantom. While this allows to simulate realistic cardiac function, it is specific to XCAT and it cannot be rapidly deployed to general pathological cases.
In the last years, solutions based on shape models (SM) (with voxelised or mesh representations) have been proposed to address the need for expressive anatomical descriptions [9,10,11,12,13]. While these works have shown the capability of representing dominant LV anatomical features, they did not focus on the definition of a sampling strategy to generate synthetic anatomies to capture population variability, including both healthy and pathological conditions.
Given an in-silico phantom, two main methodologies for generating CMR images can be identified. In the first approach, the signal is generated using numerical solutions of the physical equations (Bloch equations). This has been applied for cardiac and brain image synthesis [14,15,16,17,18]. In [1, 19] the use of signal models for specific sequences of interest has been proposed to compute the resulting image data. In [20] a dataset for a virtual population with varying acquisition parameters was generated using MRXCAT [1] and used to pre-train a segmentation network, which was subsequently fine-tuned on real images. This approach greatly reduces the amount of in-vivo images required. However, the segmentation performance degraded when there was no fine-tuning on real data as the simulated images were not completely realistic. In [16, 21, 22] it has been shown that synthetic images can be used to augment, and eventually replace, in-vivo datasets for training of neural networks, making realistic image synthesis an important tool for CMR development.
Alternative generative approaches consist of using neural networks for conditional synthesis or style transfer [23,24,25,26,27,28,29,30]. They have been used for several imaging modalities such as ultrasound [31], computed tomography [26] and magnetic resonance imaging [23,24,25, 32]. The reader is referred to [33] for a recent overview of medical image synthesis.
In [26, 34] a factorised representation of images has been proposed, composed of a spatial representation of the anatomy combined with a modality description. The latter describes how tissue structures are rendered in the image. However, the network cannot be used to generate new anatomies and it requires labelled images for training, which are costly to obtain. In [23] unlabelled CMR images were used to learn a multi-tissue anatomical model which was fit to variable anatomies by a learned deformation model. The anatomical model was then used to condition a SPADE-GAN [35] to synthesise an image volume. While this approach solved both issues of the two previous factorised representation learning approaches [26, 34], the anatomical model learnt using the network does not represent conventional tissue classes and is thus not suited as anatomical ground truth. In [24], the XCAT phantom was used as anatomical ground truth semantic labels and MR images were synthesized using a SPADE-GAN. In [36], DatasetGAN, leveraging the generator features of StyleGAN [37], was proposed to produce a large synthetic dataset of images and to also predict pixel-wise class labels. The evaluation of this method has demonstrated that a segmentation network trained with datasets from DatasetGAN outperforms previous semi-supervised methods and is on par with the same network trained fully-supervised on a real dataset. Similarly, SemanticGAN [38] was developed to simultaneously generate both synthetic images and corresponding segmentation labels using StyleGAN2.
While physics-based approaches allow for better control over the parameters related to image generation with respect to style transfer approaches, they produce less realistic appearance. In [39] intra-organ texture for bones and organs was proposed to improve the realism of images generated with signal models. This approach, however, has not yet been applied to CMR image synthesis.
The present work proposes MRXCAT2.0 to address the two main limitations of in-silico phantoms: reduced variability and lack of realism. Realistic LV anatomy and function are generated by coupling a statistical shape description with a biophysical model. Surrounding tissue structures are generated with the XCAT model. Tissue maps of proton density (PD), longitudinal and transverse relaxation times (T1, T2) are assigned to image labels using a neural network trained to maximize the similarity of the background with the target appearance of real CMR images. Synthetic images are then generated using MRXCAT2.0 and used to assess the performance of published CMR processing methods [40, 41] against known ground truth of healthy and pathological cardiac function as a use case.
c80f0f1006