Bone is a highly vascularized tissue and efficient bone regeneration requires neovascularization, especially for critical-sized bone defects. We developed a novel hybrid biomaterial comprising nanocalcium sulfate (nCS) and fibrin hydrogel to deliver mesenchymal stem cells (MSCs) and angiogenic factors, vascular endothelial growth factor (VEGF) and fibroblast growth factor 9 (FGF9), to promote neovascularization and bone formation. MSC and growth factor(s)-loaded scaffolds were implanted subcutaneously into mice to examine their angiogenic and osteogenic potential. Micro CT, alkaline phosphatase activity assay, and histological analysis were used to evaluate bone formation, while immunohistochemistry was employed to assess neovessel formation. The presence of fibrin preserved the nCS scaffold structure and promoted de novo bone formation. In addition, the presence of bone morphogenic protein 2-expressing MSC in nCS and fibrin hydrogels improved bone regeneration significantly. While FGF9 alone had no significant effect, the combination FGF9 and VEGF conjugated in fibrin enhanced neovascularization and bone formation more than 4-fold compared to nCS with MSC. Overall, our results suggested that the combination of nCS (to support bone formation) with a fibrin-based VEGF/FGF9 release system (support vascular formation) is an innovative and effective strategy that significantly enhanced ectopic bone formation in vivo.

High-resolution 3D bone-tissue structure measurements may provide information critical to the understanding of the bone regeneration processes and to the bone strength assessment. Tissue engineering studies rely on such nondestructive measurements to monitor bone graft regeneration area. In this study, we measured bone yield, fractal dimension and trabecular thickness through micro-CT slices for different grafts and controls. Eight canines underwent surgery to remove a bone volume (defect) in the canine’s jaw at a total of 44 different locations. We kept 11 defects empty for control and filled the remaining ones with three regenerative materials; NanoGen (NG), a FDA-approved material (n=11), a novel NanoCalcium Sulfate (NCS) material (n=11) and NCS alginate (NCS+alg) material (n=11). After a minimum of four and eight weeks, the canines were sacrificed and the jaw samples were extracted. We used a custom-built micro-CT system to acquire the data volume and developed software to measure the bone yield, fractal dimension and trabecular thickness. The software used a segmentation algorithm based on histograms derived from volumes of interest indicated by the operator. Using bone yield and fractal dimension as indices we are able to differentiate between the control and regenerative material (p<0.005). Regenerative material NCS showed an average 63.15% bone yield improvement over the control sample, NCS+alg showed 55.55% and NanoGen showed 37.5%. The bone regeneration process and quality of bone were dependent upon the position of defect and time period of healing. This study presents one of the first quantitative comparisons using non-destructive Micro-CT analysis for bone regenerative material in a large animal with a critical defect model. Our results indicate that Micro-CT measurement could be used to monitor in-vivo bone regeneration studies for greater regenerative process understanding.

Perfusion imaging is the most applied modality for the assessment of acute stroke. Parameters such as Cerebral Blood Flow (CBF), Cerebral Blood volume (CBV) and Mean Transit Time (MTT) are used to distinguish the tissue infarct core and ischemic penumbra. Due to lack of standardization these parameters vary significantly between vendors and software even when provided with the same data set. There is a critical need to standardize the systems and make them more reliable. We have designed a uniform phantom to test and verify the perfusion systems. We implemented a flow loop with different flow rates (250, 300, 350 ml/min) and injected the same amount of contrast. The images of the phantom were acquired using a Digital Angiographic system. Since this phantom is uniform, projection images obtained using DSA is sufficient for initial validation. To validate the phantom we measured the contrast concentration at three regions of interest (arterial input, venous output, perfused area) and derived time density curves (TDC). We then calculated the maximum slope, area under the TDCs and flow. The maximum slope calculations were linearly increasing with increase in flow rate, the area under the curve decreases with increase in flow rate. There was 25% error between the calculated flow and measured flow. The derived TDCs were clinically relevant and the calculated flow, maximum slope and areas under the curve were sensitive to the measured flow. We have created a systematic way to calibrate existing perfusion systems and assess their reliability.

Purpose: Identifying an appropriate tube current setting can be challenging when using iterative reconstruction due to the varying relationship between spatial resolution, contrast, noise, and dose across different algorithms. This study developed and investigated the application of a generalized detectability index (d 0 gen ) to determine the noise parameter to input to existing automated exposure control (AEC) systems to provide consistent image quality (IQ) across different reconstruction approaches.Methods: This study proposes a task-based automated exposure control (AEC) method using a generalized detectability index (d 0 gen ). The proposed method leverages existing AEC methods that are based on a prescribed noise level. The generalized d 0 gen metric is calculated using lookup tables of task-based modulation transfer function (MTF) and noise power spectrum (NPS). To generate the lookup tables, the American College of Radiology CT accreditation phantom was scanned on a multidetector CT scanner (Revolution CT, GE Healthcare) at 120 kV and tube current varied manually from 20 to 240 mAs. Images were reconstructed using a reference reconstruction algorithm and four levels of an in-house iterative reconstruction algorithm with different regularization strengths (IR1-IR4). The task-based MTF and NPS were estimated from the measured images to create lookup tables of scaling factors that convert between d 0 gen and noise standard deviation. The performance of the proposed d 0 gen -AEC method in providing a desired IQ level over a range of iterative reconstruction algorithms was evaluated using the American College of Radiology (ACR) phantom with elliptical shell and using a human reader evaluation on anthropomorphic phantom images. Results: The study of the ACR phantom with elliptical shell demonstrated reasonable agreement between the d 0 gen predicted by the lookup table and d 0 measured in the images, with a mean absolute error of 15% across all dose levels and maximum error of 45% at the lowest dose level with the elliptical shell. For the anthropomorphic phantom study, the mean reader scores for images resulting from the d 0 gen -AEC method were 3.3 (reference image), 3.5 (IR1), 3.6 (IR2), 3.5 (IR3), and 2.2 (IR4). When using the d 0 gen -AEC method, the observers' IQ scores for the reference reconstruction were statistical equivalent to the scores for IR1, IR2, and IR3 iterative reconstructions (P > 0.35). The d 0 gen -AEC method achieved this equivalent IQ at lower dose for the IR scans compared to the reference scans. Conclusions: A novel AEC method, based on a generalized detectability index, was investigated. The proposed method can be used with some existing AEC systems to derive the tube current profile for iterative reconstruction algorithms. The results provide preliminary evidence that the proposed d 0 gen -AEC can produce similar IQ across different iterative reconstruction approaches at different dose levels.