A Multi-Frequency 3D Printed Hand Phantom for Electromagnetic Measurements.

It has been shown that the presence of a hand holding a wireless handset (cell phone) can influence antenna efficiency and the measurement of specific absorption rate (SAR) and electromagnetic compatibility. Head phantoms, used in handset compliance testing to estimate SAR in the head, have achieved low cost and multi-frequency use. Head phantoms typically consist of a thin plastic shell, open on the top, holding a tissue simulating fluid. The specific simulant fluid used is determined by the radio frequency of the test. IEC 62209-1 has recipes, using safe nontoxic materials, for all the required frequency bands. Thus, head phantoms can be reused at different frequencies simply by changing the tissue simulating fluid. However, standards have not adopted the use of hand phantoms because SAR limits in limbs are less restrictive than the head, the tissue depth in a hand is insufficient to make accurate measurements with current electric field probes, and the cost of a solid hand phantom is limited to a single frequency band. Our goal was to determine whether 3D printing techniques would allow the construction of a hand phantom with the same utility as existing head phantoms. We developed this phantom based on computer simulations to determine how much human anatomy needed to be included in the phantom to obtain results consistent with actual use. Electric field scans of a handset alone, and held by the hand phantom, were performed. Comparison of handset scans using the phantom and human subjects was planned, but not performed due to Covid-19 restrictions and subsequent changes in priorities. We feel a fluid-filled 3D printed hand phantom is viable and practical. The 3D print files are available on GitHub.

A computational model for bipolar deep brain stimulation of the subthalamic nucleus.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been shown to reduce some of the symptoms of advanced, levodopa-responsive Parkinson's disease that are not adequately controlled with medication. However, the precise mechanism of the therapeutic action of DBS is still unclear. Stimulation-induced side effects are not uncommon and require electrical "dose" adjustments. Quantitative methods are needed to fully characterize the electric field in the deep brain region that surrounds the electrodes in order to help with adjustments and maximize the efficacy of the device. Herein we report a magnetic resonance imaging (MRI)-based head model proposed for analysis of fields generated by deep brain stimulation (DBS). The model was derived from multimodal image data at 0.5mm isotropic spatial resolution and distinguishes 142 anatomical structures, including the basal ganglia and 38 nuclei of the thalamus. Six bipolar electrode configurations (1-2, 1-3, 1-4, 2-3, 2-4, 3-4) were modeled in order to assess the effects of the inter-electrode distance of the electric field. Increasing the distance between the electrodes results in an attenuated stimulation, with up to 25% reduction in electric field amplitude delivered (2-3 vs. 1-4). The map of the deep brain structures provided a highly precise anatomical detail which is useful for the quantitative assessment of current spread around the electrode and a better evaluation of the stimulation setting for the treatment optimization.

Atlas-based segmentation for globus pallidus internus targeting on low-resolution MRI.

In this paper we report a method to automatically segment the internal part of globus pallidus (GPi) on the pre-operative low-resolution magnetic resonance images (MRIs) of patients affected by Parkinson's disease. Herein we used an ultra-high resolution human brain dataset as electronic atlas of reference on which we segmented the GPi. First, we registered the ultra-high resolution dataset on the low-resolution dataset using a landmarks-based rigid registration. Then an affine and a non-rigid surface-based registration guided by the structures that surround the target was applied in order to propagate the labels of the GPi on the low-resolution un-segmented dataset and to accurately outline the target. The mapping of the atlas on the low-resolution MRI provided a highly accurate anatomical detail that can be useful for localizing the target.

A method for morphological characterization of dural ectasia in Marfan syndrome.

In this paper we reported a novel method to detect and quantify dural ectasia in Marfan syndrome. Firstly, the dural sacs of 8 Marfan patients were segmented by applying an unsupervised Fuzzy C-Means method on T2-weighed magnetic resonance images. Then, for each patient a tubular model of the dural sac was extracted by detecting and removing the existent pathological extrusions. The segmented images together with the resulting tube were then rendered using a marching cubes algorithm. The proposed algorithm represents a first attempt to quantify and to morphologically characterize the pathological ectasia that usually accompanies the Marfan disorder. The generated 3D reconstruction and the opportunity to overlap them with a physiological model provides the clinician with a tool for a panoramic view of the structures and a means for a more accurate inspection of ectasia. In addition the extracted parameters furnish quantitative and reproducible measures that could be useful as discriminative indexes for an automatic and more objective diagnosis.