AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.Comment: Preprint submitted to Elsevie

Myocardial blood flow quantification by Rb-82 cardiac PET/CT: A detailed reproducibility study between two semi-automatic analysis programs.

Several analysis software packages for myocardial blood flow (MBF) quantification from cardiac PET studies exist, but they have not been compared using concordance analysis, which can characterize precision and bias separately. Reproducible measurements are needed for quantification to fully develop its clinical potential. Fifty-one patients underwent dynamic Rb-82 PET at rest and during adenosine stress. Data were processed with PMOD and FlowQuant (Lortie model). MBF and myocardial flow reserve (MFR) polar maps were quantified and analyzed using a 17-segment model. Comparisons used Pearson's correlation ρ (measuring precision), Bland and Altman limit-of-agreement and Lin's concordance correlation ρc = ρ·C b (C b measuring systematic bias). Lin's concordance and Pearson's correlation values were very similar, suggesting no systematic bias between software packages with an excellent precision ρ for MBF (ρ = 0.97, ρc = 0.96, C b = 0.99) and good precision for MFR (ρ = 0.83, ρc = 0.76, C b = 0.92). On a per-segment basis, no mean bias was observed on Bland-Altman plots, although PMOD provided slightly higher values than FlowQuant at higher MBF and MFR values (P < .0001). Concordance between software packages was excellent for MBF and MFR, despite higher values by PMOD at higher MBF values. Both software packages can be used interchangeably for quantification in daily practice of Rb-82 cardiac PET

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measured (Formula presented.) -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC–AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC–AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE–AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE–AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.</p

Detecting CTP Truncation Artifacts in Acute Stroke Imaging from the Arterial Input and the Vascular Output Functions

Background Current guidelines for CT perfusion (CTP) in acute stroke suggest acquiring scans with a minimal duration of 60-70 s. But even then, CTP analysis can be affected by truncation artifacts. Conversely, shorter acquisitions are still widely used in clinical practice and are usually sufficient to reliably estimate lesion volumes. We aim to devise an automatic method that detects scans affected by truncation artifacts. Methods Shorter scan durations are simulated from the ISLES’18 dataset by consecutively removing the last CTP time-point until reaching a 10 s duration. For each truncated series, perfusion lesion volumes are quantified and used to label the series as unreliable if the lesion volumes considerably deviate from the original untruncated ones. Afterwards, nine features from the arterial input function (AIF) and the vascular output function (VOF) are derived and used to fit machine-learning models with the goal of detecting unreliably truncated scans. Methods are compared against a baseline classifier solely based on the scan duration, which is the current clinical standard. The ROC-AUC, precision-recall AUC and the F1-score are measured in a 5-fold cross-validation setting. Results Machine learning models obtained high performance, with a ROC-AUC of 0.964 and precision-recall AUC of 0.958 for the best performing classifier. The highest detection rate is obtained with support vector machines (F1-score = 0.913). The most important feature is the AIFcoverage, measured as the time difference between the scan duration and the AIF peak. In comparison, the baseline classifier yielded a lower performance of 0.940 ROC-AUC and 0.933 precision-recall AUC. At the 60-second cutoff, the baseline classifier obtained a low detection of unreliably truncated scans (F1-Score = 0.638). Conclusions Machine learning models fed with discriminant AIF and VOF features accurately detected unreliable stroke lesion measurements due to insufficient acquisition duration. Unlike the 60s scan duration criterion, the devised models are robust to variable contrast injection and CTP acquisition protocols and could hence be used for quality assurance in CTP post-processing software

Automatic Individual Arterial Input Functions Calculated From PCA Outperform Manual and Population-Averaged Approaches for the Pharmacokinetic Modeling of DCE-MR Images

[EN] Background: To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on prin- cipal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. Methods: The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Results: Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). Conclusion: The automatic PCA-based approach minimizes the variability associated to obtaining individual volume- based AIFs in DCE-MR studies of the prostate.Sanz Requena, R.; Prats-Montalbán, JM.; Marti Bonmati, L.; Alberich Bayarri, A.; García Martí, G.; Pérez, R.; Ferrer Riquelme, AJ. (2015). Automatic Individual Arterial Input Functions Calculated From PCA Outperform Manual and Population-Averaged Approaches for the Pharmacokinetic Modeling of DCE-MR Images. Journal of Magnetic Resonance Imaging. 42:477-487. doi:10.1002/jmri.24805S47748742Leach, M. O., Brindle, K. M., Evelhoch, J. L., Griffiths, J. R., Horsman, M. R., Jackson, A., … Workman, P. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. British Journal of Cancer, 92(9), 1599-1610. doi:10.1038/sj.bjc.6602550Tofts, P. S., & Kermode, A. G. (1991). Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic Resonance in Medicine, 17(2), 357-367. doi:10.1002/mrm.1910170208Parker, G. J. M., Roberts, C., Macdonald, A., Buonaccorsi, G. A., Cheung, S., Buckley, D. L., … Jayson, G. C. (2006). Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 993-1000. doi:10.1002/mrm.21066Meng, R., Chang, S. D., Jones, E. C., Goldenberg, S. L., & Kozlowski, P. (2010). Comparison between Population Average and Experimentally Measured Arterial Input Function in Predicting Biopsy Results in Prostate Cancer. Academic Radiology, 17(4), 520-525. doi:10.1016/j.acra.2009.11.006Loveless, M. E., Halliday, J., Liess, C., Xu, L., Dortch, R. D., Whisenant, J., … Yankeelov, T. E. (2011). A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals. Magnetic Resonance in Medicine, 67(1), 226-236. doi:10.1002/mrm.22988Shukla-Dave, A., Lee, N., Stambuk, H., Wang, Y., Huang, W., Thaler, H. T., … Koutcher, J. A. (2009). Average arterial input function for quantitative dynamic contrast enhanced magnetic resonance imaging of neck nodal metastases. BMC Medical Physics, 9(1). doi:10.1186/1756-6649-9-4Wang, Y., Huang, W., Panicek, D. M., Schwartz, L. H., & Koutcher, J. A. (2008). Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data. Magnetic Resonance in Medicine, 59(5), 1183-1189. doi:10.1002/mrm.21432Rijpkema, M., Kaanders, J. H. A. M., Joosten, F. B. M., van der Kogel, A. J., & Heerschap, A. (2001). Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors. Journal of Magnetic Resonance Imaging, 14(4), 457-463. doi:10.1002/jmri.1207Singh, A., Rathore, R. K. S., Haris, M., Verma, S. K., Husain, N., & Gupta, R. K. (2009). Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRI. Journal of Magnetic Resonance Imaging, 29(1), 166-176. doi:10.1002/jmri.21624Shi, L., Wang, D., Liu, W., Fang, K., Wang, Y.-X. J., Huang, W., … Ahuja, A. T. (2013). Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering. Journal of Magnetic Resonance Imaging, 39(5), 1327-1337. doi:10.1002/jmri.24259Kim, J.-H., Im, G. H., Yang, J., Choi, D., Lee, W. J., & Lee, J. H. (2011). Quantitative dynamic contrast-enhanced MRI for mouse models using automatic detection of the arterial input function. NMR in Biomedicine, 25(4), 674-684. doi:10.1002/nbm.1784Li, X., Welch, E. B., Arlinghaus, L. R., Chakravarthy, A. B., Xu, L., Farley, J., … Yankeelov, T. E. (2011). A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 56(17), 5753-5769. doi:10.1088/0031-9155/56/17/018Fedorov, A., Fluckiger, J., Ayers, G. D., Li, X., Gupta, S. N., Tempany, C., … Fennessy, F. M. (2014). A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation. Magnetic Resonance Imaging, 32(4), 321-329. doi:10.1016/j.mri.2014.01.004Lin, Y.-C., Chan, T.-H., Chi, C.-Y., Ng, S.-H., Liu, H.-L., Wei, K.-C., … Wang, J.-J. (2012). Blind estimation of the arterial input function in dynamic contrast-enhanced MRI using purity maximization. Magnetic Resonance in Medicine, 68(5), 1439-1449. doi:10.1002/mrm.24144Roberts, C., Little, R., Watson, Y., Zhao, S., Buckley, D. L., & Parker, G. J. M. (2010). The effect of blood inflow andB1-field inhomogeneity on measurement of the arterial input function in axial 3D spoiled gradient echo dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 65(1), 108-119. doi:10.1002/mrm.22593Jackson, J. E. (1991). A Use’s Guide to Principal Components. Wiley Series in Probability and Statistics. doi:10.1002/0471725331Prats-Montalbán, J. M., Sanz-Requena, R., Martí-Bonmatí, L., & Ferrer, A. (2013). Prostate functional magnetic resonance image analysis using multivariate curve resolution methods. Journal of Chemometrics, 28(8), 672-680. doi:10.1002/cem.2585Eyal, E., Bloch, B. N., Rofsky, N. M., Furman-Haran, E., Genega, E. M., Lenkinski, R. E., & Degani, H. (2010). Principal Component Analysis of Dynamic Contrast Enhanced MRI in Human Prostate Cancer. Investigative Radiology, 45(4), 174-181. doi:10.1097/rli.0b013e3181d0a02fTofts, P. S. (1997). Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of Magnetic Resonance Imaging, 7(1), 91-101. doi:10.1002/jmri.1880070113Donahue, K. M., Burstein, D., Manning, W. J., & Gray, M. L. (1994). Studies of Gd-DTPA relaxivity and proton exchange rates in tissue. Magnetic Resonance in Medicine, 32(1), 66-76. doi:10.1002/mrm.1910320110Taylor, J. S., & Reddick, W. E. (2000). Evolution from empirical dynamic contrast-enhanced magnetic resonance imaging to pharmacokinetic MRI. Advanced Drug Delivery Reviews, 41(1), 91-110. doi:10.1016/s0169-409x(99)00058-7Port, R. E., Knopp, M. V., & Brix, G. (2001). Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic Resonance in Medicine, 45(6), 1030-1038. doi:10.1002/mrm.1137Dale, B. M., Jesberger, J. A., Lewin, J. S., Hillenbrand, C. M., & Duerk, J. L. (2003). Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI. Journal of Magnetic Resonance Imaging, 18(5), 575-584. doi:10.1002/jmri.10399Garpebring, A., Brynolfsson, P., Yu, J., Wirestam, R., Johansson, A., Asklund, T., & Karlsson, M. (2012). Uncertainty estimation in dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 69(4), 992-1002. doi:10.1002/mrm.24328Onxley, J. D., Yoo, D. S., Muradyan, N., MacFall, J. R., Brizel, D. M., & Craciunescu, O. I. (2014). Comprehensive Population-Averaged Arterial Input Function for Dynamic Contrast–Enhanced vMagnetic Resonance Imaging of Head and Neck Cancer. International Journal of Radiation Oncology*Biology*Physics, 89(3), 658-665. doi:10.1016/j.ijrobp.2014.03.006Chen, Y.-J., Chu, W.-C., Pu, Y.-S., Chueh, S.-C., Shun, C.-T., & Tseng, W.-Y. I. (2012). Washout gradient in dynamic contrast-enhanced MRI is associated with tumor aggressiveness of prostate cancer. Journal of Magnetic Resonance Imaging, 36(4), 912-919. doi:10.1002/jmri.23723Vos, E. K., Litjens, G. J. S., Kobus, T., Hambrock, T., Kaa, C. A. H. de, Barentsz, J. O., … Scheenen, T. W. J. (2013). Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3 T. European Urology, 64(3), 448-455. doi:10.1016/j.eururo.2013.05.045Yang, C., Karczmar, G. S., Medved, M., Oto, A., Zamora, M., & Stadler, W. M. (2009). Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic Resonance in Medicine, 61(4), 851-859. doi:10.1002/mrm.21912McGrath, D. M., Bradley, D. P., Tessier, J. L., Lacey, T., Taylor, C. J., & Parker, G. J. M. (2009). Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magnetic Resonance in Medicine, 61(5), 1173-1184. doi:10.1002/mrm.21959Orton, M. R., d’ Arcy, J. A., Walker-Samuel, S., Hawkes, D. J., Atkinson, D., Collins, D. J., & Leach, M. O. (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology, 53(5), 1225-1239. doi:10.1088/0031-9155/53/5/005Heisen, M., Fan, X., Buurman, J., van Riel, N. A. W., Karczmar, G. S., & ter Haar Romeny, B. M. (2010). The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data. Physics in Medicine and Biology, 55(16), 4871-4883. doi:10.1088/0031-9155/55/16/01

Automatic generation of absolute myocardial blood flow images using [15O]H2O and a clinical PET/CT scanner

PURPOSE: Parametric imaging of absolute myocardial blood flow (MBF) using [(15)O]H(2)O enables determination of MBF with high spatial resolution. The aim of this study was to develop a method for generating reproducible, high-quality and quantitative parametric MBF images with minimal user intervention. METHODS: Nineteen patients referred for evaluation of MBF underwent rest and adenosine stress [(15)O]H(2)O positron emission tomography (PET) scans. Ascending aorta and right ventricular (RV) cavity volumes of interest (VOIs) were used as input functions. Implementation of a basis function method (BFM) of the single-tissue model with an additional correction for RV spillover was used to generate parametric images. The average segmental MBF derived from parametric images was compared with MBF obtained using nonlinear least-squares regression (NLR) of VOI data. Four segmentation algorithms were evaluated for automatic extraction of input functions. Segmental MBF obtained using these input functions was compared with MBF obtained using manually defined input functions. RESULTS: The average parametric MBF showed a high agreement with NLR-derived MBF [intraclass correlation coefficient (ICC) = 0.984]. For each segmentation algorithm there was at least one implementation that yielded high agreement (ICC > 0.9) with manually obtained input functions, although MBF calculated using each algorithm was at least 10% higher. Cluster analysis with six clusters yielded the highest agreement (ICC = 0.977), together with good segmentation reproducibility (coefficient of variation of MBF <5%). CONCLUSION: Parametric MBF images of diagnostic quality can be generated automatically using cluster analysis and a implementation of a BFM of the single-tissue model with additional RV spillover correction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00259-011-1730-3) contains supplementary material, which is available to authorized users

Multimodality Quantitative Assessments of Myocardial Perfusion Using Dynamic Contrast Enhanced Magnetic Resonance and 15O-Labeled Water Positron Emission Tomography Imaging

Kinetic modeling of myocardial perfusion imaging data allows the absolute quantification of myocardial blood flow (MBF) and can improve the diagnosis and clinical assessment of coronary artery disease (CAD). Positron emission tomography (PET) imaging is considered the reference standard technique for absolute quantification, whilst oxygen-15 (15O)-water has been extensively implemented for MBF quantification. Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has also been used for MBF quantification and showed comparable diagnostic performance against (¹⁵ O)-water PET studies. We investigated for the first time the diagnostic performance of two different PET MBF analysis softwares PMOD and Carimas, for obstructive CAD detection against invasive clinical standard methods in 20 patients with known or suspected CAD. Fermi and distributed parameter modeling-derived MBF quantification from DCE-MRI was also compared against (15O)-water PET, in a subgroup of six patients. The sensitivity and specificity for PMOD was significantly superior for obstructive CAD detection in both per vessel (0.83, 0.90) and per patient (0.86, 0.75) analysis, against Carimas (0.75, 0.65) and (0.81, 0.70), respectively. We showed strong, significant correlations between MR and PET MBF quantifications (r = 0.83 - 0.92). However, DP and PMOD analysis demonstrated comparable and higher hemodynamic differences between obstructive versus (no, minor, or non)-obstructive CAD, against Fermi and Carimas analysis. Our MR method assessments against the optimum PET reference standard technique for perfusion analysis showed promising results in per segment level and can support further multimodality assessments in larger patient cohorts. Further MR against PET assessments may help to determine their comparative diagnostic performance for obstructive CAD detection