DIGITAL PROCESSING FOR FLUOROSCOPY-BASED INTERVERTEBRAL KINEMATIC ANALYSIS

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Spinal degenerations can lead to segmental instability that is regarded as a major cause of back pain and is often an important factor in deciding on surgical fusion or prosthesis implant. The spinal kinematics analysis can provide useful information for diagnosis of instability and for the assessment of therapy and surgical treatment or for performance evaluation of disc prostheses. Digitized videofluoroscopy permits to analyze spinal motion during the full patient’s movement, with an acceptable low X-ray dose. By recognizing the vertebrae position on successive fluoroscopic images through manual selection or automated algorithms the relative kinematics between pairs of adjacent vertebrae (i.e. intervertebral kinematics) can be easily estimated. The application of fluoroscopy in the study of spinal kinematics is, however, limited because large errors can occur in the measurements. This thesis presents a comprehensive study of an innovative technique designed to provide a more accurate estimation of intervertebral kinematics. The recognition of vertebrae along the fluoroscopic sequence is implemented using an automated template-matching algorithm and involving a strong enhancement of the outline of vertebrae by resorting to derivative operators. Particular attention is devoted to fluoroscopic noise suppression and to edge-preserving filter design. Spline interpolation of the kinematic data extracted by videofluoroscopy is applied in order to obtain a more complete, continuous description of spinal kinematics and, more specifically, of instantaneous center of rotation. In the introductory part of the thesis (Chapter I and II) the motivation of the study and a survey of spinal measurement techniques are given. The feasibility of videofluoroscopic analysis of spinal motion is extensively discussed. In Chapter III common kinematic parameters (such as range of motion, center of rotation, etc.) utilized for describing intervertebral spinal behaviour are presented, providing particular emphasis on the difficulty to determine a “boundary” between normal and abnormal measures of segmental kinematics for the definition of spinal instability. An extensive review of recent proposals in analysis of segmental motion is reported. Manual recognition of anatomical landmarks in videofluoroscopy can be very problematic. It is also well-known that derivative operators, commonly used for automatic recognition, are highly sensitive to noise. Chapter IV attempts to address this issue: fluoroscopic noise model, also in presence of non-linear gray-level transformations for image enhancement, is presented; various denoising algorithms specifically designed for signal-dependent noise and AWGN are examined and a performance comparison among them is carried out. In Chapter V the proposed algorithm for automated vertebrae recognition is described and its performance is experimentally analyzed on fluoroscopic images of a calibration model. A comparison with a manual selection procedure and other automated algorithms on real lumbar fluoroscopic sequences is presented. In Chapter VI a continuous-time description of intervertebral motion by cubic smoothing spline interpolation is presented and the evaluation of instantaneous center of rotation of spinal motion segments by videofluoroscopy is discussed.