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1.
Sci Rep ; 11(1): 483, 2021 01 12.
Artículo en Inglés | MEDLINE | ID: mdl-33436837

RESUMEN

Quantitative measurement of lung perfusion is a promising tool to evaluate lung pathophysiology as well as to assess disease severity and monitor treatment. However, this novel technique has not been adopted clinically due to various technical and physiological challenges; and it is still in the early developmental phase where the correlation between lung pathophysiology and perfusion maps is being explored. The purpose of this research work is to quantify the impact of pulmonary artery occlusion on lung perfusion indices using lung dynamic perfusion CT (DPCT). We performed Lung DPCT in ten anesthetized, mechanically ventilated juvenile pigs (18.6-20.2 kg) with a range of reversible pulmonary artery occlusions (0%, 40-59%, 60-79%, 80-99%, and 100%) created with a balloon catheter. For each arterial occlusion, DPCT data was analyzed using first-pass kinetics to derive blood flow (BF), blood volume (BV) and mean transit time (MTT) perfusion maps. Two radiologists qualitatively assessed perfusion maps for the presence or absence of perfusion defects. Perfusion maps were also analyzed quantitatively using a linear segmented mixed model to determine the thresholds of arterial occlusion associated with perfusion derangement. Inter-observer agreement was assessed using Kappa statistics. Correlation between arterial occlusion and perfusion indices was evaluated using the Spearman-rank correlation coefficient. Our results determined that perfusion defects were detected qualitatively in BF, BV and MTT perfusion maps for occlusions larger than 55%, 80% and 55% respectively. Inter-observer agreement was very good with Kappa scores > 0.92. Quantitative analysis of the perfusion maps determined the arterial occlusion threshold for perfusion defects was 50%, 76% and 44% for BF, BV and MTT respectively. Spearman-rank correlation coefficients between arterial occlusion and normalized perfusion values were strong (- 0.92, - 0.72, and 0.78 for BF, BV and MTT, respectively) and were statically significant (p < 0.01). These findings demonstrate that lung DPCT enables quantification and stratification of pulmonary artery occlusion into three categories: mild, moderate and severe. Severe (occlusion ≥ 80%) alters all perfusion indices; mild (occlusion < 55%) has no detectable effect. Moderate (occlusion 55-80%) impacts BF and MTT but BV is preserved.


Asunto(s)
Arteriopatías Oclusivas/patología , Arteria Pulmonar/patología , Tomografía Computarizada por Rayos X/métodos , Animales , Animales Recién Nacidos , Arteriopatías Oclusivas/diagnóstico por imagen , Volumen Sanguíneo , Perfusión , Arteria Pulmonar/diagnóstico por imagen , Porcinos
2.
Quant Imaging Med Surg ; 6(1): 25-34, 2016 Feb.
Artículo en Inglés | MEDLINE | ID: mdl-26981452

RESUMEN

BACKGROUND: A critical source of variability in dynamic perfusion computed tomography (DPCT) is the arterial input function (AIF). However, the impact of the AIF location in lung DPCT has not been investigated yet. The purpose of this study is to determine whether the location of the AIF within the central pulmonary arteries influences the accuracy of lung DPCT maps. METHODS: A total of 54 lung DPCT scans were performed in three pigs using different rates and volumes of iodinated contrast media. Pulmonary blood flow (PBF) perfusion maps were generated using first-pass kinetics in three different AIF locations: the main pulmonary trunk (PT), the right main (RM) and the left main (LM) pulmonary arteries. A total of 162 time density curves (TDCs) and corresponding PBF perfusion maps were generated. Linear regression and Spearman's rank correlation coefficient were used to compare the TDCs. PBF perfusion maps were compared quantitatively by taking twenty six regions of interest throughout the lung parenchyma. Analysis of variance (ANOVA) was used to compare the mean PBF values among the three AIF locations. Two chest radiologists performed qualitative assessment of the perfusion maps using a 3-point scale to determine regions of perfusion mismatch. RESULTS: The linear regression of the TDCs from the RM and LM compared to the PT had a median (range) of 1.01 (0.98-1.03). The Spearman rank correlation between the TDCs was 0.88 (P<0.05). ANOVA analysis of the perfusion maps demonstrated no statistical difference (P>0.05). Qualitative comparison of the perfusion maps resulted in scores of 1 and 2, demonstrating either identical or comparable maps with no significant difference in perfusion defects between the different AIF locations. CONCLUSIONS: Accurate PBF perfusion maps can be generated with the AIF located either at the PT, RM or LM pulmonary arteries.

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