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Abstract-Physical unclonable functions (PUF), as hardware security primitives, exploit manufacturing randomness to extract instance-specific challenge (input) response (output) pairs (CRPs). Since its emergence, the community started pursuing a strong PUF primitive that is with large CRP space and resilient to modeling building attacks. A practical realization of a strong PUF is still challenging to date. This paper presents the PUF finite state machine (PUF-FSM) that is served as a practical controlled strong PUF. Previous controlled PUF designs have the difficulties of stabilizing the noisy PUF responses where the error correction logic is required. In addition, the computed helper data to assist error correcting, however, leaks information, which poses the controlled PUF under the threatens of fault attacks or reliability-based attacks. The PUF-FSM eschews the error correction logic and the computation, storage and loading of the helper data on-chip by only employing error-free responses judiciously determined on demand in the absence of an Arbiter PUF with a large CRP space. In addition, the access to the PUF-FSM is controlled by the trusted entity. Control in means of i) restricting challenges presented to the PUF and ii) further preventing repeated response evaluations to gain unreliability side-channel information are foundations of defensing the most powerful modeling attacks. The PUF-FSM goes beyond authentications/identifications to such as key generations and advanced cryptographic applications built upon a shared key.

Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent.

PurposeIntraoperative coronary motion modeling with motion surrogates enables prospective motion prediction in X-ray angiograms (XA) for percutaneous coronary interventions. The motion of coronary arteries is mainly affected by patients breathing and heartbeat. Purpose of our work is therefore to extract coronary motion surrogates that are related to respiratory and cardiac motion. In particular, we focus on respiratory motion surrogates extraction in this paper.MethodsWe propose a fast automatic method for extracting patient-specific respiratory motion surrogate from cardiac XA. The method starts with an image preprocessing step to remove all tubular and curvilinear structures from XA images, such as vessels and guiding catheters, followed by principal component analysis on pixel intensities. The respiratory motion surrogate of an XA image is then obtained by projecting its vessel-removed image onto the first principal component.ResultsThis breathing motion surrogate was demonstrated to get high correlation with ground truth diaphragm motion (correlation coefficient over 0.9 on average). In comparison with other related methods, the method we developed did not show significant difference (), but did improve robustness and run faster on monoplane and biplane data in retrospective and prospective scenarios.Conclusionswe developed and evaluated a method in extraction of respiratory motion surrogate from interventional X-ray images that is easy to implement and runs in real time and thus allows extracting respiratory motion surrogates during interventions.