G2

Jeedhara Rose D.

Customer Service Representative

Enterprise (> 1000 emp.)

10/27/2026

"Cleaner Images and Faster Scans Without Workflow Disruption"

5/5

What do you like best about Deep Resolve?

What I like most about Deep Resolve is the way it improves the trade-off between scan time and image quality. Since the reconstruction is trained on real anatomical data, we’re able to shorten acquisitions without the usual drop in clarity. The images come out noticeably cleaner, with less noise and crisper detail, even when we use faster settings. This has a real impact for patients who struggle to stay still—such as older, pediatric, or claustrophobic patients—because shorter scans mean fewer motion artifacts and fewer repeat sequences. It also helps with scheduling, since we can fit in more exams without sacrificing diagnostic confidence. The reconstruction runs automatically within our normal workflow, so it hasn’t added extra steps for our technologists. Review collected by and hosted on G2.com.

What do you dislike about Deep Resolve?

My main reservation about Deep Resolve is that, like many deep-learning reconstruction tools, it isn’t always clear exactly how the final image was generated. That lack of transparency can make some radiologists hesitant to trust very aggressive settings. In addition, the images can sometimes have a slightly smoothed or “processed” appearance that takes time to get used to, particularly for readers who are accustomed to conventional reconstructions. To get the best results, you often need to tune protocols and then validate them across different sequences and body regions, which adds extra work during implementation. Reconstruction times can also be longer on some systems. Finally, access to certain features depends on the scanner platform, software version, and licensing, which can increase the overall cost. Review collected by and hosted on G2.com.

What problems is Deep Resolve solving and how is that benefiting you?

Deep Resolve helps address the long-standing trade-off in MRI between scan time, image quality, and spatial resolution. In the past, shortening an exam usually meant accepting more noise or reduced resolution, while aiming for higher quality meant keeping patients in the scanner longer, which increases the risk of motion artifacts and can lead to repeat sequences. With deep-learning reconstruction that denoises images and helps recover detail from accelerated acquisitions, Deep Resolve allows us to reduce scan times while still maintaining the clarity we need for confident diagnosis. In our experience, this has translated into better tolerance for patients who struggle to stay still, fewer repeated scans, and improved scheduling efficiency because we can complete more exams in the same amount of time. It has also given us more flexibility to push toward higher resolution on sequences where detail matters most, without a significant time penalty. Just as importantly, it fits into our existing workflow, so it hasn’t added extra steps for our technologists. Review collected by and hosted on G2.com.