The Value of a Deep Learning Image Reconstruction Algorithm on Low Dose Triphasic-enhanced Renal CT

doi:10.21203/rs.3.rs-4682967/v1

Download PDF

Research Article

The Value of a Deep Learning Image Reconstruction Algorithm on Low Dose Triphasic-enhanced Renal CT

https://doi.org/10.21203/rs.3.rs-4682967/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: While deep learning image reconstruction(DLIR) has been applied successfully in thoracic, abdominal, and vascular examinations, its application in low-dose renal CT protocols has not been previously explored.

Purpose: To explore the value of DLIR in reducing radiation dose and improving image quality in contrast-enhanced renal CT compared with the adaptive statistical iterative reconstruction Veo(ASIR-V).

Material and Methods: Methods: 129 renal disease patients underwent unenhanced and triphasic-enhanced CT scans, utilizing a standard 120 kVp dose for parenchymal-phase scans and a lower 100 kVp dose for corticomedullary-phase scans. Images in both phases were reconstructed with high-strength DLIR(DLIR-H), medium-strength DLIR(DLIR-M) and ASIR-V level 50%(ASIR-V-50%) for comparison. CT values and standard deviations were measured and compared for various tissues in both phases, and two radiologists assessed image quality using a 5-point Likert scale in seven aspects.

Results: A total of 118 patients were included, with corticomedullary-phase radiation dose reduced by over 15% compared to parenchymal-phase (CTDIvol: 6.57±2.13mGy vs. 7.75±2.63mGy). DLIR-M and DLIR-H exhibited significantly lower image noise in both phases compared to ASIR-V-50% (p<0.001). Corticomedullary-phase DLIR-M and DLIR-H images reduced subcutaneous-adipose tissue noise by 15% and 40% compared to parenchymal-phase ASIR-V-50%. Subjectively, DLIR-H (4.16±0.62) and DLIR-M (3.76±0.68) using 100 kVp outperformed ASIR-V-50% (3.42±0.52) at 120 kVp (p<0.001).

Conclusion: DLIR-H and DLIR-M significantly reduce image noise and generate images with better image quality and diagnostic confidence with a 15% dose reduction than ASIR-V-50%.

Clinical Trial Number

2023-278, First Hospital of Jilin University, Changchun, China.

deep learning image reconstruction

algorithm

low dose

kidney

Kidney cancer is the second most common cancer in the urinary system, accounting for 3% of all cancers. As a noninvasive and valuable imaging examination method to diagnose malignant diseases in urinary system, contrast enhanced kidney CT, especially triphasic-enhanced CT can detect and confirm lesions early and improve the survival rates of the patients.

The annual increase in the number of kidney CT examinations in China has raised serious concerns about the increased risk of radiation-induced cancer, especially in multi-phase enhanced CT scans(1). Much effort has been made to reduce the radiation dose and contrast medium dose of triphasic-enhanced CT of the kidney(2). In recent years, iterative reconstruction (IR) algorithms have been developed and used in CT applications to allow for high image quality at lower radiation dose(3). However, unlike conventional filtered back projection (FBP), which uses linear mathematical operations, IR algorithms use nonlinear operations that affect noise spatial correlations within an image, leading to variations in the noise texture and spatial resolution depending on the contrast of the object of interest and its background(4).

Recently, the deep learning image reconstruction (DLIR) has been developed, which has more than one million parameters that offer the possibility for a considerable reduction in image noise while improving spatial resolution. Notably, tests on phantoms(5–8) and clinical studies have shown that DLIR algorithm is useful in thoracic(9, 10), abdominal(11–16), and vascular(17–20) examinations. However, to the best of our knowledge, no study has evaluated the application of DLIR algorithm in renal low-dose CT protocols. Therefore, the purpose of this study was to investigate the effectiveness of deep learning image reconstruction (DLIR) in reducing radiation dose and improving image quality in triphasic-enhanced renal CT compared to the widely used adaptive statistical iterative reconstruction Veo (ASIR-V).

General Methods

This study was a prospective study approved by the ethics committee of our hospital, and written informed consent was obtained from all patients. A total of 129 consecutive patients scheduled for triphasic-enhanced renal CT were prospectively enrolled in this study from May 2021 to December 2021 (Fig. 1)., The inclusion and exclusion criteria were listed as follows. Inclusion criteria: (1) ultrasound confirmed renal mass and (2) clinical indications requiring triphasic-enhanced renal CT. Exclusion criteria: (1) impaired renal function (serum creatinine level > 176.8 µmol/L); (2) contraindication for iodinated contrast medium. (3) BMI greater than or equal to 35 kg/m².

CT Acquisition and Image Reconstruction

All patients underwent the unenhanced, corticomedullary, parenchymal(nephrographic) and excretory phase CT scans according to our hospital standard protocol on a 256-row multidetector CT system (Revolution CT, GE, Waukesha, WI USA). For the corticomedullary phase scan, we used a reduced radiation dose of 100 kVp and automatically adjusted between 150 and 550 mA, while maintaining the standard protocol of 120 kVp and automatically adjusted between 150 and 500 mAfor the unenhanced phase, parenchymal phase (PP), and excretory phase. All scans were performed rotation using speed of 0.5 s, collimation of 128 × 0.625 mm, pitch of 0.992:1, noise index (NI) of 9 HU. The volume and injection rate of contrast agent (Iodixanol, concentration of 320 mgI/ml) were 0.9 ml/kg and 3 ml/s, respectively. The contrast-enhanced CT scans began after 35s for the corticomedullary phase, 50 s for the parenchymal phase (nephrographic phase), and 240 s for the excretory phase.

Images of the corticomedullary phase and parenchymal phase were reconstructed with both ASIR-V level 50% (ASIR-V-50%) and medium-strength DLIR (DLIR-M) and high-strength (DLIR-H) with a thin layer thickness of 1.25mm. Radiation exposure levels were recorded.

Objective Assessment

$$\:CNRtissure1\_tissure2=\frac{{|CT}_{ROI1}-{CT}_{ROI2}|}{\sqrt{{{SD}_{ROI1}}^{2}+{{SD}_{ROI2}}^{2}}}$$

Subjective Assessment

The renal CP images reconstructed using DLIR-M and DLIR-H algorithms as well as the PP images reconstructed using ASIR-V50% algorithms, were assessed in a random order. Two observers each with more than ten years of experience reviewing CT images evaluated the qualitative image quality including 7 aspects separately on a 5-point scoring system (Table 1). The overall image quality of CT images with scores of 3 or higher was considered acceptable for diagnosis. Subjective kidney scoring was performed on axial CT images. The display window level (10-100HU) and width (200-400HU) was adjusted by the observers. All patient and scanning-related information were hidden from observers.

Statistical Analysis

All the data were represented as mean ± SD. Continuous variables following the normal distribution were analyzed by using the repeated measures analysis of variance (ANOVA) with Bonferroni correction. The ordinal scales or variables that failed to follow normal distribution were analyzed by using Friedman test. Weighted Kappa test was used to test the consistency between the two observers (k value greater than 0.75 was indicated as excellent agreement, 0.40–0.75 as good agreement, and < 0.40 as poor agreement). A P value < 0.05 was considered statistically significant.

All statistical analyses were performed using SPSS 22 software (IBM Corp., Armonk, NY, USA) or Medcalc version 11.4.1 (Medcalc Software, Mariakerke, Belgium). All plots were generated using Python 3.6 .

Basic characteristics of patients and radiation dose

A total of 118 patients (80 men, 38 women, 54.12 ± 14.79 years, age range from 23 to 92 years) with a mean weight of 67.04 ± 14.67 kg (range from 44 to130 kg) and body mass index (BMI) of 26 ± 3.45 kg/m² were included in this study. CT scans diagnosed the following diseases among the 118 patients: renal cyst (n= 55), renal cell carcinoma (n= 74), renal urothelial carcinoma (n= 6), renal tuberculosis (n= 3), and renal angiomyolipoma (n= 15). The mean volume of intravenous contrast media based on body weight was 62.45 ± 10.70 ml. Compared to the PP with 120kVp tube voltage, the radiation dose for the CP with 100kV was significantly reduced by over 15% (CTDI_vol: 6.57 ± 2.13mGy vs. 7.75 ± 2.63mGy; DLP: 190.40 ± 73.83mGy·cm vs. 225.09 ± 92.37mGy·cm) (all p<0.05).

Quantitative analysis of the image noise

The objective image quality parameters (CT value, image noise, and SNR, CNR) are summarized in Table 2-3. For the corticomedullary phase images, the image noise (in HU) for the renal cortex, renal medulla, PM, andwell as subcutaneous adipose tissue (SAT) were significantly reduced from (18.90, 16.87, 21.33, 19.05) on the ASiR-V-50% images and (18.41, 17.35, 18.21, 15.28) on the DLIR-M images to (14.34, 13.43, 13.64, 10.31) on the DLIR-H images (all p < 0.001). Moreover, the SNR for the renal cortex and renal medulla significantly increased from (10.14, 4.35) on the ASiR-V-50% images and (10.41, 4.28) on the DLIR-M images to (13.53, 5.47) on the DLIR-H images (statistical test). The CNR for the renal cortex and SAT significantly increased from (3.99, 5.97) on the ASiR-V-50% images and (4.42, 7.68) on the DLIR-M images to (5.89, 10.14) on the DLIR-H images respectively (all p < 0.001). For the PP images, the image noise (in HU) for the renal parenchyma, renal pelvis, lesion, PM and SAT on the ASIR-V 50% images (22.37, 18.08, 20.48, 20.28, 17.85) were significantly higher than that of the DLIR-M images (19.64, 18.36, 19.17, 16.99, 13.69) and DLIR-H images (15.80, 15.14, 15.22, 12.34, 9.91) (all p < 0.001). Furthermore, the SNR of the renal parenchyma, renal pelvis and lesion significantly increased from (6.80, 1.63, 3.00) on the ASiR-V-50% images and (7.77, 1.79, 3.24) on the DLIR-M images to (9.89, 2.24, 4.19) on the DLIR-H images (p< ).The CNR of the renal parenchyma, renal pelvis and lesion significantly increased from (2.70, 1.46, 1.58) on the ASiR-V-50% images and (3.12, 1.53, 1.84) on the DLIR-M images to (4.10, 2.00, 2.45) on the DLIR-H images respectively (all p < 0.001). When comparing ASIR-V-50% images in the parenchymal phase and DLIR images in the corticomedullary phase where radiation dose was reduced by 15%, DLIR-M and DLIR-H further reduced the image noise of subcutaneous adipose tissue by 15% and 40%, respectively.

Qualitative analysis

The mean subjective scores of the 7 imaging aspects were significantly higher for the DLIR-H (4.16±0.62) and DLIR-M (3.76±0.68) images compared with the ASIR-V-50% images (3.42±0.52) (all p < 0.001) (Table 4). With a 15% dose reduction, all the 7 imaging aspects scores of DLIR-H were over 3.5 points and those of DLIR-M were over 3 points, fully meeting the level for diagnostic quality. To be specific, DLIR (both DLIR-M and DLIR-H) images had significantly higher scores than the ASIR-V-50% images in diagnostic confidence, noise, sharpness and artifacts (p < 0.05). As shown in Figure 2 and Figure 3, compared with ASIR-V-50%, DLIR (both H and M) performed better in terms of Conspicuity of structures and, Smooth Sense, Image Contrast, Diagnostic confidence, Image Noise (all p < 0.001). Compared with ASIR-V-50%, DLIR (both H and M) performed better in terms of sharpness and artifact reduction (all p < 0.05).Compared with ASIR-V-50% PP on lesion level, DLIR-H and DLIR-M showed worse density contrast in lesion (renal cancer and metastatic lymph node) in PP (Figure 3). Interobserver agreements were moderate to excellent for the assessment of image quality (k = 0.77–0.90).

We conducted quantitative and qualitive assessments in renal triphasic-enhanced CT to explore the clinical application of the DLIR algorithm in reducing radiation dose and improving image quality compared with the state-of-the-art ASIR-V algorithm. Our results indicated that compared with ASIR-V-50%, DLIR-H improved overall image quality in terms of image noise, artifacts and vessel sharpness, as well as lesion specific features in terms of diagnostic confidence, lesion contrast and lesion contour in both the corticomedullary phase and parenchymal phase. DLIR-H also performed better in the corticomedullary phase with 15% reduction of radiation dose compared with ASIR-V-50% in the parenchymal phase. Furthermore, DLIR-H and DLIR-M showed significant improvements in the quantitative assessment (including SD, SNR and CNR) than ASIR-V-50-%.

There have been several studies comparing DLIR with ASIR-V in contrast-enhanced upper abdominal CT(11, 13). These studies mainly focused on image quality improvement of DLIR algorithms at reduced radiation doses. However, radiation dose reduction may reduce the diagnostic accuracy for renal lesion detectability. Thus, in our study design, we not only evaluated the image quality of different reconstructions, but also their lesion diagnostic performance. Specifically, we tested the dose reduction ability of DLIR on patients with ultrasound-confirmed renal space-occupying lesions and used the radiological information of both lesions and the surrounding background into consideration in our objective assessment. The dose reduction was moderate in this initial study, since we did not reduce the dose in all phases to avoid affecting the diagnosis. Instead, we used a low radiation dose during the corticalcorticomedullary phase and performed only one scan to minimize the patient's exposure to radiation. We compared the low dose of the corticomedullary phase with the normal dose of the parenchymal phase and found that potentially decreased image quality on low-dose scans could be compensated for lby using high-level reconstruction. For instance, this could easily make attenuation more homogenous in cyst (Fig. 2). However, we found it is difficult to use high-level reconstructions in normal dose scans, because this can easily obscure the heterogenous details of renal cancer and metastatic lymph nodes (Fig. 3).

Dose reduction did not diminish the reader's confidence in diagnosis. The improvement in image quality and lesion diagnostic confidence at 15% dose reduction suggests that we may be able to further reduce radiation dose in all imaging phases. Our results also indicated that while significantly reducing image noise, DLIR exhibits a capability to reduce artifacts and increase sharpness of vessels. This finding is consistent with a recent study of low-dose CT urography using deep learning image reconstruction by Cheng et al(21). Moreover, we extended the evaluation to both DLIR-H and DLIR-M and found that DLIR-M, similar to DLIR-H, could provide images with better image noise and excellent clinically acceptable quality with a 15% dose reduction compared with ASIR-V-50%.

Our study had several limitations. First, the sample size was relatively small and the patients were recruited from a single center. Second, we did not compare DLIR with ASIR-V with different weights, because ASIR-V-50% is commonly used in clinical practice for triphasic-enhanced CT of kidney. Third, because different phases were used for the comparisons, so any differences that were related to the phases themselves could not be studied. Finally, other deep learning-based reconstruction methods are available from other CT manufacturers (e.g., AiCE, Canon Medical), yet we only assessed DLIR (TrueFidelity^™) in our initial study. Future studies are expected to assess different deep learning-based reconstruction algorithms fully.

In conclusion, DLIR-H and DLIR-M significantly reduce image noise and generate images with better image quality and diagnostic confidence with a 15% dose reduction compared to ASIR-V-50% in renal CT. With DLIR, the potential to further reduce radiation dose in all imaging phases while maintaining the same image quality should be studied in the future.

Funding

This work was supported by Science and Technology Department of Jilin Province (Grant number 20200201397JC and 202002011516JC) and New technologies and new treatments Program of The First Hospital of Jilin University.

Competing interests

Shuai Zhang and Wenhuan Li declare relationship with the following companies: GE Healthcare. The remaining authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Authors’ contributions

All authors contributed to the study conception and design. Xiaobo Ding and Shuai Zhang carried out the studies, Jing Li and Xiang Qiu and Xiaohan Hu participated in collecting data. Xiaohan Hu and Pengfei Sun participated in material preparation, data collection and analysis. Xiaobo Ding, Wenhuan Li, Shuai Zhang and Huimao Zhang drafted the manuscript. Erick M Remer revised and edited the manuscript as a native speaker. Huimao Zhang and Yanbo Wang participated in study concept and design and critical revision of the manuscript. All authors read and approved the final manuscript.

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committees of The First Hospital of Jilin University.

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Acknowledgments

The authors would like to thank Xuan Zhang for assistance in data management and reconstruction.

Sodickson A, Baeyens P F, Andriole K P, et al. Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults. Radiology 2009,251:175-184.
Wang H, Zhang N, Huo L, et al. Dual-modality multi-atlas segmentation of torso organs from [18F]FDG-PET/CT images. Int J Comput Ass Rad 2019,14:473-482.
Caruso D, Zerunian M, Pucciarelli F, et al. Influence of Adaptive Statistical Iterative Reconstructions on CT Radiomic Features in Oncologic Patients. Diagnostics 2021,11:1000.
Greffier J, Frandon J, Larbi A, et al. CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 2020,30:487-500.
Greffier J, Frandon J, Si-Mohamed S, et al. Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data. Diagn Interv Imaging 2022,103:21-30.
Li Y, Jiang Y, Liu H, et al. A phantom study comparing low-dose CT physical image quality from five different CT scanners. Quant Imag Med Surg 2022,12:766-780.
Fukutomi A, Sofue K, Ueshima E, et al. Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in dual-energy CT of the abdomen: a phantom and clinical study. Eur Radiol 2023,33:1388-1399.
Racine D, Becce F, Viry A, et al. Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study. Physica medica 2020,76:28-37.
Fair E, Profio M, Kulkarni N, et al. Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction. J Comput Assist Tomogr 2022,46:604-611.
Yao Y, Guo B, Li J, et al. The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study. Quant Imaging Med Surg 2022,12:2777-2791.
Sun J, Li H, Li J, et al. Improving the image quality of pediatric chest CT angiography with low radiation dose and contrast volume using deep learning image reconstruction. Quant Imaging Med Surg 2021,11:3051-3058.
Cao L, Liu X, Qu T, et al. Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT. Eur Radiol 2023,33:1603-1611.
Toia G V, Zamora D A, Singleton M, et al. Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study. Am J Roentgenol 2023,220:283.
Jensen C T, Gupta S, Saleh M M, et al. Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases. Radiology 2022,303:90-98.
Bie Y, Yang S, Li X, et al. Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography. J Xray Sci Technol 2022,30:409-418.
Kaga T, Noda Y, Mori T, et al. Unenhanced abdominal low-dose CT reconstructed with deep learning-based image reconstruction: image quality and anatomical structure depiction. Jpn J Radiol 2022,40:703-711.
Nam J G, Hong J H, Kim D S, et al. Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Eur Radiol 2021,31:5533-5543.
Sun J, Li H, Li J, et al. Performance evaluation of using shorter contrast injection and 70 kVp with deep learning image reconstruction for reduced contrast medium dose and radiation dose in coronary CT angiography for children: a pilot study. Quant Imaging Med Surg 2021,11:4162-4171.
Jiang C, Jin D, Liu Z, et al. Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance. Insights Imaging 2022,13:182.
Sun J, Li H, Li H, et al. Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis. J Xray Sci Technol 2022,30:177-184.
Cheng Y, Han Y, Li J, et al. Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography. Brit J Radiol 2021,94:20201291.

Table 1. Quantitative image analysisstandards

Scores	1	2	3	4	5
Conspicuity of Structures	Cannot identify	Suboptimal	Average	Better than average	Excellentlyvisualized
Sharpness of vessels	Severe blurring	Severe to moderate blurring	Moderate blurring	Mild blurring	No blurring
Smooth Sense	Serverough	Moderate rough	Mild rough	Smooth	Verysmooth
Image Contrast	Poor	Suboptimal	Acceptable	Above average	Unacceptable
Diagnostic confidence	Non-diagnostic	Deficient	Diagnosable	Good	Verygood
Image Noise	Minimal	Below average	Average	Above average	Unacceptable
Artifact	Definite artifact	Probably artifact	Subtle artifact	Negligible artifact	Noartifacts

Table 2. Objective image analysis resultsin the 100kV cortical phase

Variables	DLIR-H	DLIR-M	ASiR-V-50%	P	P
Variables	DLIR-H	DLIR-M	ASiR-V-50%	P	DLIR-H vs. DLIR-M	DLIR-H vs. ASiR-V	DLIR-M vs. ASiR-V
Renal cortex
Attenuation (HU)	175.28±35.73	175.56±35.60	175.14±34.21	0.995	0.953	0.975	0.927
Nosie (HU)	14.34±4.82	18.41±5.70	18.90±6.08	<0.001^*	<0.001^*	<0.001^*	0.529
SNR	13.53±5.13	10.41±3.82	10.14±3.62	<0.001^*	<0.001^*	<0.001^*	0.608
CNR	5.89±2.09	4.42±1.50	3.99±1.23	<0.001^*	<0.001^*	<0.001^*	0.036^*
Renal Medulla
Attenuation (HU)	63.64±14.54	63.54±15.38	63.32±15.28	0.985	0.960	0.877	0.917
Nosie (HU)	13.43±4.61	17.35±5.59	16.87±5.68	<0.001^*	<0.001^*	<0.001^*	0.524
SNR	5.47±2.85	4.28±2.28	4.35±2.36	<0.001^*	<0.001^*	<0.001^*	0.827
CNR	0.58±0.74	0.44±0.58	0.38±0.57	0.026	0.071	0.011^*	0.457
Subcutaneous adipose tissue
Attenuation (HU)	-103.35±29.84	-115.83±92.81	-107.68±9.14	0.260	0.052	0.499	0.204
Nosie (HU)	10.31±2.63	15.28±9.60	19.05±3.05	<0.001^*	<0.001^*	<0.001^*	<0.001^*
SNR	-10.47±4.12	-8.18±6.14	-5.80±1.14	<0.001^*	<0.001^*	<0.001^*	<0.001^*
CNR	10.14±2.05	7.68±4.19	5.97±0.86	<0.001^*	<0.001^*	<0.001^*	<0.001^*
Psoas muscle
Attenuation (HU)	62.43±6.16	62.26±6.40	61.88±7.00	0.934	0.845	0.525	0.660
Nosie (HU)	13.64±5.90	18.21±2.79	21.33±3.29	<0.001^*	<0.001^*	<0.001^*	<0.001^*
SNR	4.86±1.00	3.50±0.65	3.00±0.67	<0.001^*	<0.001^*	<0.001^*	<0.001^*

Data given is mean ± SD. *P< 0.05. DLIR-H =deep-learning image reconstruction with high-strength, DLIR-M = deep-learning image reconstruction with-medium strength, ASiR-V = adaptive statistical iterative reconstruction-Veo, HU = Hounsfield units,CNR= contrast-to-noise ratio, SNR = signal-to-noise ratio, SD = standard deviation

Table 3. Objective image analysis results in the 120kV parenchymal phase

Variables	DLIR-H	DLIR-M	ASiR-V-50%	P	P
Variables	DLIR-H	DLIR-M	ASiR-V-50%	P	DLIR-H vs. DLIR-M	DLIR-H vs. ASiR-V	DLIR-M vs. ASiR-V
Renal parenchyma
Attenuation (HU)	142.37±23.76	142.62±24.00	142.99±24.56	0.998	0.937	0.844	0.907
Nosie (HU)	15.80±4.82	19.64±4.95	22.37±5.49	<0.001^*	<0.001^*	<0.001^*	<0.001^*
SNR	9.89±3.57	7.77±2.57	6.80±2.15	<0.001^*	<0.001^*	<0.001^*	0.068
CNR	4.10±1.57	3.12±1.13	2.70±0.97	<0.001^*	<0.001^*	<0.001^*	0.006^*
Renal pelvis
Attenuation (HU)	28.69±13.57	28.59±14.07	26.16±13.48	0.458	0.957	0.168	0.186
Nosie (HU)	15.14±7.67	18.36±8.09	18.08±6.96	< 0.001^*	0.002^*	0.005^*	0.788
SNR	2.24±1.36	1.79±1.10	1.63±1.00	< 0.001^*	0.002^*	<0.001^*	0.261
CNR	2.00±1.24	1.53±0.89	1.46±0.72	< 0.001^*	<0.001^*	<0.001^*	0.558
Lesion
Attenuation (HU)	58.34±43.88	58.26±44.06	57.95±43.54	0.999	0.990	0.950	0.960
Nosie (HU)	15.22±7.44	19.17±7.37	20.48±7.51	< 0.001^*	<0.001^*	<0.001^*	0.212
SNR	4.19±3.46	3.24±2.60	3.00±2.42	< 0.001^*	0.012^*	0.002^*	0.526
CNR	2.45±1.72	1.84±1.23	1.58±1.04	< 0.001^*	<0.001^*	<0.001^*	0.125
Subcutaneous adipose tissue
Attenuation (HU)	-97.98±20.51	-96.36±27.85	-106.87±95.82	0.249	0.820	0.212	0.140
Nosie (HU)	9.91±2.28	13.69±2.44	17.85±3.21	<0.001^*	<0.001^*	<0.001^*	<0.001^*
SNR	-10.41±3.20	-7.28±2.53	-6.54±7.24	<0.001^*	<0.001^*	<0.001^*	0.193
CNR	10.53±2.24	7.55±1.57	6.50±4.27	<0.001^*	<0.001^*	<0.001^*	0.002^*
Psoas muscle
Attenuation (HU)	63.43±6.38	63.67±6.01	63.44±6.72	0.952	0.776	0.996	0.780
Nosie (HU)	12.34±3.01	16.99±3.55	20.28±4.34	< 0.001^*	<0.001^*	<0.001^*	<0.001^*
SNR	5.36±1.18	3.89±0.84	3.29±0.88	<0.001^*	<0.001^*	<0.001^*	<0.001^*

Data given is mean ± SD. ^*P < 0.05.

Table 4. Subjective assessment of different algorithms in100kV cortical phase (CP) and 120kV parenchymal phase (PP)

	120kV PP	100kV CP		CP DLIR vs. PP ASiR-V 50% p value
	ASiR-V50%	DLIR-H	DLIR-M		CP DLIR-H vs.PP	CP DLIR-M vs.PP	DLIR-H vs. DLIR-M
Conspicuity of structures	3.00±0.15	3.65±0.49	3.06±0.30		<0.001^*	<0.001^*	<0.001^*
Sharpness of vessels	3.70±0.79	4.29±0.79	4.03±0.95		<0.001^*	<0.001^*	<0.05^*
Smooth Sense	3.08±0.28	4.93±0.28	4.10±0.43		<0.001^*	<0.001^*	<0.001^*
Image Contrast	3.62±0.71	4.45±0.58	3.91±0.91		<0.001^*	<0.001^*	<0.001^*
Diagnostic confidence	3.00±0.23	3.59±0.57	3.26±0.48		<0.001^*	<0.001^*	<0.001^*
Image Noise	3.20±0.43	4.43±0.62	3.85±0.68		<0.001^*	<0.001^*	<0.001^*
Artifact	2.99±0.14	4.25±0.80	3.06±0.30		<0.001^*	<0.05^*	<0.001^*
Average score	3.42±0.52	4.16±0.62	3.76±0.68		<0.001^*	<0.001^*	<0.001^*

Data given is mean ± SD.^*p< 0.05.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
17 Jul, 2024
Editor assigned by journal
16 Jul, 2024
Submission checks completed at journal
16 Jul, 2024
First submitted to journal
03 Jul, 2024

You are reading this latest preprint version

The Value of a Deep Learning Image Reconstruction Algorithm on Low Dose Triphasic-enhanced Renal CT

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

General Methods

CT Acquisition and Image Reconstruction

Objective Assessment

Subjective Assessment

Statistical Analysis

Results

Discussion

Declarations

References

Tables

Additional Declarations

Status:

Version 1