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Deep brain stimulation (DBS) is a an established therapy in neurological and mental disorders making use of electrical pulses chronically delivered to a certain disease-specific neural target through surgically implanted electrodes. The therapeutical effect of DBS is highly individual and depends on the target coverage by the stimuli and the amount of spill beyond it. This can be suitably formulated as an optimization problem. Since the biological mechanism underlying the DBS therapy is mainly unknown, and due to high inter-patient and intra-patient variability of the DBS effect, a pragmatic approach to the DBS programming is to consider the process as tuning of a control system for the symptoms. Such a technology assumes that the symptoms are accurately quantified. The paper summarizes the progress in the individualized DBS and presents the results of a limited clinical study making use of the proposed DBS programming approach.

In this paper, an unsupervised machine learning technique is proposed to localize the Subthalamic Nucleus (STN) during deep brain stimulation (DBS) Surgery. DBS is one of most common treatments for advanced Parkinson’s disease (PD). The purpose of this surgery is to permanently implant stimulation electrodes inside the STN to deliver electrical currents. It is clinically shown that DBS surgery can significantly reduce motor symptoms of PD (such as tremor). However, the outcome of this surgery is highly dependent on the location of the stimulating electrode. Since STN is a very small region inside the basal ganglia, accurate placement of the electrode is a challenging task for the surgical team. During DBS surgery, the team uses Micro-Electrode Recording (MER) of electrophysiological neural activities to intraoperatively track the location of electrodes and estimate the borders of the STN. In this work, we propose a composite unsupervised machine learning clustering approach that is capable of detecting the dorsal borders of the STN during DBS operation. For this, MER signals from 50 PD patients were recorded and used to validate the performance of the proposed method. Results show that the approach is capable of detecting the dorsal border of the STN in an online manner with an accuracy of 80% without using any supervised training.

Cardiopulmonary Resuscitation (CPR) is an emergency medical procedure applied when a patient’s heart stops functioning suddenly, mainly due to cardiac arrest, drowning or electric shock. Normally, CPR is applied manually but the process is ineffective to provide adequate chest compressions to cardiac arrest patients. The main objective in this paper is to design a mechanized process of providing CPR and thus replacing the traditional manual CPR technique altogether. We designed an automated CPR device with piston-driven chest compression system and a patient monitoring system. The patient monitoring system consists of a heart rate monitor, body temperature monitor and respiratory rate monitor. The heart rate monitor is integrated with the automated CPR device. This means that when the heart rate monitor detects patient’s heart beat then the automated CPR device will not operate but if the heart rate monitor detects no heart beat then the automated CPR device will immediately provide CPR to the patient. The automated CPR along with the patient monitoring system has been tested on a CPR manikin and the results of the performance is well within the accepted CPR guidelines.

In this paper, a novel technique is proposed for localization of Subthalamic Nucleus (STN) during deep brain stimulation (DBS) Surgery. DBS surgery is performed on individuals living with Parkinson’s disease (PD) to permanently implant stimulation electrodes for managing some motor symptoms of PD. The most challenging part of this surgery is to accurately place the electrodes inside the STN. Commonly, microelectrode recordings (MERs) are interpreted by the surgical team intraoperatively to estimate the location of electrodes and detect the borders of the STN. In this work, we aim to automate the process of localizing the STN using a machine learning technique (trained based on the electrophysiological signals that we have collected during 20 surgeries). The proposed approach is capable of detecting the dorsal borders of the STN during the procedure with high accuracy (85%), and outperforms the current state-of-the-art approach for this application.

Deep brain stimulation (DBS) has been widely practiced for the treatment of advanced Parkinsons disease (PD) but the underlying mechanism is still not well understood. Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and Globus Pallidus interna (GPi) neurons are the common DBS target sites, which are selected based on the patients symptoms. The DBS pulse shape must also guarantee activation of the desired neurons and optimized energy consumption. In this paper, we apply energy efficient Gaussian DBS signals on different targets in a computational model of the basal ganglia. The results shows that Gaussian signals outperform the widely-used rectangular signals in terms of desynchronization of the GPi neurons and the total energy consumed by the DBS process. Our quantitative results suggest that targeting STN neurons for DBS (STN-DBS) is beneficial for PD patients with axial and cardinal symptoms, while dyskinesia is better treated by GPi-DBS, and improving bradykinesia and akinesia symptoms of PD is mostly achieved by GPe-DBS.

The high efficiency with which gas microbubbles can scatter ultrasound compared with the surrounding blood pool or tissues has led to their widespread employment as contrast agents in ultrasound imaging. In recent years, their applications have been extended to include super-resolution imaging and the stimulation of localized bio-effects for therapy. The growing exploitation of contrast agents in ultrasound and in particular these recent developments have amplified the need to characterize and fully understand microbubble behavior. The aim in doing so is to more fully exploit their utility for both diagnostic imaging and potential future therapeutic applications. This paper presents the key characteristics of microbubbles that determine their efficacy in diagnostic and therapeutic applications and the corresponding techniques for their measurement. In each case, we have presented information regarding the methods available and their respective strengths and limitations, with the aim of presenting information relevant to the selection of appropriate characterization methods. First, we examine methods for determining the physical properties of microbubble suspensions and then techniques for acoustic characterization of both suspensions and single microbubbles. The next section covers characterization of microbubbles as therapeutic agents, including as drug carriers for which detailed understanding of their surface characteristics and drug loading capacity is required. Finally, we discuss the attempts that have been made to allow comparison across the methods employed by various groups to characterize and describe their microbubble suspensions and promote wider discussion and comparison of microbubble behavior.

The subthalamic nucleus (STN) within the sub-cortical region of the Basal ganglia is a crucial targeting structure for Parkinson's Deep brain stimulation (DBS) surgery. Volumetric segmentation of such small and complex structure, which is elusive in clinical MRI protocols, is thereby a pre-requisite process for reliable DBS direct targeting. While direct visualization of the STN is facilitated with advanced ultrahigh-field MR imaging (7 Tesla), such high fields are not always clinically available. In this paper, we aim at the automatic prediction of the STN region on clinical low-field MRI, exploiting dependencies between the STN and its adjacent structures, learned from ultrahigh-field MRI. We present a framework based on a statistical shape model to learn such shape relationship on high quality MR data sets. This allows for an accurate prediction and visualization of the STN structure, given detectable predictors on the low-field MRI. Experimental results on Parkinson's patients demonstrate that the proposed approach enables accurate estimation of the STN on clinical 1.5T MRI.

Clinical scale-based follow-up of patients undergoing bilateral subthalamic deep brain stimulation (STN-DBS) surgery has shown inconsistent effects on bradykinesia in Parkinson's disease patients. Furthermore, quantitative assessment of STN-DBS effect on bradykinesia has not been fully explored yet. Our group uses multisensory kinematic technologies to study short-term and long-term DBS effects on Parkinson's disease. In this paper we present a kinematic analysis of the short term (3 month) effects of STN-DBS on bradykinesia in five PD patients and compare the results with data obtained from healthy age-matched controls.

Deep brain stimulation (DBS) is a highly promising therapy for Parkinson's disease (PD). However, most patients do not get full therapeutic benefit from DBS yet, due to its critical dependence on electrode location. For this reason, we believe that the investigation of a neural modeling, estimation and control framework for the STN is an interesting research problem. This would pave the way for the development of a novel surgical tool for the DBS placement standardization, i.e., an automated intraoperative closed-loop DBS localization system. A fundamental problem to be solved for the realization of a such framework is the neurophysiologic characterization of the STN activity. Indeed, this would allow to understand if the modeling of the sweet spot is feasible. In this paper an effort towards the modeling of the neuronal activity near the stimulation target is made: first we analyze single unit spiking activity of 120 STN neurons collected from four PD patients at different distances from the sweet spot and, for each neuron, we estimate a point process model (PPM). Then, we see that PPMs capture the stochastic effects of the distance from the sweet spot on the STN spiking activity, and characterize the impact of local neuronal networks on the single neurons. Our results suggest that PPMs might be an effective tool for modeling of the STN neuronal activities accounting for the depth within it.

In this paper, we proposed Approximate Entropy (ApEn) measure for nonlinear analysis of patterns of two daily common movements, that is sit-to-stand and stand-to-sit, in patients with Parkinson's disease (PD) using deep brain stimulation (DBS). ApEn value of a signal indicates its complexity and irregularity, such that larger the ApEn measure is, more stochastic and less regular the signal is. Here, this nonlinear measure is used for classifying three groups of 1) healthy subjects, 2) PD patients with DBS off, and 3) the same PD patients with DBS on, based on their sit-to-stand and stand-to-sit patterns. To this end, the area under receiver operating characteristic (ROC) plots (AUC) is used to evaluate the capability of ApEn. For stand-to-sit patterns and for discrimination between two groups of normal vs. PD with DBS off, normal vs. PD with DBS on, and PD with DBS off vs. PD with DBS on, we achieved the AUC values of up to 0.9625, 0.9, and 0.9583, respectively. Also, there was high correlation between ApEn values of PD patients obtained from different sensors and the severity of their Parkinson's disease i.e. UPDRS scores (up to 0.7671 for PD with DBS off and 0.774 for PD with DBS on). For sit-to-stand patterns and to distinguish between normal vs. PD with DBS off, normal vs. PD with DBS on, and PD with DBS off vs. PD with DBS on, we achieved the AUC values of up to 1, 0.9778, and 0.9167, respectively. Moreover, for this pattern, there was higher correlation between ApEn values of PD patients and their UPDRS scores (up to 0.9573 for PD with DBS off and 0.7145 for PD with DBS on). Furthermore, in both sit-to-stand and stand-to-sit patterns, it was observed that PD patients have larger complexity values than healthy controls and PD subjects in DBS on state had smaller irregularity values than PD subjects in DBS off state. In general, we may conclude that sit-to-stand patterns can distinguish PD patients from healthy subjects and also be used to estimate the severity of Parkinson's disease better than stand-to-sit patterns.

In this paper, we investigated effects of deep brain stimulation (DBS) on Parkinson's disease (PD) when different target sites in the basal ganglia are stimulated. The targets which are investigated are subthalamic nucleus (STN), globus pallidus interna (GPi), and globus pallidus externa (GPe). For this purpose we used a computational model of the basal ganglia-thalamocortical system (BGTCS) with parameters calculated for mean field. This model is able to reproduce both the normal and parkinsonian activities of basal ganglia, thalamus and cortex in a unified structure. Our results suggest that DBS in the STN and GPe could restore the thalamus relay activity, while DBS in the GPi could inhibit it. Our results are compatible with the experimental and the clinical outcomes about the effects of DBS of different targets.

In this paper we investigated by a computational model of the basal ganglia-thalamocortical system (BGTCS) differences between the motor cortex stimulation (MCS) and deep brain stimulation (DBS) of the subthalamic nucleus (STN) of the basal ganglia (BG) in Parkinson's disease (PD). To this end, a mean-field model of the BGTCS is used. This model is able to reproduce both the normal and parkinsonian activities of the BG, thalamus and motor cortex in a unified structure. Using this model, we simulate DBS and MCS, and compared the effect of location of stimulation on mean firing rates of the BGTCS. Our results suggest that DBS of the STN and MCS could restore thalamus relay activity, but DBS effects on PD are stronger than MCS. Consequently, stimulation of cortex seems to mimic the stimulation of STN in a relatively less effective manner. Our results are compatible with the experimental and the clinical outcomes on the effects of DBS and MCS.

Image-guided intervention and needle steering for radiofrequency ablation (RFA) of the liver is reviewed in this paper. In particular, the concept of active needle is proposed for RFA treatment. Methods and techniques of image processing and modeling are presented for a stereo liver model. The liver model and constituent components extracted from computerized tomography (CT) images can be used to plan the navigation paths of the RFA needle. The system also provides an option for active needles, which are more amenable to those refractory cases of RFA treatment.