Multi-modality intravascular imaging for guiding coronary intervention and assessing coronary atheroma: the Novasight Hybrid IVUS-OCT system.

Intravascular imaging has evolved alongside interventional cardiology as an adjunctive tool for assessing plaque pathology and for guiding and optimizing percutaneous coronary intervention (PCI) in challenging lesions. The two modalities which have dominated the field are intravascular ultrasound (IVUS), which relies on sound waves and optical coherence tomography (OCT), relying on light waves. These approaches however have limited efficacy in assessing plaque morphology and vulnerability that are essential for guiding PCI in complex lesions and identifying patient at risk that will benefit from emerging therapies targeting plaque evolution. These limitations are complementary and, in this context, it has been recognized and demonstrated in multi-modality studies that the concurrent use of IVUS and OCT can help overcome these deficits enabling a more complete and accurate plaque assessment. The Conavi Novasight Hybrid IVUS-OCT catheter is the first commercially available device that is capable of invasive clinical coronary assessment with simultaneously acquired and co-registered IVUS and OCT imaging. It represents a significant evolution in the field and is expected to have broad application in clinical practice and research. In this review article we present the limitations of standalone intravascular imaging techniques, summarize the data supporting the value of multimodality imaging in clinical practice and research, describe the Novasight Hybrid IVUS-OCT system and highlight the potential utility of this technology in coronary intervention and in the study of atherosclerosis.

The effect of accelerometer location on the classification of single-site forearm mechanomyograms.

BACKGROUND: Recently, pattern recognition methods have been deployed in the classification of multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces. Given the propagative properties of MMG signals, it has been suggested that MMG classification should be robust to changes in sensor placement. Nonetheless, this purported robustness remains speculative to date. This study sought to quantify the change in classification accuracy, if any, when a classifier trained with MMG signals from the muscle belly, is subsequently tested with MMG signals from a nearby location. METHODS: An arrangement of 5 accelerometers was attached to the flexor carpi radialis muscle of 12 able-bodied participants; a reference accelerometer was located over the muscle belly, two peripheral accelerometers were positioned along the muscle's transverse axis and two more were aligned to the muscle's longitudinal axis. Participants performed three classes of muscle activity: wrist flexion, wrist extension and semi-pronation. A collection of time, frequency and time-frequency features were considered and reduced by genetic feature selection. The classifier, trained using features from the reference accelerometer, was tested with signals from the longitudinally and transversally displaced accelerometers. RESULTS: Classification degradation due to accelerometer displacement was significant for all participants, and showed no consistent trend with the direction of displacement. Further, the displaced accelerometer signals showed task-dependent de-correlations with respect to the reference accelerometer. CONCLUSIONS: These results indicate that MMG signal features vary with spatial location and that accelerometer displacements of only 1-2 cm cause sufficient feature drift to significantly diminish classification accuracy. This finding emphasizes the importance of consistent sensor placement between MMG classifier training and deployment for accurate control of switching interfaces.

The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements.

BACKGROUND: Individuals with severe physical disabilities and minimal motor behaviour may be unable to use conventional mechanical switches for access. These persons may benefit from access technologies that harness the volitional activity of muscles. In this study, we describe the design and demonstrate the performance of a binary switch controlled by mechanomyogram (MMG) signals recorded from the frontalis muscle during eyebrow movements. METHODS: Muscle contractions, detected in real-time with a continuous wavelet transform algorithm, were used to control a binary switch for computer access. The automatic selection of scale-specific thresholds reduced the effect of artefact, such as eye blinks and head movement, on the performance of the switch. Switch performance was estimated by cued response-tests performed by eleven participants (one with severe physical disabilities). RESULTS: The average sensitivity and specificity of the switch was 99.7 +/- 0.4% and 99.9 +/- 0.1%, respectively. The algorithm performance was robust against typical participant movement. CONCLUSIONS: The results suggest that the frontalis muscle is a suitable site for controlling the MMG-driven switch. The high accuracies combined with the minimal requisite effort and training show that MMG is a promising binary control signal. Further investigation of the potential benefits of MMG-control for the target population is warranted.

30/80 MHz Bidirectional Dual-Frequency IVUS Feasibility Evaluated In Vivo and for Stent Imaging.

Although intravascular ultrasound (IVUS) is an important tool in guiding complex coronary interventions, the resolution of existing commercial IVUS devices is considerably poorer than that of optical coherence tomography. Dual-frequency IVUS (DF IVUS), incorporating a second, higher frequency transducer, has been proposed as a possible method of overcoming this limitation. Although preliminary studies have shown that DF IVUS can produce complementary images, including large-scale morphology and high detail of superficial features, it has not yet been determined that this approach would be feasible in a more clinically relevant environment. The purpose of this study was to demonstrate the first in vivo use of a 30/80 MHz DF IVUS catheter in visualizing coronary vessels in a porcine model. In addition, two commercially available stents were studied in vitro and in vivo. Clear subjective improvement of visualization of superficial structures is demonstrated, and sufficient dynamic range is achieved to image through both the catheter sheath and blood in vivo.

Cardiac Tissue-Mimicking Ballistic Gel Phantom for Ultrasound Imaging in Clinical and Research Applications.

Ballistic gel was investigated as a tissue-mimicking material in an anthropomorphic cardiac phantom for ultrasound imaging. The gel was tested for its acoustic properties and its compatibility with conventional plastics molding techniques. Speed of sound and attenuation were evaluated in the range 2-12 MHz. The speed of sound was 1537 ± 39 m/s, close to typical values for cardiac tissue (∼1576 m/s). The attenuation coefficient was 1.07 dB/cm·MHz, within the range of values previously reported for cardiac tissue (0.81-1.81 dB/cm·MHz). A cardiac model based on human anatomy was developed using established image segmentation processes and conventional plastic molding techniques. Key anatomic features were observed, captured and identified in the model using an intracardiac ultrasound imaging system. These favorable results along with the material's durability and processes that allow for repetitive production of detailed whole-heart models at low cost are promising. There are numerous applications for geometrically complex phantoms in research, training, device development and clinical use.

Stationarity distributions of mechanomyogram signals from isometric contractions of extrinsic hand muscles during functional grasping.

This study investigates the stationarity of steady state mechanomyogram signals for the purpose of determining appropriate features for signal classification. Mechanomyography is the superficial recording of low frequency vibrations detected over contracting muscles. Steady state mechanomyogram signals, recorded at the belly of the extensor digitorum, flexor digitorum superficialis and flexor pollicis longus muscles during functional grasps were tested for weak stationarity. Twenty percent of the contractions were found to be non-stationary, indicating that time frequency methods may be appropriate for automatic pattern recognition of functional grasp from the mechanomyogram. The distribution of the stationary test statistic was dependent on the type of muscle contractions, suggesting that the test statistic itself might be a discriminating feature for mechanomyogram pattern recognition in applications such as multifunction prosthetic control. Since the major known source of non-stationarity was decreasing variance, it is suggested that shifts in the distribution of the test statistic may indicate the time course of relative muscle contributions to functional grasp.

Panorama clínico, epidemiológico e espacial da ocorrência de Leishmaniose visceral no estado do Pará, Amazônia brasileira / Clinical, epidemiological and spatial overview of visceral Leishmaniasis in the state of Pará, brazilian amazonia / Panorama clínico, epidemiológico y espacial de la Leishmaniasis visceral en el estado de Pará, amazonia brasileña

Objetivo: Essa pesquisa teve como objetivo determinar o perfil clínico, epidemiológico e espacial daLeishmaniose Visceral, bem como, sua associação com o desmatamento nos municípios pertencentesao 12º centro regional de saúde no Sudeste do Estado do Pará, Brasil de 2016 a 2020. Método: Trata-se de um estudo analítico ecológico, realizado com dados provenientes de 15 municípios do 12º Centro Regional de Saúde, obtidos por meio do banco de dados do Data-SUS-TABNET, através do SINAN. Resultados: Para o período do estudo foram notificados 415 casos de LV nos municípios analisados, o que correspondeu a uma média anual de 83 casos. O ano com maior número de notifi- cações foi 2017, apresentando 34,7%, sendo o município de Redenção com o maior número de casos.Conclusões: Portanto, há necessidade de ampliação das medidas de controle e vigilância da LV, comfoco na notificação de casos, a fim de realizar a obtenção do panorama fidedigno da LV e elaborar estratégias mais assertivas para seu controle e mitigação.

Objective: This research aimed to determine the clinical, epidemiological and spatial profile of Vis- ceral Leishmaniasis, as well as its association with deforestation in the municipalities belonging to the 12th regional health center in the Southeast of Pará State, Brazil from 2016 to 2020. Method: Thisis an ecological analytical study, conducted with data from 15 municipalities of the 12th Regional Health Center, obtained through the Data-SUS-TABNET database, through SINAN. Results: For thestudy period, 415 cases of VL were reported in the analyzed municipalities, corresponding to an an-nual average of 83 cases. The year with the highest number of notifications was 2017, present- ing 34.7%, being the municipality of Redenção with the highest number of cases. Conclu- sion: Therefore,there is a need to expand VL control and surveillance measures, focusing on the notification of casesin order to obtain a reliable picture of VL and develop more assertive strategies for its control and mitigation.

Objetivo: Esta investigación tuvo como objetivo determinar el perfil clínico, epidemiológico y espacial de la Leishmaniasis Visceral, así como su asociación con la deforestación en municipios pertenecientes al 12º Centro Regional de Salud del Sudeste del Estado de Pará, Brasil, de 2016 a 2020. Método: Trata-se de um estudo analítico ecológico, realizado com dados provenientes de 15 municípios do 12º Centro Regional de Saúde, obtidos por meio do banco de dados do Data-SUS-TABNET, através do SINAN. Resultados: Durante el período de estudio, fueron notificados 415 casos de LV en los municipios analizados, correspondiendo a una media anual de 83 casos. El año con mayor número de notificaciones fue 2017, 34,7%, y el municipio de Redenção presentó el mayor número de casos. Conclusiones: Por lo tanto, es necesario ampliar las medidas de control y vigilancia de la LV, centrándose en la notificación de casos con el fin de obtener una imagen fiable de la LV y desarrollar estrategias más asertivas para su control y mitigación.

An MEG-Compatible Electromagnetic-Tracking System for Monitoring Orofacial Kinematics.

We describe a novel motion-tracking system, called MASK (magnetoarticulography for the assessment of speech kinematics) designed to track detailed orofacial movements during magnetoencephalographic (MEG) measures of human brain activity. A three-dimensional electromagnetic-tracking method was employed using lightweight coils energized with high-frequency sinusoidal currents, creating magnetic dipoles that can be continuously localized by the MEG sensors. In addition to being compatible with commercial MEG devices, this system has advantages over optical or video methods in that it can record nonline-of-sight movements (e.g., tongue movements) and advantages over surface electromyographic recordings, which are prone to movement-related artifacts and signal crosstalk. Static and dynamic tracking accuracy was evaluated using calibration devices with fixed intercoil distances. MEG data were collected in two healthy adult volunteers to test feasibility of tracking movements during tongue and facial movement, and during overt speech. The MASK system was shown to have sufficient static and dynamic accuracy to track orofacial movements within the MEG helmet. We successfully acquired spatially precise kinematic information time-locked to brain activity with high temporal resolution. We demonstrated successful tracking of oromotor and speech movements together with brain activity using the MASK system. This novel technology will provide an innovative tool in support of research and clinical applications for individuals with speech and other oromotor disorders.

Mechanomyography as an access pathway: corporeal contraindications.

PURPOSE: The purpose of this study is to delineate the impairments of body functions and structures which specifically inhibit mechanomyography (MMG)-based switch control as an alternative access pathway. METHOD: Seven individuals with severe physical disabilities and varied diagnoses of neuromuscular or neurological conditions tested a MMG switch. A semi-structured protocol was used to gather quantitative and qualitative indications of the switch's performance, and descriptive perceptions of the participants and their care givers. RESULTS: The participants controlled the switch by contracting muscles of their forehead, forearm or shoulder. Body functions and structures that negatively affect the signal-to-noise ratio (SNR) of the recorded MMG signal included involuntary dystonic movement, impaired muscle control, atrophied muscles, muscle spasticity and involuntary activity of neighbouring muscles. CONCLUSIONS: The MMG switch is strongly recommended where the muscle site and its control are intact and signal artefact is minimal. Its viability when muscle activity at the access site may be confounded by signal artefact is dependent on the strength of the voluntary muscle contraction relative to that of artefacts such as spastic contractions, involuntary dystonic movements, muscle spasms and physiological vibrations. Neurological conditions, such as spasticity, that compromise the user's ability to voluntarily contract or suppress muscle contractions may be considered contraindications to MMG-based access.

Uncovering patterns of forearm muscle activity using multi-channel mechanomyography.

A coordinated activation of distal forearm muscles allows the hand and fingers to be shaped during movement and grasp. However, little is known about how the muscle activation patterns are reflected in multi-channel mechanomyogram (MMG) signals. The purpose of this study is to determine if multi-site MMG signals exhibit distinctive patterns of forearm muscle activity. MMG signals were recorded from forearm muscle sites of nine able-bodied participants during hand movement. By using 14 features selected by a genetic algorithm and classified by a linear discriminant analysis classifier (LDA), we show that MMG patterns are specific and consistent enough to identify 7+/-1 hand movements with an accuracy of 90+/-4%. MMG-based movement recognition required a minimum of three recording sites. Further, by classifying five classes of contraction patterns with 98+/-3% accuracy from MMG signals recorded from the residual limb of an amputee participant, we demonstrate that MMG shows pattern-specificity even in the absence of typical musculature. Multi-site monitoring of the RMS of MMG signals is suggested as a method of estimating the relative contributions of muscles to motor tasks. The patterns in MMG facilitate our understanding of the mechanical activity of muscles during movement.

Recognition of forearm muscle activity by continuous classification of multi-site mechanomyogram signals.

Recent studies on identifying multiple activation states from mechanomyogram (MMG) signals for the purpose of controlling switching interfaces have employed pattern recognition methods where MMG signal features from multiple muscle sites are extracted and classified. The purpose of this study is to determine if MMG signal features retain enough discriminatory information to allow reliable continuous classification, and to determine if there is a decline in classification accuracy over short time periods. MMG signals were recorded from two accelerometers attached to the flexor carpi radialis and extensor carpi radialis muscles of 12 able-bodied participants as participants performed three classes of forearm muscle activity. The data were collected over five recording sessions, with a ten-minute interval between each session. The data were spliced into 256 ms epochs, and a comprehensive set of signal features was extracted. A pattern classifier, trained with continuously acquired signal features from the first recording session, was tested with signals recorded from the other sessions. The average classification accuracy over the five sessions was 89 ± 2%. There was no obvious declining trend in classification accuracy with time. These results show that MMG signals recorded at the forearm retain enough discriminatory information to allow continuous recognition of hand motion across multiple (>90) repetitions, and the MMG-classifier does not show short-term degradation. These results indicate the potential of MMG as a multifunction control signal for muscle-machine interfaces.

Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods.

Knowledge of muscle activity timing is critical to many clinical applications, such as the assessment of muscle coordination and the prescription of muscle-activated switches for individuals with disabilities. In this study, we introduce a continuous wavelet transform (CWT) algorithm for the detection of muscle activity via mechanomyogram (MMG) signals. CWT coefficients of the MMG signal were compared to scale-specific thresholds derived from the baseline signal to estimate the timing of muscle activity. Test signals were recorded from the flexor carpi radialis muscles of 15 able-bodied participants as they squeezed and released a hand dynamometer. Using the dynamometer signal as a reference, the proposed CWT detection algorithm was compared against a global-threshold CWT detector as well as amplitude-based event detection for sensitivity and specificity to voluntary contractions. The scale-specific CWT-based algorithm exhibited superior detection performance over the other detectors. CWT detection also showed good muscle selectivity during hand movement, particularly when a given muscle was the primary facilitator of the contraction. This may suggest that, during contraction, the compound MMG signal has a recurring morphological pattern that is not prevalent in the baseline signal. The ability of CWT analysis to be implemented in real time makes it a candidate for muscle-activity detection in clinical applications.

A novel integrated mechanomyogram-vocalization access solution.

We introduce a novel dual-switch control paradigm based on the simultaneous measurement of frontalis muscle mechanomyography (MMG) and vocalizations (humming) using a single contact microphone attached to the forehead. Vibrations of the face and skull during vocalization are manifested as periodic high-frequency components in the microphone signal recorded at the forehead. The presence of these periodic components is detected by a normalized cross-correlation function, while muscle contractions are detected using a continuous wavelet transform method. The dual-switch provides two independent binary control signals. Eleven participants, including one individual with severe physical disabilities, participated in a cued activation task in which the dual-switch exhibited sensitivities and specificities of 96.8±3% and 98.4±1%, respectively for vocalizations, and 99.7±0.5% and 99.2±0.5%, respectively for muscle contractions. Since skin vibrations due to voiced sounds and muscle contractions have non-overlapping dominant bandwidths, the performance of the MMG switch was not affected by vocalizations. This new integrated MMG-vocalization access solution affords the user two binary switches from a single access site, and may thus augment access alternatives for certain individuals with severe physical disabilities.

Classification of the mechanomyogram: its potential as a multifunction access pathway.

Although the mechanomyogram (MMG) has been demonstrated as a viable representation of muscle activity, its potential as a multifunction (>2) control signal has not yet been investigated. This study investigates the discriminability of multiple hand motions using multichannel forearm MMG. With nine able-bodied participants, MMG signals from six sites could be differentiated among eight classes of forearm muscle activity with a mean accuracy of 93+/-9% using 15 features selected by a genetic algorithm and classified by a linear discriminant analysis classifier. These results suggest that, with additional research, MMG may indeed become a usable control signal for multifunction access devices.

Vision-based segmentation of continuous mechanomyographic grasping sequences.

In detecting motor related activity from mechanomyographic (MMG) recordings, the acquisition of long, continuous streams of MMG signals is typically preferred over the painstaking collection of individual, isolated contractions. However, a major challenge with continuous collection is the subsequent separation of the MMG data stream into segments representing individual contractions. This paper proposes a method for segmenting continuously recorded MMG data streams using computer vision while providing a highly reduced set of key images for rapid human expert verification. Transverse plane video recordings of functional grasp sequences were synchronized with the acquisition of MMG signals from the forearm. An automatic, vision-based algorithm exploiting skin color detection, motion estimation, and template matching provided segmentation cues for MMG signals arising from multiple grips. The automatic segmentation method tolerated extraneous hand movements, differentiated among multiple grips and estimated grip transition times. Our implementation segmented two grips with an average accuracy of 97.8 -/+ 4%, and up to seven grips with an accuracy of 73 -/+ 20%. The automatically extracted contraction initiation and termination times were within 173 -/+ 133 ms of the times obtained via manual segmentation. It is suggested that the proposed method would be particularly conducive to the assembly of large collections of signals for training MMG-driven prostheses.

Vision-based segmentation of continuous mechanomyographic grasping sequences for training multifunction prostheses.

In designing mechanomyographic (MMG) signal classifiers for prosthetic control, the acquisition of long, continuous streams of MMG signals is typically preferred over the painstaking collection of individual, isolated contractions. However, a major challenge with continuous collection is the subsequent separation of the MMG data stream into segments representing individual contractions. This paper proposes an automatic, vision-based segmentation method for continuously recorded MMG data streams. MMG data acquisition was synchronized with transverse plane video acquisition of functional grip sequences. The automatic segmentation system can track a hand, recognize grips and detect grip transition times as well as extraneous hand movements. The system recognizes two grips with an average accuracy of 97.8 +/- 4%, and seven grips with an accuracy of 73 +/- 20%. The contraction initiation and termination times agree closely (within 1.3 +/- 1 frames) with values obtained manually.