Abstract
PurposeIn contrast-enhanced abdominopelvic CT (CE-APCT) for oncologic follow-up, ultrahigh-resolution CT (UHRCT) may improve depiction of fine lesions and low-dose scans are desirable for minimizing the potential adverse effects by ionizing radiation. We compared image quality and radiologists’ acceptance of model-based iterative (MBIR) and deep learning (DLR) reconstructions of low-dose CE-APCT by UHRCT.MethodsUsing our high-resolution (matrix size: 1024) and low-dose (tube voltage 100 kV; noise index: 20–40 HU) protocol, we scanned phantoms to compare the modulation transfer function and noise power spectrum between MBIR and DLR and assessed findings in 36 consecutive patients who underwent CE-APCT (noise index: 35 HU; mean CTDIvol: 4.2 ± 1.6 mGy) by UHRCT. We used paired t-test to compare objective noise and contrast-to-noise ratio (CNR) and Wilcoxon signed-rank test to compare radiologists’ subjective acceptance regarding noise, image texture and appearance, and diagnostic confidence between MBIR and DLR using our routine protocol (matrix size: 512; tube voltage: 120 kV; noise index: 15 HU) for reference.ResultsPhantom studies demonstrated higher spatial resolution and lower low-frequency noise by DLR than MBIR at equal doses. Clinical studies indicated significantly worse objective noise, CNR, and subjective noise by DLR than MBIR, but other subjective characteristics were better (P < 0.001 for all). Compared with the routine protocol, subjective noise was similar or better by DLR, and other subjective characteristics were similar or worse by MBIR.ConclusionImage quality, except regarding noise characteristics, and acceptance by radiologists were better by DLR than MBIR in low-dose CE-APCT by UHRCT.Graphical abstract
Highlights
Since March 2017, our institution has employed an ultrahigh-resolution computed tomography (UHRCT) scanner to improve the in- and through-plane spatial resolution of CT images
Radiologists have been reluctant to adopt this modality because it produces a coarse texture associated with lowfrequency noise, described as an “oil painting” or “plasticlike” appearance, compared to results obtained using hybrid iterative reconstruction (HIR), which is widely applied in clinical settings [6,7,8]
We mainly aimed to (1) determine the HR & LD protocol with model-based iterative (MBIR) and/or deep learning (DLR) to achieve the lowest radiation dose and the similar low-frequency noise to that using the routine protocol in the phantom study and (2) assess validity of this HR & LD protocol based on image quality and radiologists’ acceptance using the routine protocol as reference in the clinical pilot study
Summary
Since March 2017, our institution has employed an ultrahigh-resolution computed tomography (UHRCT) scanner to improve the in- and through-plane spatial resolution of CT images. The greater image noise associated with this method may limit its usefulness in CT that requires lower contrast resolution, such as abdominopelvic CT (APCT) for oncologic follow-up, where UHRCT may improve diagnosis of fine recurrent, disseminated, and metastatic lesions. To overcome these potential limitations, deep learning (DLR) and model-based iterative (MBIR) reconstruction techniques have become clinically available for use in combination with UHRCT [6]. At a routine radiation dose, the quality of abdominal UHRCT images may be better using DLR than either MBIR or HIR [6]. Though, that the quality and acceptance by radiologists of LD CE-APCT images by UHRCT using DLR as well as MBIR has not been assessed yet
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