Dataset Information

Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD.

ABSTRACT:

Background

Interstitial lung disease (ILD) is a frequent complication of systemic sclerosis (SSc), and its early detection and treatment may prevent deterioration of lung function. Different vendors have recently made larger image matrices available as a post-processing option for computed tomography (CT), which could facilitate the diagnosis of SSc-ILD. Therefore, the objective of this study was to assess the effect of matrix size on lung image quality in patients with SSc by comparing a 1024-pixel matrix to a standard 512-pixel matrix and applying different reconstruction kernels.

Methods

Lung scans of 50 patients (mean age 54 years, range 23-85 years) with SSc were reconstructed with these two different matrix sizes, after determining the most appropriate kernel in a first step. Four observers scored the images on a five-point Likert scale regarding image quality and detectability of clinically relevant findings.

Results

Among the eight tested kernels, the Br59-kernel (sharp) reached the highest score (19.48 ± 3.99), although differences did not reach statistical significance. The 1024-pixel matrix scored higher than the 512-pixel matrix HRCT overall (p = 0.01) and in the subcategories sharpness (p < 0.01), depiction of bronchiole (p < 0.01) and overall image impression (p < 0.01), and lower for the detection of ground-glass opacities (GGO) (p = 0.04). No significant differences were found for detection of extent of reticulations/bronchiectasis/fibrosis (p = 0.50) and image noise (p = 0.09).

Conclusions

Our results show that with the use of a sharp kernel, the 1024-pixel matrix HRCT, provides a slightly better subjective image quality in terms of assessing interstitial lung changes, whereby GGO are more visible on the 512-pixel matrix. However, it remains to be answered to what extent this is related to the improved representation of the smallest structures.

SUBMITTER: Balmer BD

PROVIDER: S-EPMC9321522 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD.

Balmer Bettina D BD Blüthgen Christian C Bässler Bettina B Martini Katharina K Huber Florian A FA Ruby Lisa L Schönenberger Amadéa A Frauenfelder Thomas T

Diagnostics (Basel, Switzerland) 20220708 7

Background: Interstitial lung disease (ILD) is a frequent complication of systemic sclerosis (SSc), and its early detection and treatment may prevent deterioration of lung function. Different vendors have recently made larger image matrices available as a post-processing option for computed tomography (CT), which could facilitate the diagnosis of SSc-ILD. Therefore, the objective of this study was to assess the effect of matrix size on lung image quality in patients with SSc by comparing a 1024- ...[more]

PMID: 35885565

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Project description:BackgroundImage quality of photon-counting and energy integrating CT scanners changes with object size, dose to the object, and kernel selection.PurposeTo comprehensively compare task-generic image quality of photon-counting CT (PCCT) and energy integrating CT (EICT) systems as a function of phantom size, dose, and reconstruction kernel.MethodsA size-variant phantom (Mercury Phantom 3.0) was used to characterize the image quality of PCCT and EICT systems as a function of object size. The phantom contained five cylinders attached by slanted tapered sections. Each cylinder contained two sections: one uniform for noise, and the other with inserts for spatial resolution and contrast measurements. The phantom was scanned on Siemens' SOMATOM Force and NAEOTOM Alpha at 1.18 and 7.51 mGy without tube current modulation. CTDIvol was matched across two systems by setting the required tube currents, else, all other acquisition settings were fixed. CT sinograms were reconstructed using FBP and iterative (ADMIRE2 - Force; QIR2 - Alpha) algorithms with Body regular (Br) kernels. Noise Power Spectrum (NPS), Task Transfer Function (TTF), contrast-to-noise ratio (CNR), and detectability index (d') for a task of identifying 2-mm disk were evaluated based on AAPM TG-233 metrology using imQuest, an open-source software package. Averaged noise frequency (fav ) and 50% cut-off frequency for TTF (f50 ) were used as scalar metrics to quantify noise texture and spatial resolution, respectively. The difference between image quality metrics' measurements was calculated as IQPCCT - IQEICT .ResultsFrom Br40 (soft) to Br64 (sharp), f50 for air insert increased from 0.35 mm-1 ± 0.04 (standard deviation) to 0.76 mm-1 ± 0.17, 0.34 mm-1 ± 0.04 to 0.77 mm-1 ± 0.17, 0.37 mm-1 ± 0.02 to 0.95 mm-1 ± 0.17 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively, when averaged over all sizes and dose levels. Similarly, from Br40 to Br64, noise magnitude increased from 10.86 HU ± 7.12 to 38.61 HU ± 18.84, 10.94 HU ± 7.08 to 38.82 HU ± 18.70, 13.74 HU ± 11.02 to 52.11 HU ± 26.22 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively. The difference in fav was consistent across all sizes and dose levels. PCCT-70keV-VMI showed better consistency in contrast measurements for iodine and bone inserts than PCCT-T3D and EICT; however, PCCT-T3D had higher contrast for both inserts. From Br40 to Br64, considering all sizes and dose levels, CNR for iodine insert decreased from 52.30 ± 46.44 to 12.18 ± 10.07, 40.42 ± 33.42 to 9.48 ± 7.16, 39.94 ± 37.60 to 7.84 ± 6.67 for PCCT-T3D-QIR2, PCCT-70keV-QIR2, and EICT-ADMIRE2, respectively.ConclusionsBoth PCCT image types, T3D and 70-keV-VMI exhibited similar or better noise, contrast, CNR than EICT when comparing kernels with similar names. For 512 × 512 matrix, PCCT's sharp kernels had lower resolution than EICT's sharp kernels. For all image quality metrics, except extreme low, every dose condition had a similar set of IQ-matching kernels. It suggests that considering patient size and dose level to determine IQ-matching kernel pairs across PCCT and EICT systems may not be imperative while translating protocols, except when the signal to the detector is extremely low.

Project description:BackgroundSystemic sclerosis (SSc) is a rare connective tissue disease associated with rapid evolving interstitial lung disease (SSc-ILD), driving its mortality. Specific biomarkers associated with the evolution of the lung disease are highly needed. We aimed to identify specific biomarkers of SSc-ILD to predict the evolution of the disease. Nucleosomes are stable DNA/protein complexes that are shed into the blood stream making them ideal candidates for biomarkers.MethodsWe studied circulating cell-free nucleosomes (cf-nucleosomes) in SSc patients, 31 with ILD (SSc-ILD) and 67 without ILD. We analyzed plasma levels for cf-nucleosomes and investigated whether global circulating nucleosome levels in association with or without other biomarkers of interest for systemic sclerosis or lung fibrosis (e.g., serum growth factors: IGFBP-1 and the MMP enzyme: MMP-9), could be suitable potential biomarkers for the correct identification of SSc-ILD disease.ResultsWe found that H3.1 nucleosome levels were significantly higher in patients with SSc-ILD compared SSc patients without ILD (p < 0.05) and levels of MMP-9 were significantly increased in patients with SSc-ILD compared to SSc patients without ILD (p < 0.05). Conversely, IGFBP-1 was significantly reduced in patients with SSc-ILD compared to SSc without ILD (p < 0.001). The combination of cf-nucleosomes H3.1 coupled to MMP-9 and IGFBP-1 increased the sensitivity for the differential detection of SSc-ILD. High levels of accuracy were reached with this combined model: its performances are strong with 68.4% of positive predictive value and 77.2% of negative predictive value for 90% of specificity. With our model, we identified a significant negative correlation with FVC % pred (r = -0.22) and TLC % pred (r = -0.31). The value of our model at T1 (baseline) has a predictive power over the Rodnan score at T2 (after 6-18 months), showed by a significant linear regression with R2 = 19% (p = 0.013). We identified in the sole group of SSc-ILD patients a significant linear regression with a R2 = 54.4% with the variation of DLCO between T1 and T2 (p < 0.05).ConclusionIn our study, we identified a new blood-based model with nucleosomic biomarker in order to diagnose SSc-ILD in a SSc cohort. This model is correlated with TLC and FVC at baseline and predictive of the skin evolution and the DLCO. Further longitudinal exploration studies should be performed in order to evaluate the potential of such diagnostic and predictive model.

Project description:ObjectivesThis study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT).MethodsEleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs.ResultsIn conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences.ConclusionsIn conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm.Key points• Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT. • In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm. • Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Dataset Information

Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD.

Background

Methods

Results

Conclusions

Publications

Influence of CT Image Matrix Size and Kernel Type on the Assessment of HRCT in Patients with SSC-ILD.

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