Correlation of 360-degree Surface Mapping In Vivo Bioluminescence with Multi-Spectral Optoacoustic Tomography in Human Xenograft Tumor Models

Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small-animal imager

BACKGROUND

Computed tomography (CT) generates a three-dimensional rendering that can be used to interrogate a given region or desired structure from any orientation. However, in preclinical research, its deployment remains limited due to relatively high upfront costs. Existing integrated imaging systems that provide merged planar X-ray also dwarfs CT popularity in small laboratories due to their added versatility.

PURPOSE

In this paper, we sought to generate CT-like data using an existing small-animal X-ray imager with a specialized specimen rotation system, or MiSpinner. This setup conforms to the cone-beam CT (CBCT) geometry, which demands high spatial calibration accuracy. Therefore, a simple but robust geometry calibration algorithm is necessary to ensure that the entire imaging system works properly and accurately.

METHODS

Because the rotation system is not permanently affixed, we propose a structure tensor-based two-step online (ST-TSO) geometry calibration algorithm. Specifically, two datasets are needed, namely, calibration and actual measurements. A calibration measurement detects the background of the system forward X-ray projections. A study on the background image reveals the characteristics of the X-ray photon distribution, and thus, provides a reliable estimate of the imaging geometry origin. Actual measurements consisted of an X-ray of the intended object, including possible geometry errors. A comprehensive image processing technique helps to detect spatial misalignment information. Accordingly, the first processing step employs a modified projection matrix-based calibration algorithm to estimate the relevant geometric parameters. Predicted parameters are then fine-tuned in a second processing step by an iterative strategy based on the symmetry property of the sum of projections. Virtual projections calculated from the parameters after two-step processing compensate for the scanning errors and are used for CT reconstruction. Experiments on phantom and mouse imaging data were performed to validate the calibration algorithm.

RESULTS

Once system correction was conducted, CBCT of a CT bar phantom and a cohort of euthanized mice were analyzed. No obvious structure error or spatial artifacts were observed, validating the accuracy of the proposed geometry calibration method. Digital phantom simulation indicated that compared with the preset spatial values, errors in the final estimated parameters could be reduced to 0.05° difference in dominant angle and 0.5-pixel difference in dominant axis bias. The in-plane resolution view of the CT-bar phantom revealed that the resolution approaches 150 μ $\umu$ m.

CONCLUSIONS

A constrained two-step online geometry calibration algorithm has been developed to calibrate an integrated X-ray imaging system, defined by a first-step analytical estimation and a second-step iterative fine-tuning. Test results have validated its accuracy in system correction, thus demonstrating the potential of the described system to be modified and adapted for preclinical research.

Tunable three-dimensional engineered prostate cancer tissues for in vitro recapitulation of heterogeneous in vivo prostate tumor stiffness

In this manuscript we report the establishment and characterization of a three-dimensional in vitro, coculture engineered prostate cancer tissue (EPCaT) disease model based upon and informed by our characterization of in vivo prostate cancer (PCa) xenograft tumor stiffness. In prostate cancer, tissue stiffness is known to impact changes in gene and protein expression, alter therapeutic response, and be positively correlated with an aggressive clinical presentation. To inform an appropriate stiffness range for our in vitro model, PC-3 prostate tumor xenografts were established. Tissue stiffness ranged from 95 to 6,750 Pa. Notably, xenograft cell seeding density significantly impacted tumor stiffness; a two-fold increase in the number of seeded cells not only widened the tissue stiffness range throughout the tumor but also resulted in significant spatial heterogeneity. To fabricate our in vitro EPCaT model, PC-3 castration-resistant prostate cancer cells were co-encapsulated with BJ-5ta fibroblasts within a poly(ethylene glycol)-fibrinogen matrix augmented with excess poly(ethylene glycol)-diacrylate to modulate the matrix mechanical properties. Encapsulated cells temporally remodeled their in vitro microenvironment and enrichment of gene sets associated with tumorigenic progression was observed in response to increased matrix stiffness. Through variation of matrix composition and culture duration, EPCaTs were tuned to mimic the wide range of biomechanical cues provided to PCa cells in vivo; collectively, a range of 50 to 10,000 Pa was achievable. Markedly, this also encompasses published clinical PCa stiffness data. Overall, this study serves to introduce our bioinspired, tunable EPCaT model and provide the foundation for future PCa progression and drug development studies. STATEMENT OF SIGNIFICANCE: The development of cancer models that mimic the native tumor microenvironment (TME) complexities is critical to not only develop effective drugs but also enhance our understanding of disease progression. Here we establish and characterize our 3D in vitro engineered prostate cancer tissue model with tunable matrix stiffness, that is inspired by this study's spatial characterization of in vivo prostate tumor xenograft stiffness. Notably, our model's mimicry of the TME is further augmented by the inclusion of matrix remodeling fibroblasts to introduce cancer-stromal cell-cell interactions. This study addresses a critical unmet need in the field by elucidating the prostate tumor xenograft stiffness range and establishing a foundation for recapitulating the biomechanics of site-of-origin and soft tissue metastatic prostate tumors in vitro.

Estimating the Center of Rotation of Tomographic Imaging Systems with a Limited Number of Projections

For a tomographic imaging system, image reconstruction quality is dependent on the accurate determination of coordinates for the true center of rotation (COR). A significant COR offset error may introduce ringing, streaking, or other artifacts, while smaller error in determining COR may blur the reconstructed image. Well known COR correction techniques including image registration, center of mass calculation, or reconstruction evaluation work well under certain conditions. However, many of these methods do not consider various real-world cases such as a tilted sensor or non-parallel projections. Furthermore, a limited number of projections introduces stripe artifacts into the image reconstruction that interfere with many of these classic COR correction techniques. In this paper, we propose a revised variance-based algorithm to find the correct COR position automatically prior to tomographic reconstruction. The algorithm was tested on both simulated phantoms and acquired datasets, and our results show improved reconstruction accuracy.