Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease

doi:10.21203/rs.3.rs-4683236/v1

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Research Article

Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease

https://doi.org/10.21203/rs.3.rs-4683236/v1

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Aim

This study was aimed to compare the image quality and radiation dose between images reconstructed with deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) at prone position scanning in patients of early-stage interstitial lung disease (ILD).

Methods

This study prospectively enrolled 21 patients with early-stage ILD. All patients underwent high-resolution CT (HRCT) and low-dose CT (LDCT) scans. HRCT images were reconstructed with HIR using standard settings, and LDCT images were reconstructed with DLR (lung/bone kernel) in a mild, standard, or strong setting. Overall image quality, image noise, streak artifacts, and visualization of normal and abnormal ILD features were analysed.

Results

The effective dose of LDCT was 1.22 ± 0.09 mSv, 65.1% less than the HRCT dose. The objective noise of the LDCT DLR images was 33.0–111.8% that of the HRCT HIR images, with a signal-to-noise ratio (SNR) of 0.88 to 3.12 times that of the HRCT HIR images. The LDCT DLR was comparable to the HRCT HIR in terms of overall image quality. LDCT DLR (bone, strong) visualization of bronchiectasis and/or bronchiolectasis was significantly weaker than that of HRCT HIR. The LDCT DLR (all settings) did not significantly differ from the HRCT HIR in the evaluation of other abnormal features, including ground glass opacities (GGOs), architectural distortion, reticulation and honeycombing.

Conclusion

DLR was promising for maintaning image quality under a lower radiation dose in prone scanning for early ILD patients.

Deep learning reconstruction

Interstitial lung disease

Prone position

Low dose

Image quality

Interstitial lung disease (ILD) is a group of diseases caused by diffuse lung parenchymal disease, characterized by chronic inflammation of parabronchial tissues, alveolar spaces, and adjacent supporting structures, and interstitial fibrosis..¹² ILD can lead to lung remodeling, progressive lung function deterioration, a limited response to treatment, and even respiratory failure.³ Timely intervention, including drugs and nonmedical treatment during the early stage of ILD, is promising for attenuating lung function decline and improving clinical outcomes.⁴ To evaluate the suspected and known ILD, high-resolution CT (HRCT) was widely used, and prone position scanning was considered as a reliable protocol to differentiate pathological features from gravitation-dependent density in early ILD patients. ^5–8 A survey performed by European Society of Thoracic Imaging reported that 59% of the responders would perform additional prone position scanning in ILD evaluation⁹. However, repetitive scanning during the follow up of ILD can cause accumulative radiation exposure, which is correlated with a greater risk of cancer and other diseases. ¹⁰ Therefore, clinicians must weigh the benefits and risks of duplicated HRCT scans.

With the wide use of supercomputing power, a newly developed reconstruction method called deep learning reconstruction (DLR) has emerged. This technique is based on a deep convolutional neural network (DCCN) and, like a human brain, learns to distinguish the proper signal from noise via a training process that use low- and high-quality images.¹¹ Multiple phantom and clinical studies have shown that DLRs maintain or improve image quality at lower doses or at a given dose.^12–15 However, studies applying the DLR in early-stage ILD patients are limited. Therefore, this study explored whether DLR could maintain image quality and reduce radiation dosage for prone position scanning in early ILD patients.

Study design

This prospective study had been approved by our Institutional Review Board, and written informed consent was obtained from each participant. Patients were admitted to the outpatient clinic between December 2020 and September 2021. The inclusion criteria were as follows¹⁶: 1) typical findings of ILD in previous chest CT images; 2) no respiratory symptoms or pulmonary function test (PFT) impairment, and a diagnosis of early interstitial lung disease by a pulmonologist within the last year; 3) no other underlying lung disease (such as COPD, lung infection uncured, malignancies); and 4) need for a prone HRCT scan during a routine return visit. The exclusion criteria included: 1) inability to hold breath to finish a standard CT scan, 2) a history of lung surgery. 3) no interstitial lung abnormality (ILA) on the prone CT imaging. Finally, 21 ILD patients, including eight males and thirteen females, were enrolled.

Scan protocols and reconstruction methods

All patients were scanned with a 320-row detector CT (Aquilion ONE Genesis, Canon Medical System). Every patient underwent two consecutive unenhanced chest CT scans at 20-second intervals. First, an HRCT scan was performed using a 120 kVp tube voltage, automatic tube current, and standard deviation of 8.8 HU. Second, an LDCT scan was performed using a 120 kVp tube voltage and 30 mAs tube current. Other parameters were kept unchanged, including rotation time of 0.5 s/circle, detector number 0.5mm × 80, and pitch standard (PF 0.813/HP 65.0). Scanning was performed from the diaphragm to the pulmonary apex in the craniocaudal direction and prone position. Patients were required to hold their breath after full inspiration during the scan.

Image reconstruction

HRCT images were reconstructed with HIR (Adaptive Iterative Dose Reduction 3-Dimensional, AIDR3D) with a standard lung kernel, and LDCT images were reconstructed with DLR (Advanced Intelligence Clear-IQ Engine, AiCE) with lung/bone kernels at mild, standard, or strong levels. All images were reconstructed in the axial plane, and the slice thickness and increment were both 1 mm.

Objective image quality assessment

A radiologist with three years of experience independently performed the objective assessment by drawing the circular regions of interest (ROIs) within the tracheal lumen above the carina for three times. The ROI area was 0.98 ± 0.30 cm² (range: 0.48–1.56 cm²). The image noise was defined as the standard deviation of the ROI, and the signal-to-noise ratio (SNR) was defined as the ratio of the mean CT value to the image noise of the ROI.

Subjective image quality assessment

The subjective assessment included the overall image quality, subjective image noise, streak artifacts, and normal and abnormal lung structures. All patient information and scanning parameters were hidden and renumbered before the assessment. Two trained radiologists with three and five years of experience in ILD independently evaluated these images at a preselected lung window setting (window width 1200 HU, window level − 600 HU). No changes in these settings were allowed during the assessment to preclude any influence on image quality. When there was any uncertainty, one radiologist with over 40 years of experience would be turned to for help.

The overall image quality was scored using a five-point scale for the whole lung and each lung lobe:

1 score: Excellent (no artifacts or excessive noise, clear anatomical structures);

2 score: Good (minor artifacts, localized and unifocal loss of sharpness);

3 score: Fair (moderate artifacts without loss of diagnostic information, localized plurifocal loss of sharpness);

4 score: Poor (major artifacts with minor loss of diagnostic information);

5 score: Very poor (major artifacts with significant loss of diagnostic information).

The image noise and streak artifacts were scored on a three-point scale:

1 score: Unapparent or minimal;

2 score: Moderate but did not alter the identification of normal and/or abnormal structures;

3 score: Severe and altered the identification of normal and/or abnormal structures.

Normal lung features, including fissures, proximal bronchi and adjacent pulmonary vessels (above subsegmental level), peripheral bronchi and adjacent pulmonary vessels, and subpleural vessels, were evaluated. Abnormal lung features, including ground glass opacity (GGO), reticulation, bronchiectasis and bronchiolectasis, honeycombing, and architectural distortion, were evaluated at the lobar level. Both normal and abnormal structures were scored on a five-point scale:

0 score: No above abnormal lung features (only applied to abnormal structures evaluation)

1 score: Excellent image quality with sharp demarcation of structures;

2 score: Slight blurring of the structures, with unrestricted image evaluation;

3 score: Moderate blurring of the interface structures, with slightly restricted assessment;

4 score: Severe blurring and poorly defined structures, with uncertainty about the evaluation;

5 score: Severe reduced image quality, making reliable interpretation impossible. ^17–20

Radiation dosage

The dose-length product (DLP) and volume CT dose index (CTDI_vol) were extracted from patient protocols. The lateral (LAT) and anteroposterior (AP) diameters were measured at the mid-liver level of axial CT images with digital calipers, and the square root of the product of LAT and AP was defined as the effective diameter. The effective radiation dose (ED) was the product of DLP and a chest-specific conversion coefficient (0.014 mSv/mGy·cm).^{21 22}The size-specific dose estimates (SSDE) were the product of CTDI_vol and a size-specific conversion factor f_size, obtained based on the effective diameter by checking the AAPM Report 204.²³

Statistical analysis

Continuous variables were calculated as the mean ± standard deviation (SD), and categorical variables were summarized as numbers and frequencies. The interobserver agreement of the two radiologists was assessed using Cohen’s κ-test. The κ value was classified as poor (0–0.20), fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), or excellent (0.81–1.00).²⁴ The image quality and radiation dosage of the reconstructed HRCT and LDCT images were compared. All analyses were performed using paired sample t-tests or the Wilcoxon signed-rank test according to the results of the Shapiro-Wilk test. The data were analysed in IBM SPSS 20.0. The two-tailed statistical significance threshold was set at p < 0.05.

Twenty-one patients with early-stage ILD were enrolled, with a mean age of 62.2 ± 7.2 years (range: 45–78 years) and a mean BMI of 25.69 ± 3.05 kg/m². Seven patients were diagnosed with connective tissue disease-related (CTD-related) ILD, and the other patients were of unknown origin.

Radiation dosage

Compared with HRCT group, the LDCT scans had significantly lower CTDI_vol, DLP, ED and SSDE (p < 0.001), with a 64.1% radiation dose reduction. (Table 1).

Table 1

Radiation dose estimation of HRCT and LDCT images
	HRCT	LDCT	p value
CTDI_vol (mGy)	5.65 ± 1.35	2.00	< 0.001
DLP (mGy·cm)	239.86 ± 70.80	87.02 ± 6.61	< 0.001
ED (mSv)	3.36 ± 0.84	1.22 ± 0.09	< 0.001
SSDE (mGy)	7.57 ± 1.33	2.72 ± 0.20	< 0.001

Data are presented as the mean ± SD.

CTDI_vol, volume CT dose index; DLP, dose-length product; ED, effective radiation dose; SSDE, size-specific dose estimates.

Objective image quality assessment

The LDCT DLR images (apart from the mild lung algorithm) had significantly less image noise and greater SNR than the HRCT HIR images (p < 0.001 or p < 0.01, Table 2).

Table 2

Objective image quality assessment of HRCT HIR and LDCT DLR.
	CT (HU)	SD (HU)	SNR
HRCT HIR	-973.56 ± 7.50	44.00 ± 4.72	23.00 ± 2.17
LDCT DLR (lung, mild)	-976.72 ± 7.46^#	49.19 ± 4.54^*	20.19 ± 1.87^*
LDCT DLR (lung, standard)	-977.83 ± 7.18^#	40.32 ± 3.12^*	24.63 ± 1.91^&
LDCT DLR (lung, strong)	-976.58 ± 6.69^#	30.52 ± 2.87^*	32.62 ± 3.17^*
LDCT DLR (bone, mild)	-975.40 ± 6.67	31.11 ± 4.30^*	32.86 ± 4.40^*
LDCT DLR (bone, standard)	-973.19 ± 6.50	19.12 ± 2.25^*	53.99 ± 7.60^*
LDCT DLR (bone, strong)	-970.17 ± 6.88^#	14.53 ± 1.58^*	71.85 ± 13.65^*

Data are presented as the mean ± SD.

HIR, hybrid iterative reconstruction; DLR, deep learning reconstruction; SNR, signal-to-noise ratio.

^* p < 0.001 when compared with HRCT HIR.

^&p < 0.01 when compared with HRCT HIR.

^# p < 0.05 when compared with HRCT HIR.

Subjective image quality assessment

The interobserver agreement was substantial (κ value = 0.73). There was no significant difference in the overall image quality of whole lung or lobe pairs between HRCT HIR and LDCT DLR (Table 3, Fig. 1). Subjective image noise in the LDCT DLR group (lung/bone, standard/strong) was significantly lower than that in the HRCT HIR group(p < 0.001). Streak artifacts on LDCT DLR were not significantly different from those on HRCT HIR (Table 3).

Table 3

Whole lung overall image quality assessment for HRCT HIR and LDCT DLR.
Scores	HRCT HIR	LDCT DLR (lung, mild)	LDCT DLR (lung, standard)		LDCT DLR (lung, strong)		LDCT DLR (bone, mild)	LDCT DLR (bone, standard)		LDCT DLR (bone, strong)
Overall image quality
1	1(4.8)	1(4.8)	1(4.8)		1(4.8)		1(4.8)	1(4.8)		1(4.8)
2	12(57.1)	12(57.1)	12(57.1)		12(57.1)		12(57.1)	11(52.4)		11(52.4)
3	7(33.3)	8(38.1)	8(38.1)		8(38.1)		8(38.1)	9(42.9)		9(42.9)
4	1(4.8)	0	0		0		0	0		0
5	0	0	0		0		0	0		0
Image noise
1	0	1(4.8)	12(57.1)	*	19(90.5)	*	0	13(61.9)	*	12(57.1)	*
2	21(100)	20(95.2)	9(42.9)		2(9.5)		21(100)	8(38.1)		9(42.9)
3	0	0	0		0		0	0		0
Streak artifact
1	12(57.1)	10(47.6)	10(47.6)		11(52.4)		10(47.6)	10(47.6)		10(47.6)
2	8(38.1)	11(52.4)	11(52.4)		10(47.6)		11(52.4)	11(52.4)		11(52.4)
3	1(4.8)	0	0		0		0	0		0

Data are presented as the number of patients (n = 21) with percentages in parentheses.

HIR, hybrid iterative reconstruction; DLR, deep learning reconstruction.

^* p < 0.001 when compared with HRCT HIR.

Assessment of normal and abnormal lung features

Most of the normal lung features were scored as 1 or 2. LDCT DLR (lung, standard/strong; bone, mild/standard/strong) revealed significantly better visualization of the proximal bronchi and adjacent pulmonary vessels (p < 0.05). The LDCT DLR was comparable to the HRCT HIR in evaluating other normal lung structures (Table 4).

Table 4

Assessment of normal lung features in HRCT HIR and LDCT DLR.
Scores	HRCT HIR	LDCT DLR (lung, mild)	LDCT DLR (lung, standard)	LDCT DLR (lung, strong)	LDCT DLR (bone, mild)	LDCT DLR (bone, standard)	LDCT DLR (bone, strong)
Fissure
1	7(33.3)	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)
2	14(66.7)	12(57.1)	12(57.1)	12(57.1)	12(57.1)	12(57.1)	12(57.1)
3	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0
Proximal bronchi and adjacent pulmonary vessels
1	12(57.1)	14(66.7)	15(71.4) #	15(71.4) #	15(71.4) #	15(71.4) #	15(71.4) #
2	8(38.1)	6(28.6)	5(23.8)	5(23.8)	5(23.8)	5(23.8)	5(23.8)
3	1(4.8)	1(4.8)	1(4.8)	1(4.8)	1(4.8)	1(4.8)	1(4.8)
4	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0
Peripheral bronchi and adjacent pulmonary vessels
1	8(38.1)	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)
2	10(47.6)	12(57.1)	12(57.1)	12(57.1)	12(57.1)	12(57.1)	12(57.1)
3	3(14.3)	0	0	0	0	0	0
4	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0
Subpleural vessels
1	9(42.9)	10(47.6)	10(47.6)	10(47.6)	10(47.6)	10(47.6)	10(47.6)
2	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)	9(42.9)
3	2(9.5)	2(9.5)	2(9.5)	2(9.5)	2(9.5)	2(9.5)	2(9.5)
4	1(4.8)	0	0	0	0	0	0
5	0	0	0	0	0	0	0

Data are presented as the number of patients (n = 21) with percentages in parentheses.

HIR, hybrid iterative reconstruction; DLR, deep learning reconstruction.

^# p < 0.05 when compared with HRCT HIR.

In total, 126 lung lobes were assessed for abnormal lung structures, including GGOs (82, 65.1%), reticulation (39, 31.0%), bronchiectasis and/or bronchiolectasis (50, 39.7%), architectural distortion (28, 22.2%), and honeycombing (8, 6.3%). LDCT DLR (bone, strong) visualization of bronchiectasis and/or bronchiolectasis was significantly weaker than that of HRCT HIR (p < 0.05) (Fig. 1). The assessment of other abnormal lung structures assessment showed no significant differences between LDCT DLRs and HRCT HIRs (Table 5, Fig. 2).

Table 5

Assessment of abnormal lung structures in HRCT HIR and LDCT DLR.
Scores	HRCT HIR	LDCT DLR (lung, mild)	LDCT DLR (lung, standard)	LDCT DLR (lung, strong)	LDCT DLR (bone, mild)	LDCT DLR (bone, standard)	LDCT DLR (bone, strong)
GGO
0	44(34.9)	44(34.9)	44(34.9)	44(34.9)	44(34.9)	44(34.9)	44(34.9)
1	24 (19.0)	24 (19.0)	24 (19.0)	24 (19.0)	24 (19.0)	24 (19.0)	24 (19.0)
2	46(36.5)	48(38.1)	48(38.1)	48(38.1)	48(38.1)	48(38.1)	48(38.1)
3	12(9.5)	10(7.9)	10(7.9)	10(7.9)	10(7.9)	10(7.9)	10(7.9)
4	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0
Reticulation
0	87(69.0)	87(69.0)	87(69.0)	87(69.0)	87(69.0)	87(69.0)	87(69.0)
1	0	0	0	0	0	0	0
2	17(13.5)	17(13.5)	17(13.5)	17(13.5)	17(13.5)	17(13.5)	18(14.3)
3	21(16.7)	21(16.7)	21(16.7)	21(16.7)	21(16.7)	21(16.7)	20(15.9)
4	1(0.8)	1(0.8)	1(0.8)	1(0.8)	1(0.8)	1(0.8)	1(0.8)
5	0	0	0	0	0	0	0
Bronchiectasis and bronchiolectasis
0	76(60.3)	76(60.3)	76(60.3)	76(60.3)	76(60.3)	76(60.3)	76(60.3)	#
1	5(4.0)	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)	2(1.6)
2	25(19.8)	26(20.6)	26(20.6)	26(20.6)	26(20.6)	26(20.6)	27(21.4)
3	18(14.3)	18(14.3)	18(14.3)	18(14.3)	18(14.3)	18(14.3)	19(15.1)
4	2(1.6)	2(1.6)	2(1.6)	2(1.6)	2(1.6)	2(1.6)	2(1.6)
5	0	0	0	0	0	0	0
Architectural distortion
0	98(77.8)	98(77.8)	98(77.8)	98(77.8)	98(77.8)	98(77.8)	98(77.8)
1	8(6.3)	9(7.1)	9(7.1)	9(7.1)	9(7.1)	9(7.1)	9(7.1)
2	15(11.9)	14(11.1)	14(11.1)	14(11.1)	14(11.1)	14(11.1)	14(11.1)
3	5(4.0)	5(4.0)	5(4.0)	5(4.0)	5(4.0)	5(4.0)	5(4.0)
4	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0
Honeycombing
0	118 (93.7)	118 (93.7)	118 (93.7)	118 (93.7)	118 (93.7)	118 (93.7)	118 (93.7)
1	0	0	0	0	0	0	0
2	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)
3	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)	4(3.2)
4	0	0	0	0	0	0	0
5	0	0	0	0	0	0	0

Data are presented as the number of patients (n = 21) with percentages in parentheses.

HIR, hybrid iterative reconstruction; DLR, deep learning reconstruction.

^# p < 0.05 when compared with HRCT HIR.

This study revealed that the DLR method could maintain the quality and reduce the noise of LDCT images compared with HRCT HIR in early ILD patients.

The clinical importance of early ILD had been increasing recognized in recent years, and ILA was considered as possible signs of it based on systematic evaluation of large cohorts. ILA was defined as incidental finding of nondependent abnormalities affecting more than 5% of any lung zone at CT, including ground-glass or reticular abnormalities, lung distortion, traction bronchiectasis or bronchiolectasis, honeycombing, and nonemphysematous cysts. Until now, the relation between ILA and clinical outcomes and pathology of ILD was under discussion, and no consensus on the report of ILA had been reached.^{4 25}Therefore, we only chose several representative features in this study to evaluate the image quality between different image series.

Besides, to eliminate the influence of gravitation-dependent density, prone position scanning was designed for better recognition of ILA in this study. The survey performed by European Society of Thoracic Imaging had reported that 59% of responders indicated that additional prone position scanning would be performed on demand in clinical practice⁹. The high radiation exposure of prone position scanning may be the main cause that restricts its clinical use. In this study, the SSDE of prone HRCT was 7.57 ± 1.33 mGy, which was much greater than the 5.24 ± 0.87 mGy of supine RDCT in ILD reported by Xu et al. ²² and similar to the 7.29 ± 1.45 mGy in supine HRCT reported in our previous study²⁶. After combination with DLR, the radiation dose was reduced to 64.1% in prone LDCT scans, which is much lower than that in chest RDCT.

DLR is a newly developed reconstruction method based on artificial intelligence. It reduces image noise and improves spatial resolution simultaneously^{13 27}, setting it apart from the HIR, which trades off image noise and spatial resolution at different radiation doses.^{28 29} Several studies performed on coronary CTA, CTPA and abdominal ultrahigh-resolution CT revealed a concurrently significant reduction in the radiation dose and maintenance of image quality with DLR^30–32. This study applied DLR to prone chest LDCT scans, and the results confirmed that DLR image quality was maintained at a lower dose than that of HRCT HIR.

To date, studies comparing images reconstructed with DLR and HIR in chest CT have been limited. Singh et al. compared standard-dose CT HIR and LDCT DLR images from 22 patients and found no significant differences in image quality or lung nodule detection between these image series³². Lenfant et al. applied DLR and HIR (AIDR3D) reconstruction techniques to 45 and 48 CTPA images, respectively, and reported significantly greater image quality scores with DLR than with HIR. Nevertheless, the PE diagnostic confidence level showed no significant difference¹². Similarly, our study of prone chest LDCT in early ILD patients revealed no significant difference in the overall image quality of the whole lung or lobar level between LDCT DLR and HRCT HIR.

Six DLR reconstruction algorithms (lung/bone, mild/standard/strong) were applied in this study. The objective assessment indicated that DLR significantly reduced image noise and increased the SNR of LDCT, which was consistent with the subjective evaluation of image noise. Interestingly, the LDCT DLR (lung, mild) had significantly greater image noise and a lower SNR than did the HRCT HIR. Nevertheless, no significant difference was found in the evaluation of normal or abnormal lung features between the two series of images. The LDCT DLR (bone, strong) showed significantly less image noise and greater SNR than did the HRCT HIR, but the former was weaker for identifying in bronchiectasis and/or bronchiolectasis. According to previous studies, the bone kernel was recognized as a sharper, higher frequency kernel, as it was trained with high- and low-quality bone image pairs to produce an image quality similar to that of high-dose MBIR images. Several phantom studies have applied the bone kernel to small airway assessment and parenchymal density measurements and have shown improved spatial resolution and measurement accuracy. ^{33 34} Additionally, small group tests in diffuse lung disease patients revealed that sharper images are helpful for clinical evaluation.³⁵ A phantom study by Greffier J. et al. demonstrated that image smoothness and feature detectability improved as the DLR levels increased³⁶. Thus, the lung and bone kernels at the strong level were expected to produce the best image quality. However, our results were inconsistent with this expectation. Another DLR phantom study reported a difference in the behavior of the DLR strong level when comparing “body” and “body sharp” kernels. The researchers found that the mild and standard levels of the “body sharp” kernel better detected two different simulated lesions. In contrast, the strong level led to opposite changes in spatial resolution between high- and low-contrast lesions. The different spatial resolution results highlighted the potential uncertainty of “body sharp” kernels, and the authors conjectured that the training database of MBIR images might transfer nonlinear spatial resolution and noise characteristics to the DLR algorithm¹⁵. This finding might explain the opposite effects of “lung” and “bone” kernels in our study. Moreover, future studies with larger sample sizes and comparisons of multiple lesions should help clarify these findings.

This study has several limitations. First, as early ILD was usually occasional findings during the clinical practice, this study only enrolled a small number of patients, and more patients are needed to verify the results. Second, the scanning protocol was derived from a small-sample pretest in the supine position and was designed for equal image quality for diagnosis and a relatively lower radiation dose. Therefore, the maximal radiation dose reduction may not have been achieved, and further exploration of prone position scanning protocols is necessary. Third, the lack of unified clinical standards for prone position chest scans have led to selection bias, and a larger sample size and multicenter study may help reduce bias. The last but not least, the definition of ILA was subjective at present stage, and the relationship of ILA and clinical outcomes was still under study. Further studies could focus more on the features highly related to disease progression and clinical management.

DLR maintained image quality and reduced image noise in prone chest CT while reducing the radiation dose by 65%. LDCT reconstructed with DLR is potentially applicable for assessing early ILD in the prone position.

ILD, interstitial lung disease;

DLR, deep learning reconstruction;

HIR, hybrid iterative reconstruction;

HRCT, high-resolution CT;

LDCT, low-dose CT;

SNR, signal-to-noise ratio

GGO, ground glass opacity;

DCCN, deep convolutional neural network;

PFTs, pulmonary function tests;

ILA, interstitial lung abnormality;

AIDR3D, Adaptive Iterative Dose Reduction 3-Dimensional;

AiCE, Advanced Intelligence Clear-IQ Engine;

ROIs, regions of interest;

DLP, dose-length product;

CTDI_vol, volume CT dose index;

LAT, lateral diameter;

AP, anteroposterior diameter;

ED, effective radiation dose;

SSDE, size-specific dose estimates;

SD, standard deviation;

CTD, connective tissue disease.

Ethics approval and consent to participate

The Institutional Review Board of Peking Union Medical College Hospital approved this prospective study, and written informed consent was obtained from each participant.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this article.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Funding

This work was supported by National High Level Hospital Clinical Research Funding [grant numbers: 2022-PUMCH-A-034, 2022-PUMCH-B-069].

Authors' contributions

RZ had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis, including and especially any adverse effects. RZ, YX, ZM contributed substantially to the study design, data acquisition, analysis and interpretation, drafting and revision of the manuscript. JW, ZW, RX, YM, SP, JW, LS, PF and YW contributed substantially to the data acquisition, analysis and interpretation, and revision of the manuscript. XS and WS both contributed to study concept and design, study supervision, acquisition of data, analysis and interpretation of data, revision of the manuscript, final approval of the version to be submitted.

Acknowledgements

Not applicable

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Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease

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Abstract

Aim

Methods

Results

Conclusion

Figures

Introduction

Methods

Study design

Scan protocols and reconstruction methods

Image reconstruction

Objective image quality assessment

Subjective image quality assessment

Radiation dosage

Statistical analysis

Results

Radiation dosage

Objective image quality assessment

Subjective image quality assessment

Assessment of normal and abnormal lung features

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1