Acoustic neuroma surgery is a procedure in which a benign mass is removed from the internal auditory canal (IAC). Currently, this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working on the development of an acoustic neuroma surgery robot (ANSR) to perform the initial drilling procedure. Planning the ANSR’s drilling region using preoperative CT requires expertise and takes about 35 min. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and require minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from nine subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2 min were required in each case for the surgeon to verify and edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08 mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeon’s planning effort.
Safe and effective planning for robotic surgery that involves cutting or ablation of tissue must consider all potential sources of error when determining how close the tool may come to vital anatomy. A pre-operative plan that does not adequately consider potential deviations from ideal system behavior may lead to patient injury. Conversely, a plan that is overly conservative may result in ineffective or incomplete performance of the task. Thus, enforcing simple, uniform-thickness safety margins around vital anatomy is insufficient in the presence of spatially varying, anisotropic error. Prior work has used registration error to determine a variable-thickness safety margin around vital structures that must be approached during mastoidectomy but ultimately preserved. In this paper, these methods are extended to incorporate image distortion and physical robot errors, including kinematic errors and deflections of the robot. These additional sources of error are discussed and stochastic models for a bone-attached robot for otologic surgery are developed. An algorithm for generating appropriate safety margins based on a desired probability of preserving the underlying anatomical structure is presented. Simulations are performed on a CT scan of a cadaver head and safety margins are calculated around several critical structures for planning of a robotic mastoidectomy.
Acoustic neuroma surgery is a procedure in which a benign mass is removed from the Internal Auditory Canal (IAC). Currently this surgical procedure requires manual drilling of the temporal bone followed by exposure and removal of the acoustic neuroma. This procedure is physically and mentally taxing to the surgeon. Our group is working to develop an Acoustic Neuroma Surgery Robot (ANSR) to perform the initial drilling procedure. Planning the ANSR's drilling region using pre-operative CT requires expertise and around 35 minutes' time. We propose an approach for automatically producing a resection plan for the ANSR that would avoid damage to sensitive ear structures and require minimal editing by the surgeon. We first compute an atlas-based segmentation of the mastoid section of the temporal bone, refine it based on the position of anatomical landmarks, and apply a safety margin to the result to produce the automatic resection plan. In experiments with CTs from 9 subjects, our automated process resulted in a resection plan that was verified to be safe in every case. Approximately 2 minutes were required in each case for the surgeon to verify and edit the plan to permit functional access to the IAC. We measured a mean Dice coefficient of 0.99 and surface error of 0.08 mm between the final and automatically proposed plans. These preliminary results indicate that our approach is a viable method for resection planning for the ANSR and drastically reduces the surgeon's planning effort.
Otologic surgery often involves a mastoidectomy procedure, in which part of the temporal bone is milled away in order to visualize critical structures embedded in the bone and safely access the middle and inner ear. We propose to automate this portion of the surgery using a compact, bone-attached milling robot. A high level of accuracy is required t o avoid damage to vital anatomy along the surgical path, most notably the facial nerve, making this procedure well-suited for robotic intervention. In this study, several of the design considerations are discussed and a robot design and prototype are presented. The prototype is a 4 degrees-of-freedom robot similar to a four-axis milling machine that mounts to the patient's skull. A positioning frame, containing fiducial markers and attachment points for the robot, is rigidly attached to the skull of the patient, and a CT scan is acquired. The target bone volume is manually segmented in the CT by the surgeon and automatically converted to a milling path and robot trajectory. The robot is then attached to the positioning frame and is used to drill the desired volume. The accuracy of the entire system (image processing, planning, robot) was evaluated at several critical locations within or near the target bone volume with a mean free space accuracy result of 0.50 mm or less at all points. A milling test in a phantom material was then performed to evaluate the surgical workflow. The resulting milled volume did not violate any critical structures.
Otologic surgery is performed for a variety of reasons including treatment of recurrent ear infections, alleviation of
dizziness, and restoration of hearing loss. A typical ear surgery consists of a tympanomastoidectomy in which both the
middle ear is explored via a tympanic membrane flap and the bone behind the ear is removed via mastoidectomy to treat
disease and/or provide additional access. The mastoid dissection is performed using a high-speed drill to excavate bone
based on a pre-operative CT scan. Intraoperatively, the surface of the mastoid component of the temporal bone provides
visual feedback allowing the surgeon to guide their dissection. Dissection begins in "safe areas" which, based on surface
topography, are believed to be correlated with greatest distance from surface to vital anatomy thus decreasing the chance
of injury to the brain, large blood vessels (e.g. the internal jugular vein and internal carotid artery), the inner ear, and the
facial nerve. "Safe areas" have been identified based on surgical experience with no identifiable studies showing
correlation of the surface with subsurface anatomy. The purpose of our study was to investigate whether such a
correlation exists. Through a three-step registration process, we defined a correspondence between each of twenty five
clinically-applicable temporal bone CT scans of patients and an atlas and explored displacement and angular differences
of surface topography and depth of critical structures from the surface of the skull. The results of this study reflect
current knowledge of osteogenesis and anatomy. Based on two features (distance and angular difference), two regions
(suprahelical and posterior) of the temporal bone show the least variability between surface and subsurface anatomy.
During endoscopic procedures it is often desirable to determine the distance between anatomical features. One such
clinical application is percutaneous cochlear implantation (PCI), which is a minimally invasive approach to the cochlea
via a single, straight drill path and can be achieved accurately using bone-implanted markers and customized
microstereotactic frame. During clinical studies to validate PCI, traditional open-field cochlear implant surgery was
performed and prior to completion of the surgery, a customized microstereotactic frame designed to achieve the desired
PCI trajectory was attached to the bone-implanted markers. To determine whether this trajectory would have safely
achieved the target, a sham drill bit is passed through the frame to ensure that the drill bit would reach the cochlea
without damaging vital structures. Because of limited access within the facial recess, the distances from the bit to
anatomical features could not be measured with calipers. We hypothesized that an endoscope mounted on a sliding stage
that translates only along the trajectory, would provide sufficient triangulation to accurately measure these distances. In
this paper, the design, fabrication, and testing of such a system is described. The endoscope is mounted so that its optical
axis is approximately aligned with the trajectory. Several images are acquired as the stage is moved, and threedimensional
reconstruction of selected points allows determination of distances. This concept also has applicability in a
large variety of rigid endoscopic interventions including bronchoscopy, laparoscopy, and sinus endoscopy.
Cochlear implantation is a surgical procedure for treating patients with hearing loss in which an electrode array is
inserted into the cochlea. The traditional surgical approach requires drilling away a large portion of the bone behind the
ear to provide anatomical reference and access to the cochlea. A minimally-invasive technique, called percutaneous
cochlear implantation (PCI), has been proposed that involves drilling a linear path from the lateral skull to the cochlea
avoiding vital structures and inserting the implant using that drilled path. The steps required to achieve PCI safely
include: placing three bone-implanted markers surrounding the ear, obtaining a CT scan, planning a surgical path to the
cochlea avoiding vital anatomy, designing and constructing a microstereotactic frame that mounts on the markers and
constrains the drill to the planned path, affixing the frame on the markers, using it to drill to the cochlea, and inserting
the electrode through the drilled path. We present in this paper a cadaveric study demonstrating the PCI technique on
three temporal bone cadaveric specimens for inserting electrode array into the cochlea. A custom fixture, called a
Microtable, which is a type of microstereotactic frame that can be constructed in less than five minutes, was fabricated
for each specimen and used to reach the cochlea. The insertion was successfully performed on all three specimens. Postinsertion
CT scans confirm the correct placement of the electrodes inside the cochlea without any damage to the facial
nerve.
Rigid-body, point-based registration is commonly used for image-guided systems. Fiducial markers that can be localized
in image and physical space are attached to patient anatomy. The fiducial marker locations in the two spaces are used to
obtain the physical-to-image registration. It is a common practice to obtain physical positions via optical systems, whose
localization error is anisotropic. Furthermore, the positions are often reckoned relative to a coordinate reference frame
(CRF) that is rigidly attached to the patient. The use of a CRF enables patient movement relative to the tracking system,
but it tends to exacerbate the anisotropy. It is common practice to ignore the localization anisotropy and employ a closed-form
solution, which is available for isotropic weighting but not for anisotropic weighting. Iterative methods are
available for anisotropic weighting but are quite complex. We present a new iterative algorithm for anisotropic weighting
that is simple, intuitive, and has only one adjustable parameter. We show using simulations that our algorithm is more
accurate than the isotropic solution for anisotropic localization error. In particular, we show that the new algorithm
reduces target registration error when anisotropic localization error is present. When all the localization errors are
isotropic, the new algorithm performs as well as the closed-form solution.
Applying image-guidance to an electronically-controlled surgical drill can prevent damage to patients' anatomy during
resection. A system is presented that disables the drill when it nears pre-defined critical patient anatomy. The system
consists of a tracking system, image-guidance software, and drill-control circuit. The software was developed in C++
with the help of the Image-Guided Surgery Toolkit, and was designed to track tools based on input from a
MicronTracker (Claron Tech, Toronto, Ontario) tracking system. The system registers physical to image space using
fiducial markers rigidly attached to the patient, tracks the drill, and automatically disables the drill when close to
restricted regions. A coordinate reference frame is used for all physical acquisitions. Visual feedback of the tool's
position in image space is provided during tracking. Two tests were performed to determine the feasibility of the system.
Virtual restricted regions were defined inside a phantom, and an operator attempted to drill the phantom with the help of
the application. No feedback was provided to the user except for the automatic disablement of the drill by the application
when close to a restricted region. In the first test, the drill was disabled at 0.74 ± 0.46 mm from the restricted region and
entered 5.3% of the surface area of the restricted region. In the second test, the drill was disabled 1.3 ± 0.69 mm from the
restricted region and entered the restricted region 8.5% of the time. We conclude that the system shows promise and
further testing should be conducted.
Many image-guidance surgical systems rely on rigid-body, point-based registration of fiducial markers attached to the
patient. Marker locations in image space and physical space are used to provide the transformation that maps a point
from one space to the other. Target registration error (TRE) is known to depend on the fiducial localization error (FLE),
and the fiducial registration error (FRE) of a set of markers, though a poor predictor of TRE, is a useful predictor of FLE.
All fiducials are typically weighted equally for registration purposes, but is also a common practice to ignore a marker at
position r by zeroing its weight when its individual error,
FRE(r), is high in an effort to reduce TRE. The idea is that
such markers are likely to have been compromised, i.e., perturbed badly between imaging and surgery. While ignoring a
compromised marker may indeed reduce TRE, the expected effect of ignoring an uncompromised marker is to increase
TRE. There is unfortunately no established method for deciding whether a given marker is likely to have been
compromised. In order to make this decision, it is necessary to know the probability distribution p(FRE(r)), which has
not been heretofore determined. With such a distribution, it may be possible to identify a compromised marker and to
adjust its weight in order to improve the expected TRE. In this paper we derive an approximate formula for p(FRE(r))
accurate to first order in FLE. We show by means of numerical simulations that the approximation is valid.
Image guidance in otologic surgery has been thwarted by the need for a non-invasive fiducial system with target
registration error (TRE) at the inner ear below 1.5mm. We previously presented a fiducial frame for this purpose that
attaches to the upper dentition via patient-specific bite blocks and demonstrated a TRE of 0.73mm (±0.23mm) on
cadaveric skulls. In that study, TRE measurement depended upon placement of bone-implanted, intracranial target
fiducials-clearly impossible to repeat clinically. Using cadaveric specimens, we recently presented a validation method
based on an auditory implant system (BAHA System®; Cochlear Corp., Denver, CO). That system requires a skull-implanted
titanium screw behind the ear upon which a bone-anchored hearing aid (BAHA) is mounted. In our validation,
we replace the BAHA with a fiducial marker to permit measurement of TRE. That TRE is then used to estimate TRE at
an internal point. While the method can be used to determine accuracy at any point within the head, we focus in this
study on the inner ear, in particular the cochlea, and we apply the method to patients (N=5). Physical localizations were
performed after varying elapsed times since bite-block fabrication, and TRE at the cochlea was estimated. We found
TRE to be 0.97mm at the cochlea within one month and 2.5mm after seven months. Thus, while accuracy deteriorates
considerably with delays of seven months or more, if this frame is used within one month of the fabrication of the bite-block,
it achieves the goal and in fact exhibits submillimetric accuracy.
An algorithm is presented that is designed for image-guidance systems that employ coordinate reference frames. It is common in image-guided surgery (IGS) to use a tracking system to determine the position of fiducial markers in physical space. Typically a “Coordinate Reference Frame” (CRF), is also employed, which is rigidly attached to the object being tracked. The positions of markers attached to the object are then measured in physical space relative to the CRF, and hence it is acceptable to allow the object to move during tracking. It is known that errors are introduced while localizing markers in image space and also while localizing markers in physical space using a probe. The use of a CRF causes additional error, which is anisotropic in nature and varies with the position of the marker being tracked relative to the CRF. This additional error has heretofore not been accounted for in the process of registering image space to physical space. We present in this paper a new rigid-body, point-based registration algorithm that accounts for the fiducial localization errors that arise in tracking systems that employ a coordinate reference frame. Simulations are presented that show that for such systems the new algorithm has the capability to perform better than the standard registration algorithm. The effect is enhanced for small CRFs and for marker configurations that are widely spaced relative to their mean distance from the CRF.
Image registration is an important procedure for medical diagnosis. Since the large inter-site retrospective validation study led by Fitzpatrick at Vanderbilt University, voxel-based methods and more specifically mutual information (MI) based registration methods have been regarded as the method of choice for rigid-body intra-subject registration problems. In this study we propose a method that is based on the iterative closest point (ICP) algorithm and a pre-computed closest point map obtained with a slight modification of the fast marching method proposed by Sethian. We also propose an interpolation scheme that allows us to find the corresponding points with a sub-voxel accuracy even though the closest point map is defined on a regular grid. The method has been tested both on synthetic and real images and registration results have been assessed quantitatively using the data set provided by the Retrospective Registration Evaluation Project. For these volumes, MR and CT head surfaces were extracted automatically using a level-set technique. Results show that on these data sets this registration method leads to accuracy numbers that are comparable to those obtained with voxel-based methods.
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