https://www.selleckchem.com/products/abt-199.html Recent developments in the field of cellular therapeutics have indicated the potential of stem cell injections directly to the spinal cord. Injections require either open surgery or a Magnetic Resonance Imaging (MRI) guided injection. Needle positioning during MRI imaging is a significant hurdle to direct spinal injection, as the small target region and interlaminar space require high positioning accuracy. To improve both the procedure time and positioning accuracy, an MRI guided robotic needle positioning system is developed. The robot uses linear piezoelectric motors to directly drive a parallel plane positioning mechanism. Feedback is provided through MRI during the orientation procedure. Both accuracy and repeatability of the robot are characterized. This system is found to be capable of repeatability below 51μm. Needle endpoint error is limited by imaging modality, but is validated to 156μm. The reported robot and MRI image feedback system is capable of repeatable and accurate needle guide positioning. This high accuracy will result in a significant improvement to the workflow of spinal injection procedures. This high accuracy will result in a significant improvement to the workflow of spinal injection procedures. This work examines the claim made in the literature that the inverse problem associ- ated with image reconstruction in sparse-view computed tomography (CT) can be solved with a convolutional neural network (CNN). Training and testing image/data pairs are gener- ated in a dedicated breast CT simulation for sparse-view sampling, using two different object models. The trained CNN is tested to see if images can be accurately recovered from their corresponding sparse-view data. For reference, the same sparse-view CT data is reconstructed by the use of constrained total-variation (TV) minimization (TVmin), which exploits sparsity in the gradient magnitude image (GMI). There is a significant discrepancy betwe