A study on observed ultrasonic motor-induced magnetic resonance imaging (MRI) artifacts.

BACKGROUND: The safe performance of magnetic resonance imaging (MRI)-guided robot-assisted interventions requires full control and high precision of assistive devices. Because many currently available tools are not MRI-compatible, the characterization of existing tools and development of new ones are necessary. The purpose of this research is to identify and minimize the image artifacts generated by a USM in MR images. METHODS: The behavior of an ultrasonic motor (USM), the most common MRI-safe actuator, in a high-field scanner was investigated. The motor was located in three orientations with respect to the bore axis with the power on or off. The induced image artifacts were compared across four sequences. Three artifact reduction methods (employing ultrashort sequences, slice thickness reductions, and bandwidth increments) were tested. RESULTS: Signal voids, pileups, and geometric distortions were observed when the motor was off. The artifact size was minimal when the motor shaft was aligned with the bore axis. In addition to the above artifacts, zipper and motion artifacts were noted when the motor was running, and these artifacts increased with increasing motor speed. Increasing the bandwidth slightly reduced the artifacts. However, decreasing the slice thickness from 5 mm to 3 mm and from 5 mm to 1 mm reduced artifact size from 30% to 40% and from 60% to 75%, respectively. CONCLUSION: The image artifacts were due to the non-homogenous nature of the static and gradient fields caused by the motor structure. The operating motor interferes with the RF field, causing zipper and motion artifacts.

Signal-to-noise ratio evaluation of magnetic resonance images in the presence of an ultrasonic motor.

BACKGROUND: Safe robot-assisted intervention using magnetic resonance imaging (MRI) guidance requires the precise control of assistive devices, and most currently available tools are rarely MRI-compatible. To obtain high precision, it is necessary to characterize and develop existing MRI-safe actuators for use in a high magnetic field (≥3 T). Although an ultrasonic motor (USM) is considered to be an MRI-safe actuator, and can be used in the vicinity of a high field scanner, its presence interferes with MR images. Although an MR image provides valuable information regarding the pathology of a patient's body, noise, generally of a granular type, decreases the quality of the image and jeopardizes the true evaluation of any existing pathological issues. An eddy current induced in the conductor material of the motor structure can be a source of noise when the motor is close to the isocenter of the image. We aimed to assess the effects of a USM on the signal-to-noise ratio (SNR) of MR images in a 3-T scanner. The SNR was compared for four image sequences in transverse directions for three orientations of the motor (x, y, and z) when the motor was in the "off" state. The SNR was evaluated to assess three artifact reduction methods used to minimize the motor-induced artifacts. RESULTS: The SNR had a range of 5-10 dB for slices close to the motor in the x and y orientations, and increased to 15-20 dB for slices far from the motor. Averaging the SNR for slices in all cases gave an SNR loss of about 10 dB. The maximum SNR was measured in the z orientation. In this case, the SNR loss was almost the same as that of other motor orientations, approximately 10 dB, but with a higher range, approximately 20-40 dB. CONCLUSIONS: The selection of certain scanning parameters is necessary for reducing motor-generated artifacts. These parameters include slice selection and bandwidth. In developing any MRI-compatible assisted device actuated by a USM, this study recommends the use of an approximately 3-mm slice thickness with minimum bandwidth to achieve optimized SNR values when a USM is operating close to (within approximately 40 mm) the region being imaged. The SNR can be further enhanced by increasing the number of signal averages, but this is achieved only at the cost of increased scan duration.

Automated detection of nocturnal slow eye movements modulated by selective serotonin reuptake inhibitors.

INTRODUCTION: Eye movements convey important information about brain function. Neuropsychiatric conditions and medications may produce abnormal eye movements (EMs) in sleep. Serotonergic drugs are known to increase nocturnal EMs. Few studies have quantified sleep EMs due to technical complexity. We investigated the effects of serotonergic drugs on slow EMs in sleeping patients using an objective automated EM quantification tool. METHODS: We studied patients who had polysomnograms for clinical assessment. We identified 5 sertraline, 7 citalopram, and 4 fluoxetine users who were not using other psychoactive medications. Controls were 10 age-matched patients on no medications. An automated objective EM detection tool was developed. The first and last 5 min of each sleep stage were assessed. ANOVA was used to assess the effects of time, stage, and medication. RESULTS: No differences were noted between SSRI users and controls in demographics. Medications were associated with more slow EMs compared to controls (p<0.05). Among SSRI users, a non-statistical trend was noted for increasing slow EMs in the following pattern: sertraline

Computer-assisted method for quantifying sleep eye movements that reflects medication effects.

A significant amount of data is not attended to clinically in routine sleep studies. Measures of sleep physiology not obvious to the human eye may provide important clues to disease states, and responses to therapy. For example, it has been noted that eye movements change significantly in patients exposed to antidepressant medications. This paper describes how eye movements were different in depressed patients who used antidepressant medications, compared to those who did not. Groups 1 and 2 included five patients each who used citalopram and venlafaxine respectively compared to five patients not taking any antidepressants. Autoregressive (AR) coefficients of eye movements recorded during sleep have been derived. These coefficients represent the shape of the sleep eye movements of all three groups and were classified using discriminant analysis. In this paper, an improved methodology has been used for this classification. This method includes eye movement detection with improved eye movement detection software and evaluation of AR coefficients with fixed segments. The AIC method has been used for determination of an appropriate model order of 27. AR coefficients are then derived on the basis of this optimized value and are then classified with a linear discriminant function. The overall average of the regular method accuracies were 76.4%, and 78.7% for groups 1 and 2 respectively. The overall average of the leave-one-out method accuracies were 75.5% and 77.5% for Groups 1 and 2. The results demonstrate that eye movements can be quantified and characterized with this approach. This methodology will allow the development of new metrics that may assist in disease classification, and response to treatment in a variety of neuropsychiatric conditions.