Dataset Information

Pitfalls in quantitative myocardial PET perfusion II: Arterial input function.

ABSTRACT: RATIONALE:We aimed to define the impact of variable arterial input function on myocardial perfusion severity that may misguide interventional decisions and relates to limited capacity of 3D PET for high-count arterial input function of standard bolus R-82. METHODS:We used GE Discovery-ST 16 slice PET-CT, serial 2D and 3D acquisitions of variable Rb-82 dose in a dynamic circulating arterial function model, static resolution and uniformity phantoms, and in patients with dipyridamole stress to quantify per-pixel rest and stress cc·min-1·g-1, CFR and CFC with (+) and (-) 10% simulated change in arterial input. RESULTS:For intermediate, border zone severity of stress perfusion, CFR and CFC comprising 7% of 3987 cases, simulated arterial input variability of ± 10% may cause over or underestimation of perfusion severity altering interventional decisions. In phantom tests, current 3D PET has capacity for quantifying high activity of arterial input and high-count per-pixel values of perfusion metrics per artery or branches. CONCLUSIONS:Accurate, reproducible arterial input function is essential for at least 7% of patients at thresholds of perfusion severity for optimally guiding interventions and providing high-activity regional per-pixel perfusion metrics by 3D PET for displaying complex quantitative perfusion readily understood ("owned") by interventionalists to guide procedures.

SUBMITTER: Bui L

PROVIDER: S-EPMC7174279 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Pitfalls in quantitative myocardial PET perfusion II: Arterial input function.

Bui Linh L Kitkungvan Danai D Roby Amanda E AE Nguyen Tung T TT Gould K Lance KL

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology 20200303 2

<h4>Rationale</h4>We aimed to define the impact of variable arterial input function on myocardial perfusion severity that may misguide interventional decisions and relates to limited capacity of 3D PET for high-count arterial input function of standard bolus R-82.<h4>Methods</h4>We used GE Discovery-ST 16 slice PET-CT, serial 2D and 3D acquisitions of variable Rb-82 dose in a dynamic circulating arterial function model, static resolution and uniformity phantoms, and in patients with dipyridamole ...[more]

PMID: 32128675

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Project description:PurposeThe purpose of this study was to determine an optimal saturation-recovery time (TS) for minimizing the underestimation of arterial input function (AIF) in quantitative cardiac perfusion MRI without multiple gadolinium injections per subject.MethodsWe scanned 18 subjects (mean age = 59 ± 14 years, 9/9 males/females) to acquire resting perfusion data and 1 additional subject (age = 38 years, male) to obtain stress-rest perfusion data using a 5-fold accelerated pulse sequence with radial k-space sampling and applied k-space weighted image contrast (KWIC) filters on the same k-space data to retrospectively reconstruct five AIF images with effective TS ranging from 10 to 21.2 ms (2.8 ms steps). Undersampled images were reconstructed using a compressed sensing framework with temporal-total-variation and temporal-principal-component as 2 orthogonal sparsifying transforms. The image processing steps included, same motion correction across five different AIF images, signal normalization by the proton-density-weighted-image, signal-to-T1 conversion using a Bloch equation, T1 -to-gadolinium-concentration conversion assuming fast water exchange, T2 * correction to the AIF, and gadolinium-concentration to myocardial blood flow (MBF) conversion based on a Fermi model.ResultsAmong five TS values, the shortest TS (10 ms) produced significantly (P < 0.05) higher peak AIF and lower resting MBF (13.73 mM, 0.73 mL g-1 min-1 ) than 12.8 ms (11.24 mM, 0.89 mL g-1 min-1 ), 15.6 ms (9.56 mM, 1.05 mL g-1 min-1 ), 18.4 ms (8.55 mM, 1.17 mL g-1 min-1 ), and 21.2 ms (7.95 mM, 1.27 mL g-1 min-1 ). Similarly, shorter TS reduced underestimation of AIF (or overestimation of MBF) for both during stress and at rest, but this effect was canceled in myocardial-perfusion-reserve (MPR).ConclusionThis study demonstrates that TS of 10 ms reduces the underestimation of AIF and, hence, the overestimation of MBF compared with longer TS values (12.8-21.2 ms).

Project description:BackgroundQuantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification.MethodsGadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95% confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements.ResultsTwo clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63% of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95% CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs (p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation (p = NS to p = 0.03).ConclusionThe proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available.

Project description:BackgroundQuantification of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) by cardiovascular magnetic resonance (CMR) perfusion requires sampling of the arterial input function (AIF). While variation in the AIF sampling location is known to impact quantification by CMR and positron emission tomography (PET) perfusion, there is no evidence to support the use of a specific location based on their diagnostic accuracy in the detection of coronary artery disease (CAD). This study aimed to evaluate the accuracy of stress MBF and MPR for different AIF sampling locations for the detection of abnormal myocardial perfusion with expert visual assessment as the reference.MethodsTwenty-five patients with suspected or known CAD underwent vasodilator stress-rest perfusion with a dual-sequence technique at 3T. A low-resolution slice was acquired in 3-chamber view to allow AIF sampling at five different locations: left atrium (LA), basal left ventricle (bLV), mid left ventricle (mLV), apical left ventricle (aLV) and aortic root (AoR). MBF and MPR were estimated at the segmental level using Fermi function-constrained deconvolution. Segments were scored as having normal or abnormal perfusion by visual assessment and the diagnostic accuracy of stress MBF and MPR for each location was evaluated using receiver operating characteristic curve analysis.ResultsIn both normal (300 out of 400, 75 %) and abnormal segments, rest MBF, stress MBF and MPR were significantly different across AIF sampling locations (p < 0.001). Stress MBF for the AoR (normal: 2.42 (2.15-2.84) mL/g/min; abnormal: 1.71 (1.28-1.98) mL/g/min) had the highest diagnostic accuracy (sensitivity 80 %, specificity 85 %, area under the curve 0.90; p < 0.001 versus stress MBF for all other locations including bLV: normal: 2.78 (2.39-3.14) mL/g/min; abnormal: 2.22 (1.83-2.48) mL/g/min; sensitivity 91 %, specificity 63 %, area under the curve 0.81) and performed better than MPR for the LV locations (p < 0.01). MPR for the AoR (normal: 2.43 (1.95-3.14); abnormal: 1.58 (1.34-1.90)) was not superior to MPR for the bLV (normal: 2.59 (2.04-3.20); abnormal: 1.69 (1.36-2.14); p = 0.717).ConclusionsThe AIF sampling location has a significant impact on MBF and MPR estimates by CMR perfusion, with AoR-based stress MBF comparing favorably to that for the current clinical reference bLV.

Dataset Information

Pitfalls in quantitative myocardial PET perfusion II: Arterial input function.

Publications

Pitfalls in quantitative myocardial PET perfusion II: Arterial input function.

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