20 skills found
agencyenterprise / NeurotechdevkitNeurotech Development Kit (NDK)
ManuelPalermo / BrainUltrasoundSimulationSimulation of Ultrasound signals on a 3D Brain model using K-Wave toolbox. Plus simple ultrassound focusing algorithm.
jws2f / KranionTranscranial focused ultrasound visualization system
nbottenus / REFoCUSCode for Retrospective Encoding For Conventional Ultrasound Sequences - recover the complete data set from focused (and other) beams
han-liu / SynCT TcMRgFUSOfficial PyTorch Implementation of "Synthetic CT Skull Generation for Transcranial MR Imaging–Guided Focused Ultrasound Interventions with Conditional Adversarial Networks"
nbottenus / Decode CompleteRecovery of the complete data set from focused ultrasound beams
OpenwaterHealth / Openlifu Pythonfocused ultrasound toolbox
PRLab-FAU / Mt Lecture SlidesThese are the lecture slides used at FAU Erlangen-Nuremberg, Germany for the lecture "Medical Engineering". This class gives a complete and comprehensive introduction to the fields of medical imaging systems, as designed for a broad range of applications. The authors of the book first explain the foundations of system theory and image processing, before highlighting several modalities in a dedicated chapter. The initial focus is on modalities that are closely related to traditional camera systems such as endoscopy and microscopy. This is followed by more complex image formation processes: magnetic resonance imaging, X-ray projection imaging, computed tomography, X-ray phase-contrast imaging, nuclear imaging, ultrasound, and optical coherence tomography. Open Access Link to the Text Book: https://link.springer.com/book/10.1007/978-3-319-96520-8#about Link to Video Recordings on YouTube: https://www.youtube.com/watch?v=vvftvjnXzsY&list=PLpOGQvPCDQzsgK1XuhUXO8r9M4WRqhvDf
priyamittal15 / Implementation Of Different Deep Learning Algorithms For Fracture Detection Image ClassificationUsing-Deep-Learning-Techniques-perform-Fracture-Detection-Image-Processing Using Different Image Processing techniques Implementing Fracture Detection on X rays Images on 8000 + images of dataset Description About Project: Bones are the stiff organs that protect vital organs such as the brain, heart, lungs, and other internal organs in the human body. There are 206 bones in the human body, all of which has different shapes, sizes, and structures. The femur bones are the largest, and the auditory ossicles are the smallest. Humans suffer from bone fractures on a regular basis. Bone fractures can happen as a result of an accident or any other situation in which the bones are put under a lot of pressure. Oblique, complex, comminute, spiral, greenstick, and transverse bone fractures are among the many forms that can occur. X-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and other types of medical imaging techniques are available to detect various types of disorders. So we design the architecture of it using Neural Networks different models, compare the accuracy, and get a result of which model works better for our dataset and which model delivers correct results on a specific related dataset with 10 classes. Basically our main motive is to check that which model works better on our dataset so in future reference we all get an idea that which model gives better type of accuracy for a respective dataset . Proposed Method for Project: we decided to make this project because we have seen a lot of times that report that are generated by computer produce error sometimes so we wanted to find out which model gives good accuracy and produce less error so we start to research over image processing nd those libraries which are used in image processing like Keras , Matplot lib , Image Generator , tensor flow and other libraries and used some of them and implement it on different image processing algorithm like as CNN , VGG-16 Model ,ResNet50 Model , InceptionV3 Model . and then find the best model which gives best accuracy for that we generate classification report using predefined libraries in python such as precision , recall ,r2score , mean square error etc by importing Sklearn. Methodology of Project: Phase 1: Requirement analysis: • Study concepts of Basic Python programming. • Study of Tensor flow, keras and Python API interface . • Study of basic algorithms of Image Processing and neural network And deep learning concepts. • Collect the dataset from different resources and describe it into Different classes(5 Fractured + 5 non fractured). Phase 2: Designing and development: The stages of design and development are further segmented. This step starts with data from the Requirement and Analysis phase, which will lead to the model construction phase, where a model will be created and an algorithm will be devised. After the algorithm design phase is completed, the focus will shift to algorithm analysis and implementation in this project. Phase 3: Coding Phase: Before real coding begins, the task is divided into modules/units and assigned to team members once the system design papers are received. Because code is developed during this phase, it is the developers' primary emphasis. The most time-consuming aspect of the project will be this. This project's implementation begins with the development of a program in the relevant programming language and the production of an error-free executable program. Phase 4: Testing Phase: When it comes to the testing phase, we may test our model based on the classification report it generates, which contains a variety of factors such as accuracy, f1score, precision, and recall, and we can also test our model based on its training and testing accuracy. Phase 5: Deployment Phase: One of our goals is to bring all of the previous steps together and put them into practice. Another goal is to deploy our model into a python-based interface application after comparing the classification reports and determining which model is best for our dataset.
SamC873 / FUSF Hydrophone ScannerThe Focused Ultrasound Foundation has developed a low-cost, validated, open-source hydrophone scanner for the spatial characterization of ultrasound transducers. A validation paper has been submitted to Ultrasound in Biology and Medicine and will be referenced here upon publication. This GitHub repository provides the programs, parts list, 3D files, and instructions to replicate this system.
bfinl / TFUS MVEPBCI AnalysisThe analysis scripts used to analyze EEG mVEP Data in the study: Kosnoff J, Yu K, Liu C, He B. Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention. Nat Commun 2024;15:4382. https://doi.org/10.1038/s41467-024-48576-8.
wgrissom / ZebrographyCode for implementation and validation of optical focused ultrasound beam mapping using CW background oriented schlieren imaging (aka zebrography)
raiyanjaz / B Mode Beamforming In Ultrasound ImagingThis C++ project focuses on B-Mode ultrasound imaging, featuring a program to generate ultrasound images from data. It covers data management, beamforming, and image generation, demonstrating the role of software in healthcare imaging.
USgHIFU / USgHIFU QtThe software used in the phased array B-mode ultrasound guided high-intensity focused ultrasound system
OpenwaterHealth / SlicerOpenLIFUA 3D Slicer extension for Openwater’s OpenLIFU (Low Intensity Focused Ultrasound) research platform, providing an advanced interface to features found in the OpenLIFU app.
dpksonker / Focused Ultrasound FUS K WaveNo description available
mohammadrezashahsavari / Medical Image Translation GANsA collection of deep learning models (CycleGAN, Pix2Pix, UNet) for medical image-to-image translation, with a focus on transforming 2D and 3D Ultrasound images to MRI.
TVSNEXT / Transforming Healthcare Through IoTLook around and you will find people with smart devices that track their every move, calculate their intake and gives them trends on this data. Primitively, Caregivers and hospitals were using telemetry to remotely gather data for improving patient care. The primary aim of preventive healthcare was to deliver personalized care, improve patient care while reducing costs. However, Internet of Medical Things (IoMT) is driving the future of healthcare for better outcomes, improving efficiency and making healthcare more affordable as caretakers are increasingly resorting to more self- care due to increased awareness. To get there, healthcare providers must make use of technology in a more systematic way. The Scope of IoT is getting bigger and better in: Preventive healthcare: by use of wearables. Patient tracking: in monitoring patient movement and health analysis. Geriatric care: in tracking Senior citizens which is a large market for IoT, medical devices. Real-time location: in tracking medical devices, people and asset movement. Gartner believes that 30% of smart wearables will be unobtrusive to the eye by 2017. it is predicted that the revenue from smart wearable devices will generate $22.9 billion by 2020. Experts from P&S Market research expect that the healthcare Internet of Things industry will grow at a compound annual growth rate (CAGR) of 37.6 percent between 2015 and 2020. The rising need for advanced healthcare combined with the requirements of Affordable Care Act (ACA), technology in the industry is expected to grow through 2020 Wearables for preventive health analysis Imagine a wearable used for preventive health analysis. The term wearable in health parlance should not be restricted to just fitness tracking devices worn in the wrist that are used to monitor personal health. By and far, this term should go beyond tracking of physical activities; it could be used as a communication device or it could even be a device that interacts with other devices like an Apple watch, track patients’ body conditions, sleep patterns or any other critical information which may require immediate care. It could be a device in the body, on the body or near the body like a medical app that helps track personal health; Some of the industry medical apps that are already disrupting the healthcare market are Philips’ Medication Dispensing Service Airfinder Boiron Medicine Finder App Future Path Medical’s Urosense Digital Hospitals making headway Healthcare is increasingly leveraging modern technology and digital hospitals are making headway such as the Humber River Hospital in Toronto Canada and the Medical Center at Mission Bay San Francisco. Innovative approaches towards engaging robots in the radiology and other departments are also disrupting the way healthcare is delivered. Deakin University Australia, in partnership with Telstra Australia, has developed haptics-enabled robots that can perform ultrasound diagnostics remotely. This means the patient need not be in the same place as the sonographer conducting the ultrasound. IoMT for improved healthcare There were 165,000 mobile healthcare apps as of November 2015 and the mobile app marketplace is expected to grow 15 times faster, according to a survey. Another survey shows users expect digital healthcare services to be able to communicate with doctors via their smartphones, monitor health and collaborate with care givers with ease. IoMT is slowly and steadily reducing human intervention and dependency to provide early diagnosis and contributing to improved healthcare in accordance with the Patient Protection and Affordable Care Act which focus on certified EHR (Electronic Health Record) to enhance efficiency and quality of patient care. Conclusion: With the cost of Hardware coming down, software enabling devices to talk to multiple devices across platforms is going to redefine the way people take care of their health. The biggest change is going to be in Preventive Healthcare, making it the biggest gamechanger.
yuchanWang / Focused Ultrasound LongTimeHeating Of RingTransTemperature Controlled Hyperthermia with Non-invasive Temperature Monitoring through Speed of Sound Imaging (Matlab code)
rjh-mopjones / HIFU GuiUsing MATLAB to Develop a Graphical User Interface to Visualise Acoustic Pressure Fields in View of Pre-Processing a High-Intensity Focused Ultrasound Treatment Plan.
