Magnetic Resonance
Compressed SENSE
Up to 50% faster MRI scans with virtually equal image quality1
Philips Compressed SENSE is a cutting-edge MRI technology that significantly speeds up scans while delivering virtually equal image quality. By intelligently compressing data, it accelerates your existing MR scans by up to 50%, frees up time to improve your patient experience and can provide up to 60% higher resolution to enhance diagnostic confidence.1 Philips Compressed SENSE is suitable for all anatomies and can be used for all anatomical contrasts, in both 2D and 3D.
Demonstrated results of Compressed SENSE
How does Compressed SENSE work?
Compressed sensing is a signal processing technique built on the fact that signals contain redundant information. In MRI this technique is used to reconstruct a full image from severely under-sampled data (in k-space) while maintaining virtually equivalent image quality.
Increase productivity
If you are looking for ways to increase the utilization of your MR equipment to meet the rising demand for MRI services, Compressed SENSE can be the answer. A full MRI exam performed with Compressed SENSE can save minutes compared to a conventional MRI. This could free up one or two extra exam slots in your daily schedule, which can result in much higher productivity and shorter waitlists without adding more operator hours.
Reduce scheduling hassles
With Compressed SENSE you can create a buffer to easily handle emergency cases or urgent patients that are referred on the same day. This extra capacity can help you serve patients and referring physicians faster and make daily workflow go smoother for your staff.
Increase speed and image quality, driven by AI
Philips SmartSpeed is an award-winning2 acceleration technique, combining our proven Compressed SENSE technology with AI-reconstruction. Together, these AI radiology technologies speed up scans up to 3 times faster6 with no loss in image quality.
1/3
Frequently asked questions
Footnotes
- Compared to Philips scans without Compressed SENSE.
- Adaptive-C-SENSE-Net technology is the winner of the FastMRI Challenge hosted by Facebook AI Research and NYU Langone Health.
- Donoho D. Compressed sensing. IEEE Trans Inf Theory 2006; 52: 1289–1306.
- Candès EJ, Romberg JK, Tao T. Stable signal recovery from incomplete and inaccurate measurements. Commun Pur Appl Math 2006; 59:1207–1223.
- Candès, Emmanuel J.; Romberg, Justin K.; Tao, Terence (2006). “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Fourier Information”. IEEE Trans. Inf. Theory. 52 (8): 489–509.
- Compared to Philips SENSE imaging.
Disclaimer
Results are specific to the institution where they were obtained and may not reflect the results achievable at other institutions. Results in other cases may vary.