Ultra-low-dose chest computed tomography for interstitial lung disease using model-based iterative reconstruction with or without the lung setting

Akinori Hata, Masahiro Yanagawa, Osamu Honda, Tomo Miyata, Noriyuki Tomiyama, Akinori Hata, Masahiro Yanagawa, Osamu Honda, Tomo Miyata, Noriyuki Tomiyama

Abstract

The aim of this study was to assess the effects of reconstruction on the image quality and quantitative analysis for interstitial lung disease (ILD) using filtered back projection (FBP) and model-based iterative reconstruction (MBIR) with the lung setting and the conventional setting on ultra-low-dose computed tomography (CT).Fifty-two patients with known ILD were prospectively enrolled and underwent CT at an ultra-low dose (0.18 ± 0.02 mSv) and a standard dose (7.01 ± 2.66 mSv). Ultra-low-dose CT was reconstructed using FBP (uFBP) and MBIR with the lung setting (uMBIR-Lung) and the conventional setting (uMBIR-Stnd). Standard-dose CT was reconstructed using FBP (sFBP). Three radiologists subjectively evaluated the images on a 3-point scale (1 = worst, 3 = best). For objective image quality analysis, regions of interest were placed in the lung parenchyma and the axillary fat, and standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated. For 32 patients with clinically diagnosed idiopathic interstitial pneumonia, quantitative measurements including total lung volume (TLV) and the percentage of ILD volume (%ILDV) were obtained. The medians of 3 radiologists' scores were analyzed using the Wilcoxon signed-rank test and the objective noise was analyzed using the paired t test. The Bonferroni correction was used for multiple comparisons. The quantitative measurements were analyzed using the Bland-Altman method.uMBIR-Lung scored better than uMBIR-Stnd and worse than sFBP (P < .001), except for noise and streak artifact in subjective analysis. The SD decreased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). The SNR and CNR increased significantly in the order of uMBIR-Stnd, uMBIR-Lung, sFBP, and uFBP (P < .001). For TLV, there was no significant bias between ultra-low-dose MBIRs and sFBP (P > .3). For %ILDV, there was no significant bias between uMBIR-Lung and sFBP (p = 0.8), but uMBIR-Stnd showed significantly lower %ILDV than sFBP (P = .013).uMBIR-Lung provided more appropriate image quality than uMBIR-Stnd. Although inferior to standard-dose CT for image quality, uMBIR-Lung showed equivalent CT quantitative measurements to standard-dose CT.

Conflict of interest statement

N.T. received a research grant from GE Healthcare for this study. A.H., M.Y., O.H., and T.M. have no conflicts of interest related to this study.

The authors report no conflicts of interest.

Figures

Figure 1
Figure 1
Flowchart of enrolled patients and our procedure.
Figure 2
Figure 2
A 72-year-old woman with collagen vascular diseases (systemic sclerosis and polymyositis). Body mass index was 19.2 kg/m2. Axial images at the carina level reconstructed using FBP on standard-dose CT (A), FBP on ultra-low-dose CT (B), MBIR with the lung setting (C), and MBIR with the conventional setting (D). Ground-glass opacity (arrowheads) is depicted more clearly on MBIR with the lung setting compared with the conventional setting, but they are slightly blurred compared with FBP on standard-dose CT. The border between the lung and chest wall (black arrows) is blurred on MBIR with the conventional setting. The noise of MBIR with the conventional setting is less than that with the lung setting. FBP on ultra-low-dose CT shows poor image quality with much noise and streak artifact. CT = computed tomography, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
Figure 3
Figure 3
A 66-year-old man with idiopathic interstitial pneumonia (usual interstitial pneumonia pattern). Body mass index was 22.2 kg/m2. Axial images using FBP on standard-dose CT (A), FBP on ultra-low-dose CT (B), MBIR with the lung setting (C), and MBIR with the conventional setting (D). Bronchiectasis (black arrows), reticulation (arrowheads), and small vessels (white arrows) are depicted more clearly on MBIR with the lung setting than with the conventional setting. They are depicted similarly using FBP on standard-dose CT and MBIR with the lung setting. CT = computed tomography, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
Figure 4
Figure 4
A 73-year-old woman with idiopathic interstitial pneumonia (usual interstitial pneumonia pattern). Body mass index was 19.7 kg/m2. Axial images using FBP on standard-dose CT (A), FBP on ultra-low-dose CT (B), MBIR with the lung setting (C), and MBIR with the conventional setting (D). Small honeycombing is detectable on the FBP image on standard-dose CT, but it is difficult to see on ultra-low-dose CT images (arrows). CT = computed tomography, FBP = filtered back projection, MBIR = model-based iterative reconstruction.
Figure 5
Figure 5
Bland-Altman plots of TLV (A and B) and %ILDV (C and D) comparing MBIRs on ultra-low-dose CT with FBP on standard-dose CT. MBIR with lung setting (a and c) and MBIR with conventional setting (b and d). Solid thin line indicates mean difference (bias). Top and bottom dashed lines correspond to upper and lower margins of limits of agreement. Linear regression lines are indicated as solid bold lines; y = 0.004 x − 0.046, R2 = 0.001, and P = .86 for (A); y = −0.002 x − 0.009, R2 < 0.001, and P = 0.94 for (B); y = −0.031 x + 0.3, R2 = 0.01, and P = .58 for (C); y = −0.1.11 x + 0.5, R2 = 0.10, and P = 0.08 for (D). CT = computed tomography, FBP = filtered back projection, ILDV = the percentage of interstitial lung disease volume, MBIR = model-based iterative reconstruction, TLV = total lung volume%.

References

    1. Katsura M, Matsuda I, Akahane M, et al. Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 2012;22:1613–23.
    1. Yamada Y, Jinzaki M, Tanami Y, et al. Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study. Invest Radiol 2012;47:482–9.
    1. Yanagawa M, Gyobu T, Leung AN, et al. Ultra-low-dose CT of the lung. Acad Radiol 2014;21:695–703.
    1. Haggerty JE, Smith Ea, Kunisaki SM, et al. CT imaging of congenital lung lesions: effect of iterative reconstruction on diagnostic performance and radiation dose. Pediatr Radiol 2015;989–97.
    1. Koc G, Courtier JL, Phelps A, et al. Computed tomography depiction of small pediatric vessels with model-based iterative reconstruction. Pediatr Radiol 2014;44:787–94.
    1. Annoni AD, Andreini D, Pontone G, et al. Ultra-low-dose CT for left atrium and pulmonary veins imaging using new model-based iterative reconstruction algorithm. Eur Heart J Cardiovasc Imaging 2015;16:1366–73.
    1. Pickhardt PJ, Lubner MG, Kim DH, et al. Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. Am J Roentgenol 2012;199:1266–74.
    1. Lubner MG, Pooler BD, Kitchin DR, et al. Sub-milliSievert (sub-mSv) CT colonography: a prospective comparison of image quality and polyp conspicuity at reduced-dose versus standard-dose imaging. Eur Radiol 2015;25:2089–102.
    1. Notohamiprodjo S, Deak Z, Meurer F, et al. Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR). Eur Radiol 2014;140–6.
    1. Hérin E, Gardavaud F, Chiaradia M, et al. Use of Model-Based Iterative Reconstruction (MBIR) in reduced-dose CT for routine follow-up of patients with malignant lymphoma: dose savings, image quality and phantom study. Eur Radiol 2015;2362–70.
    1. Boudabbous S, Arditi D, Paulin E, et al. Model-Based Iterative Reconstruction (MBIR) for the Reduction of Metal Artifacts on CT. AJR Am J Roentgenol 2015;205:380–5.
    1. Thibault J-B, Sauer KD, Bouman CA, et al. A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 2007;34:4526–44.
    1. Yasaka K, Katsura M, Hanaoka S, et al. High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction. Eur J Radiol 2016;85:599–606.
    1. Hata A, Yanagawa M, Honda O, et al. Submillisievert CT using model-based iterative reconstruction with lung-specific setting: an initial phantom study. Eur Radiol 2016;26:4457–64.
    1. Wang R, Sui X, Schoepf UJ, et al. Ultralow-radiation-dose chest CT: accuracy for lung densitometry and emphysema detection. Am J Roentgenol 2015;204:743–9.
    1. Messerli M, Kluckert T, Knitel M, et al. Ultralow dose CT for pulmonary nodule detection with chest x-ray equivalent dose—a prospective intra-individual comparative study. Eur Radiol 2017;27:3290–9.
    1. Huber A, Landau J, Ebner L, et al. Performance of ultralow-dose CT with iterative reconstruction in lung cancer screening: limiting radiation exposure to the equivalent of conventional chest X-ray imaging. Eur Radiol Springer Berlin Heidelberg; 2016;26:3643–52.
    1. Ebner L, Bütikofer Y, Ott D, et al. Lung nodule detection by microdose CT versus chest radiography (standard and dual-energy subtracted). Am J Roentgenol 2015;204:727–35.
    1. Best AC, Meng J, Lynch AM, et al. Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality. Radiology 2008;246:935–40.
    1. Shin KE, Chung MJ, Jung MP, et al. Quantitative computed tomographic indexes in diffuse interstitial lung disease. J Comput Assist Tomogr 2011;35:266–71.
    1. Camiciottoli G, Orlandi I, Bartolucci M, et al. Lung CT densitometry in systemic sclerosis: correlation with lung function, exercise testing, and quality of life Chest. Am Coll Chest Phys 2007;131:672–81.
    1. Matsuoka S. Objective quantitative CT evaluation using different attenuation ranges in patients with pulmonary fibrosis: correlations with visual scores. Int J Respir Pulm Med 2016;3:
    1. Sverzellati N, Calabrò E, Chetta A, et al. Visual score and quantitative CT indices in pulmonary fibrosis: relationship with physiologic impairment. Radiol Med 2007;112:1160–72.
    1. Medicine AA of P in. AAPM Report No. 96 - The Measurement, Reporting, and Management of Radiation Dose in CT. Am Assoc Phys Med. 2008; 1–34.
    1. Boehm T, Willmann JK, Hilfiker PR, et al. Thin-section CT of the lung: does electrocardiographic triggering influence diagnosis? Radiology 2003;229:483–91.
    1. Mayo JR, Webb WR, Gould R, et al. High-resolution CT of the lungs: an optimal approach. Radiology 1987;163:507–10.
    1. Murata K, Khan A, Rojas KA, et al. Optimization of computed tomography technique to demonstrate the fine structure of the lung. Invest Radiol 1988;23:170–5.
    1. Padole A, Singh S, Ackman JB, et al. Submillisievert Chest CT With Filtered Back Projection and Iterative Reconstruction Techniques. AJR Am J Roentgenol 2014;203:772–81.
    1. Raghu G, Collard HR, Egan JJ, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 2011;183:788–824.
    1. Jacob J, Bartholmai BJ, Rajagopalan S, et al. Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures. Eur Respir J 2017;49:1601011.

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