Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks

Existing approaches for automated tracking of fascicle length (FL) and pennation angle (PA) rely on the presence of a single, user-defined fascicle (feature tracking) or on the presence of a specific intensity pattern (feature detection) across all the recorded ultrasound images. These prerequisites are seldom met during large dynamic muscle movements or for deeper muscles that are difficult to image. Deep-learning approaches are not affected by these issues, but their applicability is restricted by their need for large, manually analyzed training data sets. To address these limitations, the present study proposes a novel approach that tracks changes in FL and PA based on the distortion pattern within the fascicle band. The results indicated a satisfactory level of agreement between manual and automated measurements made with the proposed method. When compared against feature tracking and feature detection methods, the proposed method achieved the lowest average root mean squared error for FL and the second lowest for PA. The strength of the proposed approach is that the quantification process does not require a training data set and it can take place even when it is not possible to track a single fascicle or observe a specific intensity pattern on the ultrasound recording.UK-India Education and Research Initiative (UKIERI)Department of Science and Technology (DST), New DelhiPeer Reviewe

Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks

Existing approaches for automated tracking of fascicle length (FL) and pennation angle (PA) rely on the presence of a single, user-defined fascicle (feature tracking) or on the presence of a specific intensity pattern (feature detection) across all the recorded ultrasound images. These prerequisites are seldom met during large dynamic muscle movements or for deeper muscles that are difficult to image. Deep-learning approaches are not affected by these issues, but their applicability is restricted by their need for large, manually analyzed training data sets. To address these limitations, the present study proposes a novel approach that tracks changes in FL and PA based on the distortion pattern within the fascicle band. The results indicated a satisfactory level of agreement between manual and automated measurements made with the proposed method. When compared against feature tracking and feature detection methods, the proposed method achieved the lowest average root mean squared error for FL and the second lowest for PA. The strength of the proposed approach is that the quantification process does not require a training data set and it can take place even when it is not possible to track a single fascicle or observe a specific intensity pattern on the ultrasound recording

Automated Method for Tracking Human Muscle Architecture on Ultrasound Scans during Dynamic Tasks

Existing approaches for automated tracking of fascicle length (FL) and pennation angle (PA) rely on the presence of a single, user-defined fascicle (feature tracking) or on the presence of a specific intensity pattern (feature detection) across all the recorded ultrasound images. These prerequisites are seldom met during large dynamic muscle movements or for deeper muscles that are difficult to image. Deep-learning approaches are not affected by these issues, but their applicability is restricted by their need for large, manually analyzed training data sets. To address these limitations, the present study proposes a novel approach that tracks changes in FL and PA based on the distortion pattern within the fascicle band. The results indicated a satisfactory level of agreement between manual and automated measurements made with the proposed method. When compared against feature tracking and feature detection methods, the proposed method achieved the lowest average root mean squared error for FL and the second lowest for PA. The strength of the proposed approach is that the quantification process does not require a training data set and it can take place even when it is not possible to track a single fascicle or observe a specific intensity pattern on the ultrasound recording

Automated Strategies in Multimodal and Multidimensional Ultrasound Image-based Diagnosis

Medical ultrasonography is an effective technique in traditional anatomical and functional diagnosis. However, it requires the visual examination by experienced clinicians, which is a laborious, time consuming and highly subjective procedure. Computer-aided diagnosis (CADx) have been extensively used in clinical practice to support the interpretation of images; nevertheless, current ultrasound CADx still entails a substantial user-dependency and are unable to extract image data for prediction modelling. The aim of this thesis is to propose a set of fully automated strategies to overcome the limitations of ultrasound CADx. These strategies are addressed to multiple modalities (B-Mode, Contrast-Enhanced Ultrasound-CEUS, Power Doppler-PDUS and Acoustic Angiography-AA) and dimensions (2-D and 3-D imaging). The enabling techniques presented in this work are designed, developed and quantitively validated to efficiently improve the overall patients’ diagnosis. This work is subdivided in 2 macro-sections: in the first part, two fully automated algorithms for the reliable quantification of 2-D B-Mode ultrasound skeletal muscle architecture and morphology are proposed. In the second part, two fully automated algorithms for the objective assessment and characterization of tumors’ vasculature in 3-D CEUS and PDUS thyroid tumors and preclinical AA cancer growth are presented. In the first part, the MUSA (Muscle UltraSound Analysis) algorithm is designed to measure the muscle thickness, the fascicles length and the pennation angle; the TRAMA (TRAnsversal Muscle Analysis) algorithm is proposed to extract and analyze the Visible Cross-Sectional Area (VCSA). MUSA and TRAMA algorithms have been validated on two datasets of 200 images; automatic measurements have been compared with expert operators’ manual measurements. A preliminary statistical analysis was performed to prove the ability of texture analysis on automatic VCSA in the distinction between healthy and pathological muscles. In the second part, quantitative assessment on tumor vasculature is proposed in two automated algorithms for the objective characterization of 3-D CEUS/Power Doppler thyroid nodules and the evolution study of fibrosarcoma invasion in preclinical 3-D AA imaging. Vasculature analysis relies on the quantification of architecture and vessels tortuosity. Vascular features obtained from CEUS and PDUS images of 20 thyroid nodules (10 benign, 10 malignant) have been used in a multivariate statistical analysis supported by histopathological results. Vasculature parametric maps of implanted fibrosarcoma are extracted from 8 rats investigated with 3-D AA along four time points (TPs), in control and tumors areas; results have been compared with manual previous findings in a longitudinal tumor growth study. Performance of MUSA and TRAMA algorithms results in 100% segmentation success rate. Absolute difference between manual and automatic measurements is below 2% for the muscle thickness and 4% for the VCSA (values between 5-10% are acceptable in clinical practice), suggesting that automatic and manual measurements can be used interchangeably. The texture features extraction on the automatic VCSAs reveals that texture descriptors can distinguish healthy from pathological muscles with a 100% success rate for all the four muscles. Vascular features extracted of 20 thyroid nodules in 3-D CEUS and PDUS volumes can be used to distinguish benign from malignant tumors with 100% success rate for both ultrasound techniques. Malignant tumors present higher values of architecture and tortuosity descriptors; 3-D CEUS and PDUS imaging present the same accuracy in the differentiation between benign and malignant nodules. Vascular parametric maps extracted from the 8 rats along the 4 TPs in 3-D AA imaging show that parameters extracted from the control area are statistically different compared to the ones within the tumor volume. Tumor angiogenetic vessels present a smaller diameter and higher tortuosity. Tumor evolution is characterized by the significant vascular trees growth and a constant value of vessel diameter along the four TPs, confirming the previous findings. In conclusion, the proposed automated strategies are highly performant in segmentation, features extraction, muscle disease detection and tumor vascular characterization. These techniques can be extended in the investigation of other organs, diseases and embedded in ultrasound CADx, providing a user-independent reliable diagnosis

The application of b-mode ultrasonography for analysis of human skeletal muscle

Skeletal muscles control the joints of the skeletal system and they allow human movement and interaction with the environment. They are vital for stability in balance, walking and running, and many other skilled motor tasks. To understand how muscles operate in general and specific situations there are a variety of tools at the disposal of research scientists and clinicians for analysing muscle function. Strain gauges for example allow the quantification of forces exerted during joint rotation. However, skeletal muscles are multilayer systems and often different muscles are responsible for the overall force generated during joint rotation. Therefore, strain gauges do not reveal the extent of the contribution of individual muscles during muscle function. The most widely-used and accepted muscle analysis tool is electromyography (EMG), which can measure the activation level of individual muscles by measuring the electrical potential propagating through muscle resulting from local activations of motor units. However, EMG does not linearly relate to any real physical forces, meaning that without prior knowledge of the force exertion on the level of the muscle, force cannot be estimated. EMG can measure superficial layers of muscle non-invasively by attaching surface electrodes (surface EMG) to the skin over the belly of the muscle. To measure the activity of individual muscle beneath the superficial muscle, a needle or thin-wire electrode must be inserted through the skin and into the muscle volume (intramuscular EMG), which is invasive and not practical in many situations. Furthermore, intramuscular EMG can only provide measurement of a very small volume (<1mm3) which can have varying amounts of active motor units. Ultrasonography is a powerful cost-effective non-invasive imaging technology which allows real-time observation of cross-sections of multiple layers of dynamic skeletal muscle. Recent advances in automated skeletal muscle ultrasound analysis techniques, and advances in image processing techniques make ultrasound a valuable line of investigation for analysis of dynamic skeletal muscle. This aim of this thesis is to study and develop advanced image analysis techniques applicable to the analysis of dynamic skeletal muscle. The broader aim is to understand the capacity/limits of ultrasound as a skeletal muscle analysis tool. The ideas presented within offer new approaches to modelling complex muscle architecture and function via ultrasound. Tools have also been developed here that will contribute to, and promote ultrasound skeletal muscle analysis as a new and emerging technology which may be used by clinicians and research scientists to develop our understanding of skeletal muscle function. The main findings of this thesis are that automated segmentation of architecturally simple and complex skeletal muscle groups is possible and accurate, and that information about joint angles and muscle activity/force can be automatically extracted directly from ultrasound images without the explicit knowledge of how to extract it. The techniques used offer new possibilities for non-invasive information extraction from complex muscle groups such as the muscles in the human posterior neck

In vivo Quantifizierung und Implementierung der Dynamik der Muskelarchitekturparameter Faserwinkel und Faserlänge in ein komplexes Muskelmodell

Die Kontraktionsdynamik der Skelettmuskulatur wird durch die Architektur und die Geometrie des kontrahierenden Muskels beeinflusst und ist abhängig von den kontraktilen Eigenschaften der Muskelfaser. In Muskeln mit (uni)pennater Architektur sind die Muskelfasern in einem bestimmten Winkel zur Kraftwirkungsrichtung angeordnet. Dieser Muskelfaserwinkel und die Faserlänge verändern sich bei Kontraktion entscheidend in Abhängigkeit von der Gelenkwinkelstellung (Muskellänge) und der Kraftproduktion (Kontraktionsintensität). Die dynamischen Veränderungen der beiden Muskelarchitekturparameter beeinflussen dadurch maßgebend das Kontraktionsverhalten hinsichtlich der Muskelverkürzung und der Kontraktionsgeschwindigkeit (Muscle Gearing). Muskelverkürzungen wie sie für Alltagsbewegungen erforderlich sind, können nur durch die Vergrößerung des Muskelfaserwinkels realisiert werden. Spezifische Muskelmodelle bieten die Möglichkeit, Einblick in die im Inneren der Muskulatur ablaufenden Prozesse der Kraftentwicklung zu erhalten und das Kontraktionsverhalten des Muskels abzubilden. Trotz der nachweislichen Abhängigkeit des Faserwinkels von den genannten Kontraktionsbedingungen, wird ein dynamischer Faserwinkelverlauf, nicht zuletzt aufgrund fehlender zuverlässiger Parameter, in den meisten Muskelmodellen nicht berücksichtigt, sondern der Faserwinkel als konstant angenommen. Die Dissertation widmet sich daher einerseits der ultraschallbasierten Quantifizierung der Muskelarchitekturparameter Faserlänge und Faserwinkel der drei Muskeln der menschlichen Wadenmuskulatur (m. triceps surae) bei verschiedenen Kontraktionsbedingungen (50°-130° Sohlenwinkel und 0-100 % Kontraktionsintensität), andererseits ihrer Implementierung in ein komplexes Muskelmodell unipennater Architektur. Es wurde ein quadratischer Zusammenhang zwischen der Faserlänge und dem Faserwinkel gefunden, der die Basis für die iterative Berechnung der kontraktilen und serienelastischen Komponenten bildet. Das Modell ermöglicht die Darstellung und mathematische Beschreibung eines dynamischen Muskelfaserverlaufs bei isometrischen und konzentrischen Kontraktionen der Plantarflexoren und liefert den Zusammenhang zwischen Muskelfaserwinkel, -länge und -kraft. Auf Grundlage der Modellrechnungen kann eine allgemeine Gesetzmäßigkeit zur Bestimmung des Faserwinkels in Abhängigkeit von der Faserlänge und der Faserkraft formuliert werden, die für weitere Modellierungen eingesetzt werden kann

Ultrasonography for the prediction of musculoskeletal function

Ultrasound (US) imaging is a well-recognised technique for studying in vivo characteristics of a range of biological tissues due to its portability, low cost and ease of use; with recent technological advances that increased the range of choices regarding acquisition and analysis of ultrasound data available for studying dynamic behaviour of different tissues. This thesis focuses on the development and validation of methods to exploit the capabilities of ultrasound technology to investigate dynamic properties of skeletal muscles in vivo exclusively using ultrasound data. The overarching aim was to evaluate the influence of US data properties and the potential of inference algorithms for prediction of net ankle joint torques. A fully synchronised experimental setup was designed and implemented enabling collection of US, Electromyography (EMG) and dynamometer data from the Gastrocnemius medialis muscle and ankle joint of healthy adult volunteers. Participants performed three increasing complexity muscle movement tasks: passive joint rotations, isometric and isotonic contractions. Two frame rates (32 and 1000 fps) and two data precisions (8 and 16-bits) were obtained enabling analysis of the impact of US data temporal resolution and precision on joint torque predictions. Predictions of net joint torque were calculated using five data inference algorithms ranging from simple regression through to Artificial Neural Networks. Results indicated that accurate predictions of net joint torque can be obtained from the analysis of ultrasound data of one muscle. Significantly improved predictions were observed using the faster frame rate during active tasks, with 16-bit data precision and ANN further improving results in isotonic movements. The speed of muscle activation and complexity of fluctuations of the resulting net joint torques were key factors underpinning the prediction errors recorded. The properties of collected US data in combination with the movement tasks to be assessed should therefore be a key consideration in the development of experimental protocols for in vivo assessment of skeletal muscles

Case series of breast fillers and how things may go wrong: radiology point of view

INTRODUCTION: Breast augmentation is a procedure opted by women to overcome sagging breast due to breastfeeding or aging as well as small breast size. Recent years have shown the emergence of a variety of injectable materials on market as breast fillers. These injectable breast fillers have swiftly gained popularity among women, considering the minimal invasiveness of the procedure, nullifying the need for terrifying surgery. Little do they know that the procedure may pose detrimental complications, while visualization of breast parenchyma infiltrated by these fillers is also deemed substandard; posing diagnostic challenges. We present a case series of three patients with prior history of hyaluronic acid and collagen breast injections. REPORT: The first patient is a 37-year-old lady who presented to casualty with worsening shortness of breath, non-productive cough, central chest pain; associated with fever and chills for 2-weeks duration. The second patient is a 34-year-old lady who complained of cough, fever and haemoptysis; associated with shortness of breath for 1-week duration. CT in these cases revealed non thrombotic wedge-shaped peripheral air-space densities. The third patient is a 37‐year‐old female with right breast pain, swelling and redness for 2- weeks duration. Previous collagen breast injection performed 1 year ago had impeded sonographic visualization of the breast parenchyma. MRI breasts showed multiple non- enhancing round and oval shaped lesions exhibiting fat intensity. CONCLUSION: Radiologists should be familiar with the potential risks and hazards as well as limitations of imaging posed by breast fillers such that MRI is required as problem-solving tool

Characterization of alar ligament on 3.0T MRI: a cross-sectional study in IIUM Medical Centre, Kuantan

INTRODUCTION: The main purpose of the study is to compare the normal anatomy of alar ligament on MRI between male and female. The specific objectives are to assess the prevalence of alar ligament visualized on MRI, to describe its characteristics in term of its course, shape and signal homogeneity and to find differences in alar ligament signal intensity between male and female. This study also aims to determine the association between the heights of respondents with alar ligament signal intensity and dimensions. MATERIALS & METHODS: 50 healthy volunteers were studied on 3.0T MR scanner Siemens Magnetom Spectra using 2-mm proton density, T2 and fat-suppression sequences. Alar ligament is depicted in 3 planes and the visualization and variability of the ligament courses, shapes and signal intensity characteristics were determined. The alar ligament dimensions were also measured. RESULTS: Alar ligament was best depicted in coronal plane, followed by sagittal and axial planes. The orientations were laterally ascending in most of the subjects (60%), predominantly oval in shaped (54%) and 67% showed inhomogenous signal. No significant difference of alar ligament signal intensity between male and female respondents. No significant association was found between the heights of the respondents with alar ligament signal intensity and dimensions. CONCLUSION: Employing a 3.0T MR scanner, the alar ligament is best portrayed on coronal plane, followed by sagittal and axial planes. However, tremendous variability of alar ligament as depicted in our data shows that caution needs to be exercised when evaluating alar ligament, especially during circumstances of injury