Multimodal Image Fusion Software Market Regional Analysis to 2028According to our latest market study on “Multimodal Image Fusion Software Market Forecast to 2028 – COVID-19 Impact and Global Analysis – by Type and Application,” the market was valued at US$ 298.29 million in 2020 and it is projected to reach US$ 552.30 million by 2028; it is expected to grow at a CAGR of 8.1% from 2021 to 2028.
Multimodal image fusion software market is receiving high momentum across the world. Increasing Government and Private Spending on Healthcare Infrastructure. Healthcare spending by government and private organizations has been increasing over the years. These high spending across the world is not only enhancing the healthcare infrastructure but also influencing the adoption of various technologically advanced solutions for improving their treatment plans and diagnostic procedures. At present, the US ranks first among developing countries in terms of healthcare spending.
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According to the Organization for Economic Co-operation and Development (OECD) data published in 2019, the US average healthcare spending was a whopping US$11,072 per capita. Similarly, in 2019, Switzerland had the second-highest healthcare spending, with nearly US$8,000 per capita expenditures, followed by Germany and Norway with per capita spending of about $6,600 each. The other countries with high healthcare expenditure include Austria, Sweden, Netherlands, Canada, France, Ireland, Australia, Japan, Iceland, the UK, and Finland, amongst others
Medical image fusion is the method of combining different images from various imaging modalities to create a merged image with a vast amount of detail that can be used in clinical settings. Medical diagnosis relies heavily on imaging technology. Clinical diagnosis needs a significant amount of data. However, single-modal medical images provide insufficient information to meet the needs of clinical diagnosis; hence, medical image fusion research has become a hot topic. Single-mode fusion and multimodal fusion are two types of medical image fusion. Many researchers are studying multimodal fusion because single-modal fusion has limitations in terms of the information it can provide. Computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) are a few of the imaging techniques that have provided clinicians with knowledge about the structural features, soft tissue, and other aspects of the human body. Thus, different imaging methods maintain different characteristics, and different sensors acquire different imaging data for the same component. The aim of the fusion is to improve contrast, fusion efficiency, and overall perception.
Impact of COVID-19 Pandemic on Multimodal Image Fusion Software Market
The COVID-19 outbreak has minimum or no impact on the global multimodal image fusion software market. The healthcare sector in many North American countries has remained unaffected during the COVID-19 pandemic. With the rising COVID-19 confirmed cases across the US, the UK, Germany, and India, the high prevalence of diseases other than other COVID-19 propels the demand for all healthcare equipment and solutions. For instance, according to Alzheimer Association as of 2020, there were ~5.8 million Americans suffering from Alzheimer’s disease. Thus, the COVID-19 pandemic has a negligible impact on the demand for multimodal image fusion software across the world.
Players operating in the multimodal image fusion software market are mainly focused on the development of advanced and efficient products.
• In 2021, Intrasense SA released a new software version Myrian 2.9. The new version of the medical imaging software had a more focus on new clinical features.
• In 2020, aycan Medical Systems, LLC’s aycan workstation with imaging biometrics plug-in was featured in a study published in the American Journal of neuroradiology. The study compared the predictive performance of relative CBV standardization against relative CBV normalization for quantifying recurrent tumor burden in high-grade gliomas relative to posttreatment radiation effects.
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