The current study evaluated the improvements in image quality, diagnostic acceptance, and lesion conspicuity of using thinner slice iodine maps combined with DLIR algorithm. The thin slice DLIR images provided stable IC measurement compared to the conventional Asir-V image reconstruction algorithm and showed lower CV values than that of thin slice AV-50 to allow accurate and consistent iodine quantification. The thin slice DLIR significantly reduced the image noise compared to thin slice AV-50, while provided higher spatial resolution with thinner slice thickness compared to thick slice AV-50. The subjective evaluation showed higher diagnostic acceptance and higher lesion conspicuity with thin slice DLIR images compared to thick slice AV-50 images, indicating the potential of thin slice iodine maps with DLIR for clinical diagnostic purpose.
The previous phantom studies have demonstrated that DECT scanners using a fast-kilovoltage-switching mode with DLIR can provide possible small improvement in iodine quantification accuracy compared with the Asir-V [5, 8]. The clinical study further confirmed the potential of DLIR in reducing image noise as well as variability of IC values compared to Asir-V [6,7,8]. However, the studies only investigated the IC accuracy and image quality at one slice thickness of 5-mm [5] or thin slice [6,7,8]. As the current clinical standard of obtaining iodine maps is still iterative reconstruction with relative thick slice thickness, we further investigated the influence of slice thickness on the iodine quantification. Our results suggested that the DLIR allows thinner slice thickness with consistent IC values with lower variability, indicating the generalizability of quantitative thresholds established by different slice thickness and reconstruction algorithms in fast-kilovoltage-switching DECT scanners. It is important to evaluate the quantification consistency of IC values, because the current application of iodine maps is mainly based on established iodine concentration thresholds [2,3,4]. Our study suggested that the DLIR can be safely accepted as a new reconstruction algorithm for quantitative analysis of abdomen.
The thicker slice images have low image noise, but usually presents with lower spatial resolution and suffer from partial volume effects. This led to difficulties in displaying small and low-density objects. In contrast, the improved ERS and spatial resolution can provide higher sharpness and better contrast to allow better detectability of lesions. However, the thinner slice images have potential for improving the conspicuity for these lesions, but increase the image noise [9]. In our study, the thin slice AV-50 showed higher ERS values compared to thick slice AV-50, but suffered from the increased image noise, which resulted in suboptimal clinical acceptance evaluated by five radiologists. As the thinner slice thickness with AV-50 cannot provide satisfied balanced image quality for lesion detection, the new DLIR algorithm was introduced. To overcome the dilemma of spatial resolution and image noise, the DLIR algorithm was used and presented potential for improving image quality in VMIs [10,11,12,13,14,15,16,17,18]. The DLIR algorithm is developed by using deep convolutional neural networks with a ground-truth training data of filtered back-projection images acquired with high-dose scans, to generate high quality images from low-dose scans. The reduced image noise is believed to yield lower variability in the measured IC values [8]. The thin slice DLIR images presented similar ERS values compared to thin slice AV-50, while maintained relatively low image noise compared to thick slice AV-50, which gained higher clinical acceptance in subjective evaluation. Therefore, we believed that the DLIR may facilitate a thinner slice thickness as a new state-of-art standard for routine reconstruction of iodine maps in abdominal DECT, to replace the original thicker slice iodine maps using Asir-V.
The lesion conspicuity has been seldomly investigated in the iodine maps [8], while the studies using VMIs have demonstrated the potential of DLIR for improvement of lesion conspicuity [6, 7]. Our study suggested a possible improvement in lesion conspicuity in iodine maps by using DLIR, indicating a potential role of iodine maps for clinical diagnosis purpose in the future, in addition to the current iodine quantification. The thick slice AV-50 images, although with lower noise, were not optimal for diagnosis purpose due to thick slice thickness and lower sharpness. Cao et al. [9] have suggested in their study of using conventional CT images, the DLIR allows the use of thinner slice images by significantly suppressing image noise while improving image spatial resolution as well as overall image quality. Our study extended that statement into the iodine maps and recommended the thinner slice images as a new standard for iodine maps in abdominal DECT. The thin slice AV-50 images provided improved sharpness but suffered from high image noise, which potentially hindered the diagnosis. Xu et al. [13, 16] suggested that the DLIR significantly reduces image noise than Asir-V in low-keV VMIs, and were most evident and consistent in thin slice images. Sato et al. [7] and Noda et al. [11] showed representative cases for better lesion conspicuity in iodine maps using DLIR. Our study suggested small but significant improvements in lesion conspicuity using DLIR-M than AV-50, but the DLIR-H did not show significant advantages than AV-50. It is not surprising that the DLIR-M gained a higher rating in lesion conspicuity than DLIR-H in our study, since the DLIR-M images were preferred by the readers in subjective image quality evaluation and gained higher diagnostic acceptance. However, strength level selection of DLIR may depends on the clinical tasks, as previous studies recommended different strength level of DLIR for solid or cystic lesions [7, 18] and pancreatic cancers [11]. Our study has confirmed the ability of DLIR for improving image quality as well as lesion conspicuity in iodine maps by using objective and subjective evaluations. Nevertheless, comprehensive evaluation of abdominal diseases and modification of reconstruction parameters are needed before the iodine maps can be accepted as a new extra reference for diagnosis purpose.
Our study has following limitations to address. First, the current study was conducted with a relatively small sample size at one institution. Although post hoc power calculation showed high efficiency, our conclusions require further validation in other centers. Second, our study only employed only one fast-kilovoltage-switching DECT scanner since the DLIR algorithm is vendor-specific, and we only compared the vendor-specific Asir-V algorithm with DLIR-M and DLIR-H. The inter-vendor and inter-scanner differences were not assessed [5, 24,25,26,27]. However, we chose thick slice AV-50 iodine maps as the benchmark, to present the improvement accomplished by DLIR compared to the current clinical standard. The DLIR with low strength was not included because it is not hopeful to provide available image quality [17, 18]. Third, our study only measured the IC values of normal structures. The influence of DLIR on iodine quantification and diagnosis must be ascertained with respect to different diseases. Also, the potential influence of DLIR on advanced quantitative analysis was not evaluated [5, 25]. Fourth, the qualitative image evaluation in our study was conducted by five radiologists with 1- to 6-year-experience in radiology. The experience in radiologists may introduce bias in the rating. The results of our study should be validated by more studies with more radiologists with different levels of experience. Fifth, the diagnostic acceptance of using iodine maps was not compared with that of the VMIs, as the best kiloelectron voltage level for VMIs using DLIR has not been determined yet. Further comparisons between VMIs and iodine maps are necessary to tell whether iodine maps have potential advantages for diagnosis purpose. Sixth, the potential influence of factors like patient motion, contrast agent dosage, and scanner settings on image quality were not assessed in our study. The future study may focus on these factors to deepen the DLIR application in clinical practice. Finally, the DLIR algorithm is a black box. We need further investigation to gain acceptance in clinical practice. Further investigations on the its robustness to artifacts and noise [28,29,30,31], as well as its protentional influence on the later images processing steps [32,33,34]. The future investigations are encouraged to explore the impact of DLIR on specific types of lesions or comparing its performance across different patient populations. Moreover, the cost-effectiveness of implementing DLIR in clinical practice would also be an interesting topic.
To summarize, the thinner slice thickness iodine maps with DLIR in abdominal DECT can keep the iodine concentration measurement values unchanged with lower variability compared with the standard reconstructions to allow consistent quantitative iodine analysis using established threshold values, and can provide improved image quality with reduced image noise, more natural image texture, and better spatial resolution. Compared to the standard thicker slice reconstructions, the thinner slice thickness iodine images with DLIR have the potential can potentially improve the accuracy of lesion detection and characterization in abdominal DECT. Future studies are encouraged to determine whether DLIR has clinical impact on iodine quantification and diagnosis confidence for specific clinical tasks.
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