Motion Planning in Medicine: Optimization and Simulation Algorithms for Image-Guided Procedures (Google eBook)
The monograph written by Ron Alterovitz and Ken Goldberg combines ideas from robotics, physically-based modeling, and operations research to develop new motion planning and optimization algorithms for image-guided medical procedures. A challenge clinicians commonly face is compensating for errors caused by soft tissue deformations that occur when imaging devices or surgical tools physically contact soft tissue. A number of methods are presented which can be applied to a variety of medical procedures, from biopsies to anaesthesia injections to radiation cancer treatment. They can also be extended to address problems outside the context of medical robotics, including nonholonomic motion planning for mobile robots in field or manufacturing environments.
What people are saying - Write a review
We haven't found any reviews in the usual places.
PhysicallyBased Simulations of Soft Tissue Deformations
Motion Planning in Deformable Soft Tissue with Appplications to Needle Insertion
Motion Planning in Deformable Soft Tissue with Obstacles with Applications to Needle Steering
Motion Planning for CurvatureConstrained Mobile Robots with Applications to Needle Steering
A Sampling Based Framework for Planning with Motion Uncertanity
action circle Alterovitz Automation ICRA balloon probe bevel direction bevel-tip needle brachytherapy clinical compute Conf conﬁguration space consider uncertainty constraints continuum mechanics deﬁned deﬂections deformable body diﬀerent direction changes discrete displacement displacement vector dmax dose calculation point dwell position eﬀect explicitly considers external forces ﬁeld ﬁnd ﬁnite element method ﬁrst ﬁxed image ﬂexible IEEE Int image registration image-guided implant Inverse Planning iteration linear programming medical imaging medical procedures minimizes motion planner motion planning motion planning algorithms motion uncertainty MRSI needle shaft needle steering needle tip nonholonomic nonlinear objective function obstacles parameters patient physician placement error Pouliot probability of success probe-in image probe-out image Proc prostate cancer radiation real-time reference mesh rigid probe roadmap Robotics Robotics and Automation sample segmented shown in ﬁgure signiﬁcant simulated annealing soft tissue speciﬁed steerable needles stiﬀness target tip node tissue type ultrasound urethra vector workspace