Image quality improvement with deep learning‐based reconstruction on abdominal ultrahigh‐resolution CT: A phantom study

Abstract

In an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol.Methods: For the normal-sized abdominal models, a Catphan 600 was scanned by U-HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model-based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms.Results: Deep learning-based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the

A Review of Deep Learning CT Reconstruction: Concepts, Limitations, and Promise in Clinical Practice

“…DLR similarly exhibits high-contrast spatial resolution that is comparable to MBR methods yielding similar results at the 50% and 10% modulation transfer function (MTF) points [44]. However, as previously reported, spatial resolution can be influenced by both the contrast level of target and the dose levels.…”

“…In the assessment of absolute image noise, DLR achieves the same or higher levels of noise reduction compared to MBIR, with the relative noise increase as dose is lowered muted relative to FBP [48,49]. Performance evaluation of one commercial product shows DL outperforming all other reconstruction methods at low doses, while it is outperformed only by MBR at higher doses [44,49]. The noise reduction capabilities of DLIR enables exploring new spatial resolution limits such as deploying detectors with smaller pixels and utilizing shaper kernels with larger image matrices without suffering a noise penalty [44].…”

“…Measuring the mean frequency of DLR images using GE Healthcare's TrueFidelity (i.e., DLIR reconstruction), a marginal shift was observed across the range of diagnostic dose levels, and only at relatively low-dose levels was a meaningful shift measured for DL relative to FBP [48,50]. The DLR solution from Canon Medical System, termed Advanced intelligent Clear-IQ Engine (AiCE), showed similar NPS behavior to MBIR [44], but recent changes to the AiCE method have made the texture more FBP like [49]. There is evidence that the strength (or weight) of DLR processing impacts noise texture, with a shift to lower frequencies at high DLR strengths for both on-scanner algorithms currently available [52], albeit that work of Hasegawa et al that demonstrated this did so at phantom sizes and dose levels not representative of clinical reality.…”

“…Performance evaluation of one commercial product shows DL outperforming all other reconstruction methods at low doses, while it is outperformed only by MBR at higher doses [44,49]. The noise reduction capabilities of DLIR enables exploring new spatial resolution limits such as deploying detectors with smaller pixels and utilizing shaper kernels with larger image matrices without suffering a noise penalty [44].…”

“…If relatively high-dose FBP images are input to the network, the network output will have noise texture which more closely resembles FBP. On the other hand, if IR is used as the ''clean'' training data, the noise texture in the images may more closely resemble that of IR [43,44].…”