The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia
nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson’s disease. In order to manually
trace these structures, a combination of high-resolution and specialized sequences at 7T are used, but it is not feasible to
scan clinical patients in those scanners. Targeted imaging sequences at 3T such as F-GATIR, and other optimized inversion
recovery sequences, have been presented which enhance contrast in a select group of these structures. In this work, we
show that a series of atlases generated at 7T can be used to accurately segment these structures at 3T using a combination
of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the
thalamus and putamen, a median Dice coefficient over 0.88 and a mean surface distance less than 1.0mm was achieved
using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus
pallidus a Dice over 0.75 and a mean surface distance less than 1.2mm was achieved using a combination of T1 and FGATIR
imaging sequences. In the substantia nigra and sub-thalamic nucleus a Dice coefficient of over 0.6 and a mean
surface distance of less than 1.0mm was achieved using the optimized inversion recovery imaging sequence. On average,
using T1 and optimized inversion recovery together produced significantly improved segmentation results than any
individual modality (p<0.05 wilcox sign-rank test).
T1-weighted magnetic resonance imaging (MRI) generates contrasts with primary sensitivity to local T1 properties (with lesser T2 and PD contributions). The observed signal intensity is determined by these local properties and the sequence parameters of the acquisition. In common practice, a range of acceptable parameters is used to ensure “similar” contrast across scanners used for any particular study (e.g., the ADNI standard MPRAGE). However, different studies may use different ranges of parameters and report the derived data as simply “T1-weighted”. Physics and imaging authors pay strong heed to the specifics of the imaging sequences, but image processing authors have historically been more lax. Herein, we consider three T1-weighted sequences acquired the same underlying protocol (MPRAGE) and vendor (Philips), but “normal study-to-study variation” in parameters. We show that the gray matter/white matter/cerebrospinal fluid contrast is subtly but systemically different between these images and yields systemically different measurements of brain volume. The problem derives from the visually apparent boundary shifts, which would also be seen by a human rater. We present and evaluate two solutions to produce consistent segmentation results across imaging protocols. First, we propose to acquire multiple sequences on a subset of the data and use the multi-modal imaging as atlases to segment target images any of the available sequences. Second (if additional imaging is not available), we propose to synthesize atlases of the target imaging sequence and use the synthesized atlases in place of atlas imaging data. Both approaches significantly improve consistency of target labeling.
The use of deep brain stimulation (DBS) for the treatment of neurological movement degenerative disorders requires the
precise placement of the stimulating electrode and the determination of optimal stimulation parameters that maximize
symptom relief (e.g. tremor, rigidity, movement difficulties, etc.) while minimizing undesired physiological side-effects.
This study demonstrates the feasibility of determining the ideal electrode placement and stimulation current amplitude
by performing a patient-specific multivariate optimization using electrophysiological atlases and a bioelectric finite
element model of the brain. Using one clinical case as a preliminary test, the optimization routine is able to find the most
efficacious electrode location while avoiding the high side-effect regions. Future work involves optimization validation
clinically and improvement to the accuracy of the model.
In deep brain stimulation surgeries, stimulating electrodes are placed at specific targets in the deep brain to treat
neurological disorders. Reaching these targets safely requires avoiding critical structures in the brain. Meticulous
planning is required to find a safe path from the cortical surface to the intended target. Choosing a trajectory
automatically is difficult because there is little consensus among neurosurgeons on what is optimal. Our goals are to
design a path planning system that is able to learn the preferences of individual surgeons and, eventually, to standardize
the surgical approach using this learned information. In this work, we take the first step towards these goals, which is to
develop a trajectory planning approach that is able to effectively mimic individual surgeons and is designed such that
parameters, which potentially can be automatically learned, are used to describe an individual surgeon's preferences. To
validate the approach, two neurosurgeons were asked to choose between their manual and a computed trajectory, blinded
to their identity. The results of this experiment showed that the neurosurgeons preferred the computed trajectory over
their own in 10 out of 40 cases. The computed trajectory was judged to be equivalent to the manual one or otherwise
acceptable in 27 of the remaining cases. These results demonstrate the potential clinical utility of computer-assisted path
planning.
A number of groups have reported on the occurrence of intra-operative brain shift during deep brain stimulation (DBS)
surgery. This has a number of implications for the procedure including an increased chance of intra-cranial bleeding and
complications due to the need for more exploratory electrodes to account for the brain shift. It has been reported that the
amount of pneumocephalus or air invasion into the cranial cavity due to the opening of the dura correlates with intraoperative
brain shift. Therefore, pre-operatively predicting the amount of pneumocephalus expected during surgery is of
interest toward accounting for brain shift. In this study, we used 64 DBS patients who received bilateral electrode
implantations and had a post-operative CT scan acquired immediately after surgery (CT-PI). For each patient, the
volumes of the pneumocephalus, left ventricle, right ventricle, third ventricle, white matter, grey matter, and cerebral
spinal fluid were calculated. The pneumocephalus was calculated from the CT-PI utilizing a region growing technique
that was initialized with an atlas-based image registration method. A multi-atlas-based image segmentation method was
used to segment out the ventricles of each patient. The Statistical Parametric Mapping (SPM) software package was
utilized to calculate the volumes of the cerebral spinal fluid (CSF), white matter and grey matter. The volume of
individual structures had a moderate correlation with pneumocephalus. Utilizing a multi-linear regression between the
volume of the pneumocephalus and the statistically relevant individual structures a Pearson's coefficient of r = 0.4123 (p
= 0.0103) was found. This study shows preliminary results that could be used to develop a method to predict the amount
of pneumocephalus ahead of the surgery.
Movement disorders affect over 5,000,000 people in the United States. Contemporary treatment of these diseases
involves high-frequency stimulation through deep brain stimulation (DBS). This form of therapy is offered to
patients who have begun to see failure with standard medical therapy and also to patients for which medical therapy
is poorly effective. A DBS procedure involves the surgical placement, with millimetric accuracy, of an electrode in
the proximity of functional areas referred to as targets. Following the surgical procedure, the implant, which is a
multi-contact electrode is programmed to alleviate symptoms while minimizing side effects. Surgical placement of
the electrode is difficult because targets of interest are poorly visible in current imaging modalities. Consequently,
the process of implantation of a DBS electrode is an iterative procedure. An approximate target position is
determined pre-operatively from the position of adjacent structures that are visible in MR images. With the patient
awake, this position is then adjusted intra-operatively, which is a lengthy process. The post-surgical programming of
the stimulator is an equally challenging and time consuming task, with parameter setting combinations exceeding
4000. This paper reports on the status of the Vanderbilt University DBS Project, which involves the development
and clinical evaluation of a system designed to facilitate the entire process from the time of planning to the time of
programming.
We are developing and evaluating a system that will facilitate the placement of deep brain stimulators (DBS) used to
treat movement disorders including Parkinson's disease and essential tremor. Although our system does not rely on the
common reference system used for functional neurosurgical procedures, which is based on the anterior and posterior
commissure points (AC and PC), automatic and accurate localization of these points is necessary to communicate the
positions of our targets. In this paper, we present an automated method for AC and PC selection that uses non-rigidly
deformable atlases. To evaluate the accuracy of our multi-atlas based method, we compare it against the manual
selection of the AC and PC points by 43 neurosurgeons (38 attendings and 5 residents) and show that its accuracy is submillimetric
compared to the median of their selections. We also analyze the effect of AC-PC localization inaccuracy on
the localization of common DBS targets.
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