Background: Neuromodulation with spinal cord stimulation is a proven cost effective treatment for the management of common conditions such as chronic radicular leg pain from failed back surgery syndrome, complex regional pain syndrome, and other painful neuropathic pain syndromes. The traditionally instructed method for percutaneous spinal cord stimulator (SCS) lead placement promotes the use of a “loss of resistance” (LOR) technique under anteroposterior (AP) fluoroscopic guidance to assure midline lead placement and proper entry into the epidural space. Objective: To describe the relevant anatomy and method for a precise needle placement approach for placement of percutaneous cervical spinal cord stimulation (SCS) leads without loss of resistance (LOR) using a syringe. An oblique fluoroscopic view is presented demonstrating successful placement of cervical SCS leads. Design: Technical report. Setting: Pain management clinic. Methods: Discussion with accompanying fluoroscopic images. This technical report meets HIPAA compliance standards. Results: Successful placement of percutaneous SCS leads without traditional loss of resistance using an oblique fluoroscopic approach. Limitations: Technical report only. The risks, potential complications, and benefit from this approach are beyond the scope of the article. Conclusions: This fluoroscopic technique provides an alternative means for placing percutaneous cervical SCS leads without the use of the traditional loss of resistance technique. Key words: spinal cord stimulation, neuromodulation, cervical spine, fluoroscopy, loss of resistance, epidural injection, neuropathic pain, failed neck surgery.

Helmet-mounted display (HMD) systems allow aircraft pilots to aim at targets by using head postures. However, the direct use of helmet orientation to indicate the aiming direction ignores human eye movements, which are more flexible and efficient for interaction. Since the opaqueness of the helmet goggle blocks the sight of external cameras to capture facial or eye images of the pilots, and traditional eye feature extraction methods may fail when encountering conditions such as poor lighting, occlusion, and shaking, which are common on fighter aircrafts. In this work, an eye gaze based aiming solution that adapts to pilots wearing HMDs is proposed, and a deep learning-based method is proposed to extract eye features robustly. The prototype experiments demonstrate the ability to pick and aim at targets in real-time (60FPS) and are capable to accurately locate the target markers on a screen with an average error of fewer than 2 degrees. Conclusively, the proposed method performs the tasks of eye feature extraction on real-person imagery and the estimation of the 3D aiming direction for users with helmets, displaying competitive results with similar research.

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