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| http://dx.doi.org/10.4103/0019-5154.100512 | DOI Listing |
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Recent advances in Light Emitting Diode (LED) technology have enabled a more affordable high frame rate photoacoustic imaging (PA) alternative to traditional laser-based PA systems that are costly and have slow pulse repetition rate. However, a major disadvantage with LEDs is the low energy outputs that do not produce high signal-to-noise ratio (SNR) PA images. There have been recent advancements in integrating deep learning methodologies aimed to address the challenge of improving SNR in LED-PA images, yet comprehensive evaluations across varied datasets and architectures are lacking.
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Background And Objective: Transcranial sonography-based grading of Parkinson's Disease has gained increasing attention in recent years, and it is currently used for assistive differential diagnosis in some specialized centers. To this end, accurate midbrain segmentation is considered an important initial step. However, current practice is manual, time-consuming, and bias-prone due to the subjective nature.
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Article Synopsis
- The article discusses the limited current applications of big data technology, primarily in the medical field, and proposes a new system for analyzing athletes' neural activity based on big data principles.
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