Biomedical subjects

Alexander Schlaefer

Publications and source records attributed to Alexander Schlaefer.

3 recordsLinked to original sources

PubMed2006

Resampling: an optimization method for inverse planning in robotic radiosurgery.

By design, the range of beam directions in conventional radiosurgery are constrained to an isocentric array. However, the recent introduction of robotic radiosurgery dramatically increases the flexibility of targeting, and as a consequence, beams need be neither coplanar nor isocentric. Such a nonisocentric design permits a large number of distinct beam directions to be used in one single treatment. These major technical differences provide an opportunity to improve upon the well-established principles for treatment planning used with GammaKnife or LINAC radiosurgery. With this objective in mind, our group has developed over the past decade an inverse planning tool for robotic radiosurgery. This system first computes a set of beam directions, and then during an optimization step, weights each individual beam. Optimization begins with a feasibility query, the answer to which is derived through linear programming. This approach offers the advantage of completeness and avoids local optima. Final beam selection is based on heuristics. In this report we present and evaluate a new strategy for utilizing the advantages of linear programming to improve beam selection. Starting from an initial solution, a heuristically determined set of beams is added to the optimization problem, while beams with zero weight are removed. This process is repeated to sample a set of beams much larger compared with typical optimization. Experimental results indicate that the planning approach efficiently finds acceptable plans and that resampling can further improve its efficiency.

Computer Simulation↗

PubMed2006

Computer aided robotic radiosurgery.

Radiosurgery involves the precise delivery of sharply collimated high-energy beams of radiation to a distinct target volume along selected trajectories. Historically, accurate targeting required the application of a stereotactic frame, thus limiting the use of this procedure to single treatments of selected intracranial lesions. However, the scope of radiosurgery has undergone a remarkable broadening since the introduction of image-guided robotic radiosurgery. Recent developments in real-time image guidance provide an effective frameless alternative to conventional radiosurgery and allow both the treatment of lesions outside the skull and the possibility of performing hypofractionation. As a consequence, targets in the spine, chest and abdomen can now also be radiosurgically ablated with submillimetric precision. Meanwhile, the combination of image guidance, robotic beam delivery, and non-isocentric inverse planning can greatly enhance the conformality and homogeneity of radiosurgery. The aim of this article is to describe the technological basis of image-guided radiosurgery and provide a perspective on future developments. The current clinical usage of robotic radiosurgery will be reviewed with an emphasis on those applications that may represent a major shift in the therapeutic paradigm.

Brain Neoplasms↗

PubMedDate not supplied

Recognition of critical situations from time series of laboratory results by case-based reasoning.

OBJECTIVE: To develop a technique for recognizing critical situations based on laboratory results in settings in which a normal range cannot be defined, because what is "normal" differs widely from patient to patient. To assess the potential of this approach for kidney transplant recipients, where recognition of acute rejections is based on the pattern of changes in serum creatinine. DESIGN: We developed a case-based reasoning algorithm using dynamic time-warping as the measure of similarity which allows comparison of series of infrequent measurements at irregular intervals for retrieval of the most similar historical cases for the assessment of a new situation. MEASUREMENTS: The ability to recognize creatinine courses associated with an acute rejection was tested for a set of cases from a database of transplant patient records and compared with the diagnostic performance of experienced physicians. Tests were performed with case bases of various sizes. RESULTS: The accuracy of the algorithm increased steadily with the size of the available case base. With the largest case bases, the case-based algorithm reached an accuracy of 78 +/- 2%, which is significantly higher than the performance of experienced physicians (69 +/- 5.3%) (p < 0.001). CONCLUSION: The new case-based reasoning algorithm with dynamic time warping as the measure of similarity allows extension of the use of automatic laboratory alerting systems to conditions in which abnormal laboratory results are the norm and critical states can be detected only by recognition of pathological changes over time.

Acute Disease↗