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Magnetic resonance imaging (MRI) scans that were produced quickly and with coarse sampling can be reconstructed into high-quality images with diagnostic value comparable to that of conventional MRI scans using artificial intelligence (AI).
Reconstructing MRI scans with AI promises to increase MRI access to more patients and shorten wait times for appointments because it is four times quicker than traditional scans.
The Global AI-equipped MRI machine market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
A new scanner from Philips that combines spectral CT with real-time, image-guided therapy hardware was also unveiled, along with a new portfolio of smart MRI devices with artificial intelligence programmes to aid in speeding up image acquisition and diagnosis.
Additionally, the business revealed software for radiology control room consoles that aims to direct technicians through a scan and automate some of their workflow processes, such as exam planning and image post-processing.
A completely sealed, helium-free scanner is a feature of the FDA-510(k) cleared MR 5300 1.5T system, which makes it appropriate for use in both well-equipped hospital radiology departments and outpatient clinics. To keep the MRI’s magnets cold in the past, imagers had to be constantly refilled with liquid helium.
The more robust MR 7700 3.0T device, on the other hand, is built to handle sophisticated neuroscience and research applications. It can conduct multinuclei clinical exams, such as spectroscopy tests to look at the internal metabolism of tumour cells.
On display at the Radiological Society of North America’s annual conference were both systems. (RSNA). This year’s RSNA will be devoted to the introduction of scalable, high-performance MR systems to the imaging industry. These systems will include intelligent software that will automate duties and lessen the workload of radiology departments and staff.
Philips’ new SmartSpeed programme applies deep-learning algorithms directly to the MR signal output of the scanner to reduce the amount of time required to finish a scan and boost patient throughput.
