Implementation of AI image reconstruction in CT-how is it validated and what dose reductions can be achieved

Abstract

CT reconstruction has undergone a substantial change over the last decade with the introduction of iterative reconstruction (IR) and now with deep learning reconstruction (DLR). In this review, DLR will be compared to IR and filtered back-projection (FBP) reconstructions. Comparisons will be made using image quality metrics such as noise power spectrum, contrast-dependent task-based transfer function, and non-prewhitening filter detectability index (d_NPW'). Discussion on how DLR has impacted CT image quality, low-contrast detectability, and diagnostic confidence will be provided. DLR has shown the ability to improve in areas that IR is lacking, namely: noise magnitude reduction does not alter noise texture to the degree that IR did, and the noise texture found in DLR is more aligned with noise texture of an FBP reconstruction. Additionally, the dose reduction potential for DLR is shown to be greater than IR. For IR, the consensus was dose reduction should be limited to no more than 15-30% to preserve low-contrast detectability. For DLR, initial phantom and patient observer studies have shown acceptable dose reduction between 44 and 83% for both low- and high-contrast object detectability tasks. Ultimately, DLR is able to be used for CT reconstruction in place of IR, making it an easy "turnkey" upgrade for CT reconstruction. DLR for CT is actively being improved as more vendor options are being developed and current DLR options are being enhanced with second generation algorithms being released. DLR is still in its developmental early stages, but is shown to be a promising future for CT reconstruction.

Figure 1.

Noise power spectrum plots were derived using an ACR CT phantom (scan technique factors were held constant for all vendor-specific image acquisitions) and demonatrate noise variance reduction along the ordinate axis as a function of noise frequency (i.e. noise texture) for FBP, hybrid-IR, and DLR images along the abcissa. (a) Canon’s AiCE is shown at three strengths of DLR implementation along side a FBP and AIDR3D (i.e. hybrid-IR); (b) plot is simplified to only show AiCE at three strengths. (c) GE’s TF is shown at three strengths of DLR implementation alongside a FBP and 40% implementation of ASiR-V (i.e. hybrid-IR); (d) plot is simplified to only show TF at three strengths. ACR, American College of Radiology; DLR, deep learning reconstruction; FBP, filtered back-projection; IR, iterative reconstruction; TF, TrueFidelity.

Figure 2.

TTF plots were derived using an ACR CT phantom (scan technique factors were held constant for all vendor-specific image acquisitions; TTF was calculated using the high-contrast sensitometry insert, i.e. ~900 HU) and demonatrate image contrast along the ordinate axis as a function of object size (i.e. spatial frequency) along the abscissa for FBP, hybrid-IR, and DLR images. (a) Canon’s AiCE is shown at three strengths of DLR implementation along side a FBP and AIDR3D (i.e. hybrid-IR); (b) GE’s TF is shown at three strengths of DLR implementation alongside a FBP and 40% implementation of ASiR-V (i.e. hybrid-IR). Horizontal dashed lines are provided as common points of reference at the 50% and 10% TTF. ACR, American College of Radiology; DLR, deep learning reconstruction; FBP, filtered back-projection; IR, iterative reconstruction; TF, TrueFidelity; TTF, task-based modulation transfer function.

Figure 3.

CFR was calculated at decrementing radiation output levels (i.e., mAs) for Canon’s AiCE and GE’s TrueFidelity. The fitting function for AiCE took the form ${\displaystyle a\left(1-e^{-bx}\right)+c}$ with coefficient values: a = 0.6, b = 0.03, and c = 0.41. The SSR was 0.001. The fitting function for TrueFidelity took the form ${\displaystyle a\left(x\right)+b}$ with coefficient values: a = −0.0003 and b = 1.11. The SSR was 0.003. CFR, central frequency ratio; SSR, sum of squared residual.

Figure 4.

NMR was calculated at decrementing radiation output levels (i.e., mAs) for Canon’s AiCE and GE’s TrueFidelity. The fitting function for both AiCE and TrueFidelity took the form $\frac{1}{\left(\frac{1}{a}+bx\right)}+c$ . The coefficient values for AiCE were: a = 2.31, b = 0.01, and c = 0.66 with a SSR of 0.003. The coefficient values forTrueFidelity were: a = 14.97, b = 0.01, and c = 1.12 with a SSR of 0.82. NMR, noise magnitude ratio; SSR, sum of squared residual.