Thermal safety of a retinal prosthesis

© 2011 Dr. Nicholas Lachlan OpieFor over 27 million patients worldwide suffering from visual loss and impairment caused by retinitis pigmentosa and age related macular degeneration, a retinal implant with the ability to restore their sight at even a rudimentary level is highly desired. Current clinical trials, performed with between 16 and 60 electrodes, have had some success of eliciting perceptions of the visual field through electrical stimulation of the remaining retinal neurons. These studies, as well as computational simulations, suggest that in excess of 1000 electrodes may be required to provide mobility, facial recognition, and the ability to read at a normal speed. As the desire for enhanced visual acuity and resolution increases, the potential for hazardous amounts of thermal energy to be released into the delicate retinal neurons through the implanted circuitry is a large concern. Previous studies that investigated the effects of thermal damage on retinal tissue were predominantly performed using lasers and electromagnetic irradiation. Not only do these studies exclude the possibility of mechanical trauma caused by prosthesis implantation to exacerbate thermally induced damage, but are performed using powers greater than those dissipated by implant circuitry and durations shorter than a potential eighteen hour daily use. Thus, the primary aim of this thesis is to investigate the effects of thermal heating by an implant on retinal tissue, to ensure that energy dissipated by an implanted visual prosthesis does not induce cellular damage or death. Determination of temperatures that cause thermal damage in an acute and chronic setting can be used to assist in the design of implant circuitry and stimulation protocols to maximize the performance of visual prostheses while ensuring patient safety is not compromised. To assist with answering the question of whether the thermal safety of retinal implants can be ensured, temperature measurement and delivery devices were designed. These implants, temperature measurement systems and power sources were shown in cadaver, in vitro and in vivo experiments to be able to be implanted epiretinally in rat eye-cups and both suprachoroidally and epiretinally in cat eyes. These implants, designed to be of comparable size to current retinal prosthesis microchips, were also shown to be capable of inducing, measuring and recording the localised retinal tissue temperature increases in multiple positions simultaneously. A finite element thermal model has been specifically designed as a component of this research to model devices implanted in both an epiretinal and suprachoroidal location. The model has the versatility to investigate the many factors that contribute to the temperature of neighbouring retinal tissue such as heat dissipated by the implant circuitry, room and body temperature, choroidal and retinal blood perfusion volumes and flow rates, thermal conductivity changes related to cellular reorganization and death of retinal neurons, and metabolically generated heat. This model can be employed to greatly reduce the number of animals that would otherwise be required to perform such a large number of thermal experiments and has been shown through cadaver tests to accurately represent temperatures within the eye. There was a negligible contribution to retinal tissue temperature increase caused by alterations in room temperature, body temperature and metabolically generated heat; however, the power dissipated by the implants, the thermal conductivity of the tissue and the rate of blood perfusion were observed to play a significant role in the distribution of the implant-induced power dissipation. Comparisons between the thermal models, cadaver trials, and in vivo trials performed on cats indicated a very consistent and replicable behaviour between the finite element simulations and the experiments. Thus, the use of these models could significantly reduce the number of animals sacrificed in order to more thoroughly assess the thermal impact of retinal implants. In vivo cat experiments and in vitro rat experiments were also conducted and have assisted in the optimisation of surgical strategies and protocols for suprachoroidal prosthesis implantation. Preliminary results from the in vivo work indicated that thermoregulatory mechanisms will assist in maintaining a homeostatic retinal temperature when subjected to an implant-induced thermal elevation greater than 2°C. Analysis of in vitro microglial morphological changes, observed through retraction of microglial processes and an increase in microglia soma areas, indicated that any thermal or mechanical insult will instigate changes within the retinal tissue. Mechanical trauma, caused by insertion of a foreign object into the retina, induced a response that was greatly exacerbated in the presence of thermal stress, and it is recommended that prior to operation of an implanted prosthesis (where power will be dissipated by the implanted circuitry), the induced mechanical trauma be allowed as much time as is required to stabilise, to reduce the potential for irreversible cellular damage to occur. To ensure an implanted retinal prosthesis will not cause extensive damage to neighbouring neurons, careful monitoring of an implant is recommended. Thermal increases of 2.54˚C were observed to cause activation of microglia in in vitro rat preparations, and we recommend that retinal implants restrict power dissipation to below 31.91 mW (equivalent to a power density of 23.46 mW/mm2) to reduce the potentially deleterious effects caused by thermal heating

Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation

<div><p>Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson’s disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5–0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0–0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.</p></div

Segmentation Metrics.

<p>Segmentation Metrics.</p

The (a) skulled template and (b) deskulling mask.

<p>Variability displayed in extracortical areas reinforces the reason why deskulling is an important step in pre-processing.</p

Deskulling Metrics.

<p>Deskulling Metrics.</p

Dice Similarity Coefficients of all structures in the low resolution template.

<p>Values range from 0.12 for the amygdala to 0.75 for the cerebellum. Red = Ventricles, Green = Right Motor Cortex, Dark Blue = Left Motor Cortex, Yellow = Hippocampus, Light Blue = Thalamus, Purple = Caudate Nucleus, Peach = Amygdala, Grey = Cerebellum, Brown = Intraobserver Error. Error bars show the standard deviation of the mean for all compared structures. Intraobserver Error was calculated by comparing manual segmentations of all seven labelled structures in the four test subjects. Number of test subjects (n = 4).</p

Atlas—Template and Associated Labels.

<p>Segmented Atlas showing (1) Ventricles, (2) Motor Cortices, (3) Hippocampi, (4) Thalami, (5) Caudate (6) Amygdala and (7) Cerebellum illustrating the (a) transverse, (b) parasagittal, (d) dorsal planes and (c) 3d rendering of labels on a ventrolateral view with rostral to the left of the image.</p

Methods Flowchart.

<p>To create a template, an input image (template subject) undergoes a sequence of processing steps (black boxes) such as pre-processing, template construction, labelling and registration to finally output a labelled image. Red boxes represent input and output images.</p

Box plot displaying the average of all comparisons performed across all segmented structures.

<p>Shows the median Dice Similarity Coefficient to be 0.61 with the False Positive and False Negative values having a very large variation. This indicates a wide spread in the accuracy of segmentation with regards to various structures. The Jaccard Coefficient is as expected lower than the Dice proportionally due to single use of the intersect in its calculation leading to a more precise comparison.</p