Dynamic pulmonary CT perfusion using first-pass analysis technique with only two volume scans: Validation in a swine model

Zhao, Yixiao; Hubbard, Logan; Malkasian, Shant; Abbona, Pablo; Molloi, Sabee

doi:10.1371/journal.pone.0228110

Cited by 14 publications

(26 citation statements)

References 38 publications

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“…First-pass analysis has previously been used for blood flow measurement [22,23]. Assuming no contrast leakage over the measurement period ([ 1 , 2 ]), the whole-lobe compartment is used to calculate the integrated contrast mass change in the perfusion bed ( /∆ ) between V1 and V2 [9,24]. The average input contrast concentration ( ) of the pulmonary artery is also calculated between V1 and V2 (Fig 3).…”

Section: Two-volume Fpa Ct Perfusion Measurementmentioning

confidence: 99%

“…The average input contrast concentration ( ) of the pulmonary artery is also calculated between V1 and V2 (Fig 3). Thus, the blood flow ( ) measurement is represented by [9,24]:…”

Section: Two-volume Fpa Ct Perfusion Measurementmentioning

confidence: 99%

“…where is the average input concentration, /∆ is the rate of contrast mass change between 1 and 2 , ∆ = 2 − 1 . Finally, the regional perfusion of each 3D-segment is calculated and compared between the prospective and the reference retrospective FPA perfusion techniques, where the retrospective FPA was previously validated against fluorescent microspheres [24].…”

Section: Two-volume Fpa Ct Perfusion Measurementmentioning

confidence: 99%

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Two-Volume Dynamic CT Pulmonary Perfusion: Contrast Timing Optimization

Zhao¹,

Hubbard²,

Malkasian³

et al. 2021

Preprint

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PurposeTo develop and validate an optimal timing protocol for a low-radiation-dose CT pulmonary perfusion technique using only two volume scans.MethodsA total of 24 swine (48.5 ± 14.3 kg) underwent contrast-enhanced dynamic CT. Multiple contrast injections were made under different pulmonary perfusion conditions, resulting in a total of 147 complete pulmonary arterial input functions(AIF). Using the AIFs, an optimal timing protocol for acquisition of two-volume scans was developed for the first-pass CT perfusion technique. Specifically, the first volume scan was obtained at the base of the AIF using bolus-tracking and the second volume scan was obtained at the peak of the AIF using a time-to-peak relation derived by regression analysis. Additionally, a subset of 14 swine with 60 CT acquisitions were used to validate the prospective timing protocol. The prospective perfusion measurements using the two-volume scans, were quantitatively compared to the retrospective perfusion measurements using the entire AIF with t-test, linear regression and Bland-Altman analysis. The CT dose index(CTDI32vol) and size-specific dose estimate(SSDE) of the two-volume perfusion technique were also determined.ResultsThe pulmonary artery time-to-peak (TPA) was related to one-half of the contrast injection duration(TInj/2) by TPA = 1.06 TInj/2+0.090 (r=0.97). Simulated prospective two-volume perfusion measurements (PPRO) in ml/min/g were related to the retrospective measurements (PRETRO) by PPRO= 0.87PRETRO + 0.56 (r=0.88). The CTDI32vol and SSDE of the two-volume CT technique were estimated to be 28.4 and 47.0mGy, respectively.ConclusionThe optimal timing protocol can enable an accurate, low-radiation-dose two-volume dynamic CT perfusion technique.

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Section: Two-volume Fpa Ct Perfusion Measurementmentioning

confidence: 99%

“…The average input contrast concentration ( ) of the pulmonary artery is also calculated between V1 and V2 (Fig 3). Thus, the blood flow ( ) measurement is represented by [9,24]:…”

Section: Two-volume Fpa Ct Perfusion Measurementmentioning

confidence: 99%

Section: Two-volume Fpa Ct Perfusion Measurementmentioning

confidence: 99%

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Two-Volume Dynamic CT Pulmonary Perfusion: Contrast Timing Optimization

Zhao¹,

Hubbard²,

Malkasian³

et al. 2021

Preprint

Self Cite

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“…Fortunately, previous studies have demonstrated that accurate cardiac and pulmonary perfusion measurement is feasible with a first-pass analysis (FPA) technique using only two volume scans 9 , 10 , 15 : one at the base (V1) and one at the peak (V2) of the arterial input function (AIF). Nevertheless, these prior validations required the entire AIF curve and retrospectively down-sampled to two volume scans for blood flow measurement.…”

Section: Introductionmentioning

confidence: 99%

Contrast timing optimization of a two-volume dynamic CT pulmonary perfusion technique

Zhao

Hubbard

Malkasian

et al. 2022

Sci Rep

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The purpose of this study is to develop and validate an optimal timing protocol for a low-radiation-dose CT pulmonary perfusion technique using only two volume scans. A total of 24 swine (48.5 ± 14.3 kg) underwent contrast-enhanced dynamic CT. Multiple contrast injections were made under different pulmonary perfusion conditions, resulting in a total of 141 complete pulmonary arterial input functions (AIFs). Using all the AIF curves, an optimal contrast timing protocol was developed for a first-pass, two-volume dynamic CT perfusion technique (one at the base and the other at the peak of AIF curve). A subset of swine was used to validate the prospective two-volume pulmonary perfusion technique. The prospective two-volume perfusion measurements were quantitatively compared to the previously validated retrospective perfusion measurements with t-test, linear regression, and Bland–Altman analysis. As a result, the pulmonary artery time-to-peak ($${T}_{PA}$$ T PA ) was related to one-half of the contrast injection duration ($$\frac{{T}_{Inj}}{2}$$ T Inj 2 ) by $${T}_{PA}=1.01\frac{{T}_{Inj}}{2}+1.01$$ T PA = 1.01 T Inj 2 + 1.01 (r = 0.95). The prospective two-volume perfusion measurements (PPRO) were related to the retrospective measurements (PRETRO) by PPRO = 0.87PRETRO + 0.56 (r = 0.88). The CT dose index and size-specific dose estimate of the two-volume CT technique were estimated to be 28.4 and 47.0 mGy, respectively. The optimal timing protocol can enable an accurate, low-radiation-dose two-volume dynamic CT perfusion technique.

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“…The temporal iodine concentration is used in conjunction with kinetic models to derive physiological parameters such as blood flow (BF), blood volume (BV) and mean transit time (MTT). Lung dynamic perfusion CT (DPCT) has been applied in a porcine model [7][8][9][10] and in a patient cohort of suspected acute pulmonary embolism (PE) [11][12][13][14][15] . These studies established the feasibility of performing lung DPCT.…”

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confidence: 99%

Quantitative assessment of pulmonary artery occlusion using lung dynamic perfusion CT

Jiménez-Juan

Mehrez

Dey

et al. 2021

Sci Rep

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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.

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Dynamic pulmonary CT perfusion using first-pass analysis technique with only two volume scans: Validation in a swine model

Cited by 14 publications

References 38 publications

Two-Volume Dynamic CT Pulmonary Perfusion: Contrast Timing Optimization

Two-Volume Dynamic CT Pulmonary Perfusion: Contrast Timing Optimization

Contrast timing optimization of a two-volume dynamic CT pulmonary perfusion technique

Quantitative assessment of pulmonary artery occlusion using lung dynamic perfusion CT

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