Automatic analysis of muscular activity in the flexor digitorum superficialis muscles: A fast screening method for rapid eye movement sleep without atonia

Study objectives To identify a fast and reliable method for rapid eye movement (REM) sleep without atonia (RWA) quantification. Methods We analyzed 36 video-polysomnographies (v-PSGs) of isolated REM sleep behavior disorder (iRBD) patients and 35 controls’ v-PSGs. Patients diagnosed with RBD had: i) RWA, quantified with a reference method, i.e. automatic and artifact-corrected 3-s Sleep Innsbruck Barcelona (SINBAR) index in REM sleep periods (RSPs, i.e. manually selected portions of REM sleep); and ii) v-PSG-documented RBD behaviors. We quantified RWA with other (semi)-automated methods requiring less human intervention than the reference one: the indices proposed by the SINBAR group (the 3-s and 30-s phasic flexor digitorum superficialis (FDS), phasic/”any”/tonic mentalis), and the REM atonia, short and long muscle activity indices (in mentalis/submentalis/FDS muscles). They were calculated in whole REM sleep (i.e. REM sleep scored following international guidelines), in RSPs, with and without manual artifact correction. Area under curves (AUC) discriminating iRBD from controls were computed. Using published cut-offs, the indices’ sensitivity and specificity for iRBD identification were calculated. Apnea-hypopnea index in REM sleep (AHIREM) was considered in the analyses. Results RWA indices from FDS muscles alone had the highest AUCs and all of them had 100% sensitivity. Without manual RSP selection and artifact correction, the “30-s phasic FDS” and the “FDS long muscle activity” had the highest specificity (85%) with AHIREM<15/h. RWA indices were less reliable when AHIREM≥15/h. Conclusions If AHIREM<15/h, FDS muscular activity in whole REM sleep and without artifact correction is fast and reliable to rule out RWA.

Implementation and experimental evaluation of Mega-voltage fan-beam CT using a linear accelerator

Background Mega-voltage fan-beam Computed Tomography (MV-FBCT) holds potential in accurate determination of relative electron density (RED) and proton stopping power ratio (SPR) but is not widely available. Objective To demonstrate the feasibility of MV-FBCT using a medical linear accelerator (LINAC) with a 2.5 MV imaging beam, an electronic portal imaging device (EPID) and multileaf collimators (MLCs). Methods MLCs were used to collimate MV beam along z direction to enable a 1 cm width fan-beam. Projection data were acquired within one gantry rotation and preprocessed with in-house developed artifact correction algorithms before the reconstruction. MV-FBCT data were acquired at two dose levels: 30 and 60 monitor units (MUs). A Catphan 604 phantom was used to evaluate basic image quality. A head-sized CIRS phantom with three configurations of tissue-mimicking inserts was scanned and MV-FBCT Hounsfield unit (HU) to RED calibration was established for each insert configuration using linear regression. The determination coefficient ($${R}^{2}$$ R 2 ) was used to gauge the accuracy of HU-RED calibration. Results were compared with baseline single-energy kilo-voltage treatment planning CT (TP-CT) HU-RED calibration which represented the current standard clinical practice. Results The in-house artifact correction algorithms effectively suppressed ring artifact, cupping artifact, and CT number bias in MV-FBCT. Compared to TP-CT, MV-FBCT was able to improve the prediction accuracy of the HU-RED calibration curve for all three configurations of insert materials, with $${R}^{2}$$ R 2 > 0.9994 and $${R}^{2}$$ R 2 < 0.9990 for MV-FBCT and TP-CT HU-RED calibration curves of soft-tissue inserts, respectively. The measured mean CT numbers of blood-iodine mixture inserts in TP-CT drastically deviated from the fitted values but not in MV-FBCT. Reducing the radiation level from 60 to 30 MU did not decrease the prediction accuracy of the MV-FBCT HU-RED calibration curve. Conclusion We demonstrated the feasibility of MV-FBCT and its potential in providing more accurate RED estimation.

A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking

Functional near infrared spectroscopy (fNIRS) is a neuroimaging technique that allows to monitor the functional hemoglobin oscillations related to cortical activity. One of the main issues related to fNIRS applications is the motion artefact removal, since a corrupted physiological signal is not correctly indicative of the underlying biological process. A novel procedure for motion artifact correction for fNIRS signals based on wavelet transform and video tracking developed for infrared thermography (IRT) is presented. In detail, fNIRS and IRT were concurrently recorded and the optodes’ movement was estimated employing a video tracking procedure developed for IRT recordings. The wavelet transform of the fNIRS signal and of the optodes’ movement, together with their wavelet coherence, were computed. Then, the inverse wavelet transform was evaluated for the fNIRS signal excluding the frequency content corresponding to the optdes’ movement and to the coherence in the epochs where they were higher with respect to an established threshold. The method was tested using simulated functional hemodynamic responses added to real resting-state fNIRS recordings corrupted by movement artifacts. The results demonstrated the effectiveness of the procedure in eliminating noise, producing results with higher signal to noise ratio with respect to another validated method.