KEYWORDS: Visualization, Anatomy, Tumors, Visual process modeling, Voxels, Data modeling, 3D modeling, Process modeling, Cameras, Tumor growth modeling
While there is increased interest in medical and scientific computational modeling tools for generating in silico medical datasets, tools for visualizing the volumetric data present with hurdles for those without previous experience in graphics rendering. We describe an open-source automatedworkflowto visualize volumetric computational medical imaging datasets with a focus on cancer lesion growth models. Simulated raw data for the growth of a tumor were generated at 50 time points using a previously described growth algorithm that considers the surrounding anatomy to affect tumor morphology. The voxelized models were converted to the VDB volume format for rendering using an automated Python script within the software Houdini. The visualization of volume data allows for detailed inspection and improved understanding of the spatial configuration of the tumor and surrounding anatomy affecting the growth.
KEYWORDS: Digital breast tomosynthesis, Tumor growth modeling, Cancer detection, Breast, Breast density, 3D modeling, Tumors, Breast cancer, Cancer, X-rays
We describe a longitudinal in silico imaging trial investigating the advantages of digital breast tomosynthesis (DBT) versus digital mammography (DM) for early detection of breast cancer. To mimic cancer progression, we used a previously developed computational model based on biological and physiological phenomena accounting for rate of metabolic nutrients and cellular waste as well as the stiffness of surrounding anatomical structures affecting lesion morphology. We integrated this model with the VICTRE pipeline to create a cohort of in silico patients each with a unique manifestation of cancer recorded at 5 stages of progression. Digital patients with varying breast densities were considered. A customized version of the VICTRE pipeline was used to simulate DM and DBT imaging of patients with an in silico version of the Siemens Mammomat Inspiration system with image interpretation under a location-known-exactly tasks, relying on 2D/3D algorithmic readers previously described. We analyzed the area under the ROC curve (AUC) for both imaging modalities at the 5 stages of cancer growth to evaluate the performance of DBT and DM along the life of the tumor. Our findings suggest that DBT outperforms DM for all lesion sizes, which is consistent with studies reported in literature. We observed the mean AUCs increases from 0.64 to 0.80 (p < 0.001) forDMand from 0.66 to 0.88 (p < 0.001) for DBT as lesion size increased from 0.37 to 1.8 mm. These results suggest a potential benefit of DBT as compared to DM for the detection of small masses at earlier stages of tumor development. The in silico trial we designed allowed for studying the progression of detectability of masses at different growing stages, something that would be costly and ethically questionable with a human clinical trial.
KEYWORDS: Tumors, Systems modeling, Digital breast tomosynthesis, Breast, Tumor growth modeling, Breast cancer, Tissues, Electroluminescent displays, Digital mammography
Digital breast tomosynthesis (DBT) can improve the detectability of breast cancer by eliminating overlapping breast tissues that affects the performance of digital mammography (DM) systems. It is yet to be established if DBT can detect lesions at earlier progression stages than DM. To pursue this investigation using in-silico methods, it is necessary to develop computational models that mimic the growth of cancerous lesions. We report on a novel computational model that mimics the progression of breast tumors based on underlying biological and physiological phenomena. Our model includes anisotropic growth and irregularly shaped lesions commonly seen in breast cancer. Our method relies on the assumption that tumor shape is ultimately determined by pressure fields given by surrounding anatomical structures causing the lesion to preferentially proliferate in certain directions. By varying the direction of tumor growth via pressure maps, we simulated various anisotropic lesions seen in clinical cases. We used the open-source, freely available VICTRE imaging pipeline to obtain DM images of growing lesions within breast models and depict several time points in the growth of the tumor as seen by this imaging modality.
Digital Breast Tomosynthesis (DBT) improves the visibility of cancerous lesions as compared to 2D full-field digital mammography (FFDM) by removing the overlap in breast tissues. An integral and computationally demanding part of the DBT image acquisition process is the reconstruction of the volume from projections. To facilitate further research towards improving DBT technology, it is essential to have access to image reconstruction software that generates volumes within a reasonable amount of time. We have developed an open source version of the filtered back-projection (FBP) reconstruction algorithm for DBT using single-threaded C. This is an extention to the C codes developed by Leeser et al. for cone-beam computed tomography (CBCT) reconstruction. For each projection angle, the DBT projection view was interpolated to create an estimation of the corresponding CT projection view for that angle. The estimated CT projection views were then filtered and backprojected to generate the DBT volume. We tested our implementation using mathematical and anatomical phantom data and compared the results with a previously verified MATLAB implementation. We observed negligible relative differences between the DBT reconstruction by both methods with a considerable increase (up to 9 times faster) in speed over the MATLAB code.
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