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.
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.