Top

Published in:

01-08-2019 | Computed Tomography | Computed Tomography

Identifying perfusion deficits on CT perfusion images using temporal similarity perfusion (TSP) mapping

Authors: Jill B. De Vis, Sunbin Song, Marie Luby, Jan Willem Dankbaar, Daniel Glen, Richard Reynolds, Brigitta K. Velthuis, Wouter Kroon, Lawrence L. Latour, Reinoud P. H. Bokkers

Published in: European Radiology | Issue 8/2019

Abstract

Objectives

Deconvolution-derived maps of CT perfusion (CTP) data may be confounded by transit delays. We propose temporal similarity perfusion (TSP) analysis to decrease CTP maps’ dependence on transit times and investigate its sensitivity to detect perfusion deficits.

Methods

CTP data of acute stroke patients obtained within 9 h of symptom onset was analyzed using a delay-insensitive singular value decomposition method and with TSP. The TSP method applies an iterative process whereby a pixel’s highest Pearson’s R value is obtained through comparison of a pixel’s time-shifted signal density time-series curve and the average whole brain signal density time-series curve. Our evaluation included a qualitative and quantitative rating of deconvolution maps (MTT, CBV, and TTP), of TSP maps, and of follow-up CT.

Results

Sixty-five patients (mean 68 (SD 13) years, 34 male) were included. A perfusion deficit was identified in 90%, 86%, 65%, and 84% of MTT, TTP, CBV, and TSP maps. The agreement of MTT, TTP, and TSP with CT follow-up was comparable but noticeably lower for CBV. CBV had the best relationship with final infarct volume (R² = 0.77, p < 0.001), followed by TSP (R² = 0.63, p < 0.001). Intra-rater agreement of an inexperienced reader was higher for TSP than for CBV/MTT maps (kappa’s of 0.79–0.84 and 0.63–0.7). Inter-rater agreement for experienced readers was comparable across maps.

Conclusions

TSP maps are easier to interpret for inexperienced readers. Perfusion deficits detected by TSP are smaller which may suggest less dependence on transit delays although more investigation is required.

Key Points

• Temporal similarity perfusion mapping assesses CTP data based on similarities in signal time-curves.

• TSP maps are comparable in perfusion deficit detection to deconvolution maps.

• TSP maps are easier to interpret for inexperienced readers.

Available only for authorised users

Albers GW, Marks MP, Kemp S et al (2018) Thrombectomy for stroke at 6 to 16 hours with selection by perfusin imaging. N Engl J Med 251:241–249

Nogueira RG, Jadhav AP, Haussen DC et al (2018) Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 378:11–21CrossRef

Sanelli PC, Lev MH, Eastwood JD, Gonzalez RG, Lee TY (2004) The effect of varying user-selected input parameters on quantitative values in CT perfusion maps. Acad Radiol 11:1085–1092CrossRefPubMed

Kudo K, Sasaki M, Ogasawara K, Terae S, Ehara S, Shirato H (2009) Difference in tracer delay-induced effect among deconvolution algorithms in CT perfusion analysis: quantitative evaluation with digital phantoms. Radiology 251:241–249CrossRefPubMed

Fiorella D, Heiserman J, Prenger E, Partovi S (2004) Assessment of the reproducibility of postprocessing dynamic CT perfusion data. AJNR Am J Neuroradiol 25:97–107PubMed

Sasaki M, Kudo K, Ogasawara K, Fujiwara S (2009) Tracer delay-insensitive algorithm can improve reliability of CT perfusion imaging for cerebrovascular steno-occlusive disease: comparison with quantitative single-photon emission CT. AJNR Am J Neuroradiol 30:188–193CrossRefPubMed

Wintermark M, Maeder P, Verdun FR et al (2000) Using 80 kVp versus 120 kVp in perfusion CT measurement of regional cerebral blood flow. AJNR Am J Neuroradiol 21:1881–1884PubMed

Hirata M, Sugawara Y, Fukutomi Y et al (2005) Measurement of radiation dose in cerebral CT perfusion study. Radiat Med 23:97–103PubMed

Wintermark M, Smith WS, Ko NU, Quist M, Schnyder P, Dillon WP (2004) Dynamic perfusion CT: optimizing the temporal resolution and contrast volume for calculation of perfusion CT parameters in stroke patients. AJNR Am J Neuroradiol 25:720–729PubMed

10.

Song S, Bokkers RPH, Luby M et al (2017) Temporal similarity perfusion mapping: a standardized and model-free method for detecting perfusion deficits in stroke. PLoS One 12:e0185552CrossRefPubMedPubMedCentral

11.

Kudo K, Sasaki M, Yamada K et al (2010) Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients. Radiology 254:200–209CrossRefPubMed

12.

van Seeters T, Biessels GJ, van der Schaaf IC et al (2014) Prediction of outcome in patients with suspected acute ischaemic stroke with CT perfusion and CT angiography: the Dutch acute stroke trial (DUST) study protocol. BMC Neurol 14:37CrossRefPubMedPubMedCentral

13.

Brott T, Adams HP Jr, Olinger CP et al (1989) Measurements of acute cerebral infarction: a clinical examination scale. Stroke 20:864–870

14.

Pexman JH, Barber PA, Hill MD et al (2001) Use of the Alberta stroke program early CT score (ASPECTS) for assessing CT scans in patients with acute stroke. AJNR Am J Neuroradiol 22:1534–1542

15.

Soares BP, Dankbaar JW, Bredno J et al (2009) Automated versus manual post-processing of perfusion-CT data in patients with acute cerebral ischemia: influence on interobserver variability. Neuroradiology 51:445–451CrossRefPubMedPubMedCentral

16.

Wintermark M, Lau BC, Chien J, Arora S (2008) The anterior cerebral artery is an appropriate arterial input function for perfusion-CT processing in patients with acute stroke. Neuroradiology 50:227–236CrossRefPubMed

17.

Wintermark M, Flanders AE, Velthuis B et al (2006) Perfusion-CT assessment of infarct core and penumbra: receiver operating characteristic curve analysis in 130 patients suspected of acute hemispheric stroke. Stroke 37:979–985CrossRef

18.

Axel L (1983) Tissue mean transit time from dynamic computed tomography by a simple deconvolution technique. Invest Radiol 18:94–99CrossRefPubMed

19.

Ibaraki M, Ohmura T, Matsubara K, Hinoshita T (2015) Reliability of CT perfusion-derived CBF in relation to hemodynamic compromise in patients with cerebrovascular steno-occlusive disease: a comparative study with 15O-PET. J Cereb Blood Flow Metab 35:1280–1288CrossRefPubMedPubMedCentral

20.

Luby M, Bykowski JL, Schellinger PD, Merino JG, Warach S (2006) Intra- and interrater reliability of ischemic lesion volume measurements on diffusion-weighted, mean transit time and fluid-attenuated inversion recovery MRI. Stroke 37:2951–2956CrossRefPubMedPubMedCentral

21.

Man F, Patrie JT, Xin W et al (2015) Delay-sensitive and delay-insensitive deconvolution perfusion-CT: similar ischemic core and penumbra volumes if appropriate threshold selected for each. Neuroradiology 57:573–581CrossRefPubMed

22.

Muir KW, Halbert HM, Baird TA, McCormick M, Teasdale E (2006) Visual evaluation of perfusion computed tomography in acute stroke accurately estimates infarct volume and tissue viability. J Neurol Neurosurg Psychiatry 77:334–339CrossRefPubMed

23.

van Seeters T, Biessels GJ, Niesten JM et al (2013) Reliability of visual assessment of non-contrast CT, CT angiography source images and CT perfusion in patients with suspected ischemic stroke. PLoS One 8:e75615CrossRefPubMedPubMedCentral

24.

Haacke EM, Li M, Juvvigunta F (2013) Tissue similarity maps (TSMs): a new means of mapping vascular behavior and calculating relative blood volume in perfusion weighted imaging. Magn Reson Imaging 31:481–489CrossRefPubMed

25.

Zöllner FG, Daab M, Weidner M et al (2015) Semi-automatic lung segmentation of DCE-MRI data sets of 2-year old children after congenital diaphragmatic hernia repair: initial results. Magn Reson Imaging 33:1345–1349CrossRefPubMed

26.

Li S, Zöllner FG, Merrem AD et al (2012) Wavelet-based segmentation of renal compartments in DCE-MRI of human kidney: initial results in patients and healthy volunteers. Comput Med Imaging Graph 36:108–118CrossRefPubMed

27.

Wismüller A, Meyer-Baese A, Lange O, Reiser MF, Leinsinger G (2006) Cluster analysis of dynamic cerebral contrast-enhanced perfusion MRI time-series. IEEE Trans Med Imaging 25:62–73CrossRefPubMed

Title: Identifying perfusion deficits on CT perfusion images using temporal similarity perfusion (TSP) mapping
Authors: Jill B. De Vis
Sunbin Song
Marie Luby
Jan Willem Dankbaar
Daniel Glen
Richard Reynolds
Brigitta K. Velthuis
Wouter Kroon
Lawrence L. Latour
Reinoud P. H. Bokkers
Publication date: 01-08-2019
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Stroke
Computed Tomography
Published in: European Radiology / Issue 8/2019
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5896-y

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Identifying perfusion deficits on CT perfusion images using temporal similarity perfusion (TSP) mapping

Abstract

Objectives

Methods

Results

Conclusions

Key Points

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

Please log in to get access to this content

Other articles of this Issue 8/2019

Prognostic value of MRI in assessing extramural venous invasion in rectal cancer: multi-readers’ diagnostic performance

Comparison between M-score and LR-M in the reporting system of contrast-enhanced ultrasound LI-RADS

Radiomics model of contrast-enhanced computed tomography for predicting the recurrence of acute pancreatitis

Visual assessment of calcification in solitary pulmonary nodules on chest radiography: correlation with volumetric quantification of calcification

Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features

Multi-parametric MRI zone-specific diagnostic model performance compared with experienced radiologists for detection of prostate cancer