Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm

Objectives: To explore the significance of the hemodynamic parameters of Computerized Tomography Perfusion Imaging (CTPI) under the deconvolution optimization algorithm for the diagnosis and treatment of patients with acute cerebral infarct (ACI). Methods: A hundred and ten patients with ACI from December 2018 to September 2019 were selected for research, and CTPI was performed before and after Edaravone injection treatment. Then, the CTPI deconvolution algorithm based on the weighted adaptive (WA) total variation (TV) (WA-TV) optimization was constructed, which was compared with tensor total variation (TTV) and Motion-adaptive sparse parity (MASP). Brain Perfusion 4.0 was applied to obtain the relative time to peak (rTTP), the relative transit time of mean (rMTT), relative cerebral blood volume (rCBV), and relative cerebral blood flow (rCBF) of the core infarction area (CIA) and penumbra ischemic (PI). Results: In four parameters of rTTP, rMTT, rCBV, and CBF, the peak signal to noise ratio (PSNR) of the WA-TV algorithm was higher than the MSAP and TTV algorithms, while the Mean Square Error (MSE) and Mean Absolute Error (MAE) were lower than MSAP and TTV algorithms (P<0.05); the parameters of rCBV (71.56±9.87), rCBF (43.17±7.06) of the CIA before treatment were higher than PI (23.66±7.22; 18.37±3.99), rMTT (124.83±9.73) and rTTP (122.57±7.41) were lower than the PI (183.17±10.16); 150.74±9.74) (P<0.05). After treatment, the rCBV and rCBF of PI were higher than before treatment, and rMTT and rTTP were lower than before treatment (P<0.05), and there was no obvious difference in rCBV, rCBF, rMTT, and rTTP before and after treatment in the CIA (P>0.05). Conclusion: Compared with TTV and MASP, the WA-TV algorithm performs better in noise reduction and artifact reduction. The CTPI parameters of rCBV, rCBF, rMTT, and rTTP are all important indications for the diagnosis of PI and ACI. doi: https://doi.org/10.12669/pjms.37.6-WIT.4884 How to cite this:Fang B, Zhai H. Adoption of computerized tomography perfusion imaging in the diagnosis of acute cerebral infarct under optimized deconvolution algorithm. Pak J Med Sci. 2021;37(6):1687-1692. doi: https://doi.org/10.12669/pjms.37.6-WIT.4884 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Modified total variation norm for the maximum a posteriori ordered subsets expectation maximization reconstruction in fan-beam SPECT brain perfusion imaging

10.1117/12.877802 ◽

2011 ◽

Author(s):

Andrzej Krol ◽

Zhaoxia Yang ◽

Yuesheng Xu ◽

Axel Wismüller ◽

David H. Feiglin

Keyword(s):

Total Variation ◽

Perfusion Imaging ◽

Expectation Maximization ◽

Brain Perfusion ◽

Maximum A Posteriori ◽

A Posteriori ◽

Total Variation Norm ◽

Brain Perfusion Imaging ◽

Variation Norm

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Near infrared spectroscopy-measured changes in cerebral blood volume and cytochrome aa3 in newborn lambs exposed to hypoxia and hypercapnia, and ischemia: a comparison with changes in brain perfusion and O2 metabolism

Early Human Development ◽

10.1016/s0378-3782(99)00024-9 ◽

1999 ◽

Vol 55 (2) ◽

pp. 169-182 ◽

Cited By ~ 16

Author(s):

Majidah Shadid ◽

Lennard Hiltermann ◽

Louisa Monteiro ◽

Jehudith Fontijn ◽

Frank Van Bel

Keyword(s):

Infrared Spectroscopy ◽

Blood Volume ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Cerebral Blood Volume ◽

Brain Perfusion ◽

Cytochrome Aa3

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Comparison of Partial Volume Effects in Arterial and Venous Contrast Curves in CT Brain Perfusion Imaging

PLoS ONE ◽

10.1371/journal.pone.0097586 ◽

2014 ◽

Vol 9 (5) ◽

pp. e97586 ◽

Cited By ~ 2

Author(s):

Alan J. Riordan ◽

Edwin Bennink ◽

Jan Willem Dankbaar ◽

Max A. Viergever ◽

Birgitta K. Velthuis ◽

...

Keyword(s):

Perfusion Imaging ◽

Brain Perfusion ◽

Partial Volume ◽

Partial Volume Effects ◽

Brain Perfusion Imaging ◽

Ct Brain ◽

Volume Effects

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Perfusion Imaging of the Brain Using Z-Score and Dynamic Images Obtained by Subtracting Images from before and after Contrast Injection

Korean Journal of Radiology ◽

10.3348/kjr.2004.5.3.143 ◽

2004 ◽

Vol 5 (3) ◽

pp. 143 ◽

Cited By ~ 3

Author(s):

Sunseob Choi ◽

Haiying Liu ◽

Tae Beom Shin ◽

Jin Hwa Lee ◽

Seong Kuk Yoon ◽

...

Keyword(s):

Perfusion Imaging ◽

Z Score ◽

Contrast Injection ◽

Before And After ◽

The Brain

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Hand Laser Perfusion Imaging to Assess Radial Artery Patency: A Pilot Study

Journal of Clinical Medicine ◽

10.3390/jcm7100319 ◽

2018 ◽

Vol 7 (10) ◽

pp. 319 ◽

Cited By ~ 1

Author(s):

Ciro Indolfi ◽

Francesco Passafaro ◽

Sabato Sorrentino ◽

Carmen Spaccarotella ◽

Annalisa Mongiardo ◽

...

Keyword(s):

Radial Artery ◽

Perfusion Imaging ◽

Diagnostic Testing ◽

Area Under The Curve ◽

Diagnostic Technique ◽

Artery Occlusion ◽

Before And After ◽

Coronary Procedures ◽

Transradial Catheterization ◽

Artery Perfusion

Objectives: To test a novel diagnostic technique to assess radial artery perfusion after transradial catheterization. Background: Despite being mostly asymptomatic, radial artery occlusion (RAO) is not a benign complication, and its diagnosis is frequently missed because it requires time-consuming diagnostic testing. We developed a novel operator-independent diagnostic test to assess RAO after coronary procedures through a transradial access (TRA) by means of hand Laser Perfusion Imaging (LPI). Methods: One hundred patients were evaluated before and after TRA by means of the LPI. A radial perfusion index (RPI) was calculated as the ratio between the total perfusion measured during ulnar occlusion and total basal perfusion. Vascular Duplex scan (VDS) was used as the standard of reference to assess the artery patency. Results: LPI correctly identified RAO in 100% of cases. Post-procedural RPI was 0.89 ± 0.13 in patients with radial patency vs. 0.15 ± 0.04 in patients with RAO (p < 0.001). In line with these results, ROC analysis showed an excellent diagnostic performance of the LPI, that correctly identified all RAO cases (Area Under the Curve, AUC = 1.0; p < 0.001), with an optimal diagnostic cutoff at 0.2 RPI. Conclusions: LPI is a reliable diagnostic technique for RAO, offering the advantages of being quick and simple to perform.

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Interpreting CT perfusion in stroke

Practical Neurology ◽

10.1136/practneurol-2018-001917 ◽

2018 ◽

Vol 19 (2) ◽

pp. 136-142 ◽

Cited By ~ 7

Author(s):

Stevan Christopher Wing ◽

Hugh S Markus

Keyword(s):

Acute Stroke ◽

Perfusion Imaging ◽

Treatment Time ◽

Ct Perfusion ◽

Time Windows ◽

Onset Time ◽

Brain Perfusion ◽

Reperfusion Therapy ◽

Imaging Protocol ◽

Stroke Imaging

CT perfusion images can be rapidly obtained on all modern CT scanners and easily incorporated into an acute stroke imaging protocol. Here we discuss the technique of CT perfusion imaging, how to interpret the data and how it can contribute to the diagnosis of acute stroke and selection of patients for treatment. Many patients with acute stroke are excluded from reperfusion therapy if the onset time is not known or if they present outside of traditional treatment time windows. There is a growing body of evidence supporting the use of perfusion imaging in these patients to identify patterns of brain perfusion that are favourable for recanalisation therapy.

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