Abstract
Purpose
To create and determine the clinical feasibility of a model based on dynamic computed tomography (CT) and a bolus injection of iodine contrast medium for evaluation of pulmonary perfusion for healthy individuals and for patients with lung diseases.
Materials and methods
We analyzed pulmonary perfusion by means of dynamic 16-row multidetector CT scanning with a gamma residue function with adding a linear component (extended gamma function model) for 20 healthy individuals and in five patients.
Results
Four types of the time–attenuation curve (TAC) were identified for the peripheral lung. Although the TACs of most pixels for the peripheral lung comprised a single peak or a single-peak followed by another increase, no peak was observed for a small proportion of pixels, which either increased linearly or resulted in a delayed peak for healthy subjects. The ratios of these linearly increasing or delayed peak types of lung fields increased for pathological lungs. The analytical results for pathological cases showed that changes in lung perfusion, difficult to identify using only CT imaging, could be detected.
Conclusions
The extended gamma function model adequately evaluated pulmonary perfusion not only for normal regions, but also for structurally abnormal regions. Regional changes in perfusion could be evaluated by use of our model, and we confirmed its clinical feasibility for pulmonary perfusion analysis.
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References
Miles KA, Griffiths MR, Perfusion CT. A worthwhile enhancement? Br J Radiol. 2003;76:220–31.
Hoeffner EG, Case I, Jain R. Cerebral perfusion CT: technique and clinical applications. Radiology. 2004;231:632–44.
Gefter WB, Hatabu H. Functional lung imaging: emerging methods to visualize regional pulmonary physiology. Acad Radiol. 2003;10:1085–9.
Hatabu H, Tadamura E, Levin DL, Chen Q, Li W, Kim D, Prasad PV, et al. Quantitative assessment of pulmonary perfusion with dynamic contrast-enhanced MRI. Magn Reson Med. 1999;42:1033–8.
Tajik JK, Tran BQ, Hoffman EA. Assessing regional pulmonary microvascular transit times and flow using dynamic multi-slice CT. Am J Respir Crit Care Med. 1996;153:A816.
Guyton AC. Cardiac output and its regulation. In: Circulatory physiology 2. Philadelphia: Saunders; 1963. p. 168–70.
Van Beck EJR, Hoffman EA. Functional imaging: CT and MRI. Clin Chest Med. 2008;29:195–216.
Basran PS, Kay I, Spencer DP. Functional CT in lung with a conventional scanner: simulations and sampling considerations. Phys Med Biol. 2004;49:1755–71.
Clough AV, al-Tnawi A, Linehan JH, Dawson CA. Regional transit time estimation from image residue curves. Ann Biomed Eng. 1994;22:128–43.
Thompson HK Jr, Starmer CF, Whalen RE, Mcintosh HD. Indicator transit time considered as gamma variate. Circ Res. 1964;14:502–15.
R Development Core Team. R: a language and environment for statistical computing. Austria: R Foundation for Statistical Computing, Vienna; 2005. ISBN 3-900051-070-0. http://www.r-project.org.
Harvey M. Curvefit.com. Graphpad Software Inc. http://graphpad.com/curvefit/goodness_of_fit.htm.
Simon BA. Non-invasive imaging of regional lung function using x-ray computed tomography. J Clin Monit Comput. 2000;16:433–42.
Won C, Chon D, Tajik J, Tran BQ, Robinswood GB, Beck KC, et al. CT-based assessment of regional pulmonary microvascular blood flow parameters. J Appl Physiol. 2003;94:2483–93.
Axel L. Tissue transit time from dynamic computed tomography by a simple deconvolution technique. Investig Radiol. 1983;18:94–9.
Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusible tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223–32.
Kealey SM, Loving VA, Delong DM, Eastwood JD. User-defined vascular input function curves: influence on mean perfusion parameter values and signal-to-noise ratio. Radiology. 2004;231:587–93.
Wintermark M, Maeder P, Thiran JP, Schnyder P, Meuli R. Quantitative assessment of regional cerebral blood flows by perfusion CT studies at low injection rates: a critical review of underlying theoretical models. Eur Radiol. 2001;11:1220–30.
Hoffman EA, Chon D. Computed tomography studies of lung ventilation and perfusion. Proc Am Thorac Soc. 2005;2:492–8.
Ley-Zaporozhan J, Ley S, Kauczor HU. Morphological and functional imaging in COPD with CT and MRI: present and future. Eur Radiol. 2008;18:510–21.
Dougherty L, Asmuth JC, Gefter WB. Alignment of CT lung volumes with an optical flow method. Acad Radiol. 2003;10:249–54.
Gee J, Sundaram T, Hasegawa I, Uematsu H, Hatabu H. Characterization of regional pulmonary mechanics from serial MRI data. Acad Radiol. 2003;10:1147–52.
Lee MC, Cha S, Chang SM, Nelson SJ. Dynamic susceptibility contrast perfusion imaging of radiation effects in normal-appearing brain tissue: changes in first-pass and recirculation phases. J Magn Reson Imaging. 2005;21:683–93.
Bae KT, Heiken JP, Brink JA. Aortic and hepatic contrast medium enhancement at CT. Part 1. Prediction with a computer model. Radiology. 1998;207:647–55.
Kormano M, Dean PB. Extravascular contrast material: the major component of contrast enhancement. Radiology. 1976;121:379–82.
Brix G, Bahner ML, Hoffmann U, Horvath A, Schreiber W. Regional blood flow, capillary permeability, and compartmental volumes: measurement with dynamic CT-initial experience. Radiology. 1999;210:269–76.
Clough AV, Haworth ST, Roerig DL, Hoffman EA, Dawson CA. Influence of gravity on radiographic contrast material-based measurements of regional blood flow distribution. Acad Radiol. 2003;10:128–38.
Beck KC, Rehder K. Differences in regional vascular conductances in isolated dog lung. J Appl Physiol. 1986;61:530–8.
Austin JH. Pulmonary emphysema: imaging assessment of lung volume reduction surgery. Radiology. 1999;212:1–3.
Wu MT, Chang JM, Chiang AA, Lu JY, Hsu KK, Hsu WH, Yang CF. Use of quantitative CT to predict postoperative lung function in patients with lung cancer. Radiology. 1994;191:257–62.
Le Cras TD, Fernandez LG, Postura PA. Vascular growth and remodeling in compensatory lung growth following right lobectomy. J Appl Physiol. 2005;98:1140–8.
Yu H, Zhao S, Hoffman EA, Wang G. Ultra-low dose lung CT perfusion regularized by a previous scan. Acad Radiol. 2009;16:363–73.
Hoffman EA, Clough AV, Christensen GE, Lin CL, McLennan G, Reinhardt JM, et al. The comprehensive imaging-based analysis of the lung. Acad Radiol. 2004;11:1370–80.
Kalender WA, Wolf H, Suess C, Gies M, Greess H, Bautz WA. Dose reduction in CT by on-line tube current control: principles and validation on phantoms and cadavers. Eur Radiol. 1999;9:323–8.
Huda W. Dose and image quality in CT. Pediatr Radiol. 2002;32:709–13.
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Shimatani, Y., Kodani, K., Okada, J. et al. Clinical feasibility of pulmonary perfusion analysis using dynamic computed tomography and a gamma residue function. Jpn J Radiol 31, 243–252 (2013). https://doi.org/10.1007/s11604-012-0175-3
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DOI: https://doi.org/10.1007/s11604-012-0175-3