@article{barkhausenImagingMyocardialPerfusion2004b,
  title = {Imaging of Myocardial Perfusion with Magnetic Resonance},
  author = {Barkhausen, J{\"o}rg and Hunold, Peter and Jochims, Markus and Debatin, J{\"o}rg F.},
  year = {2004},
  month = jun,
  journal = {J Magn Reson Imaging},
  volume = {19},
  number = {6},
  pages = {750--757},
  issn = {1053-1807},
  doi = {10.1002/jmri.20073},
  abstract = {Coronary artery disease (CAD) is currently the leading cause of death in developed nations. Reflecting the complexity of cardiac function and morphology, noninvasive diagnosis of CAD represents a major challenge for medical imaging. Although coronary artery stenoses can be depicted with magnetic resonance (MR) and computed tomography (CT) techniques, its functional or hemodynamic impact frequently remains elusive. Therefore, there is growing interest in other, target organ-specific parameters such as myocardial function at stress and first-pass myocardial perfusion imaging to assess myocardial blood flow. This review explores the pathophysiologic background, recent technical developments, and current clinical status of first-pass MR imaging (MRI) of myocardial perfusion.},
  langid = {english},
  pmid = {15170781},
  keywords = {Coronary Circulation,Coronary Disease,Humans,Magnetic Resonance Imaging}
}

@article{cernicanuTheoryBasedSignalCalibration2006c,
  title = {Theory-{{Based Signal Calibration}} with {{Single-Point T1 Measurements}} for {{First-Pass Quantitative Perfusion MRI Studies}}},
  author = {Cernicanu, Alexandru and Axel, Leon},
  year = {2006},
  month = jun,
  journal = {Academic Radiology},
  volume = {13},
  number = {6},
  pages = {686--693},
  issn = {1076-6332},
  doi = {10.1016/j.acra.2006.02.040},
  urldate = {2024-01-17},
  abstract = {Rationale and Objectives The aim of the study is to develop a theory-based signal calibration approach to be used for the conversion of signal-time curves to absolute contrast concentration-time curves for first-pass contrast-enhanced quantitative myocardial perfusion studies. Materials and Methods A normalization procedure was used to obtain a theoretical relationship between image signal and T1 and perform rapid single-point T1 measurements. T1 measurements were compared with reference T1 measurements. The method also was used in preliminary in vivo contrast-enhanced first-pass perfusion studies, and its applicability for dual-delay-time acquisitions was shown. A theory-based error sensitivity analysis was used to characterize the robustness of the method. Results The normalization procedure was implemented with minimal noise enhancement and insensitivity to small misregistrations through postprocessing techniques. The rapid T1 measurements are in excellent agreement with the reference measurements (R = 0.99, slope = 1.05, bias = -5.96 milliseconds). For in vivo studies, it is possible to simultaneously calibrate the arterial input function and myocardial enhancement curves acquired with different effective trigger delays through appropriate use of the theory-based signal calibration model. With this method, errors of in vivo baseline T1 estimates are large, but the effect of these large errors on the accuracy of contrast agent concentration estimates is limited. Conclusion This theory-based signal calibration approach can be used to perform rapid T1 mapping and provides flexibility for in vivo calibration of signal-time curves resulting from dual-delay-time first-pass contrast-enhanced acquisitions.},
  keywords = {cardiac perfusion,Signal calibration,T1 measurement},
  file = {C:\Users\habib.rebbah\Zotero\storage\CIWSLN8H\S1076633206001309.html}
}

@article{choiImprovingReliabilityPharmacokinetic2020,
  title = {Improving the {{Reliability}} of {{Pharmacokinetic Parameters}} at {{Dynamic}}                    {{Contrast-enhanced MRI}} in {{Astrocytomas}}: {{A Deep Learning Approach}}},
  shorttitle = {Improving the {{Reliability}} of {{Pharmacokinetic Parameters}} at {{Dynamic}}                    {{Contrast-enhanced MRI}} in {{Astrocytomas}}},
  author = {Choi, Kyu Sung and You, Sung-Hye and Han, Yoseob and Ye, Jong Chul and Jeong, Bumseok and Choi, Seung Hong},
  year = {2020},
  month = oct,
  journal = {Radiology},
  volume = {297},
  number = {1},
  pages = {178--188},
  publisher = {{Radiological Society of North America}},
  issn = {0033-8419},
  doi = {10.1148/radiol.2020192763},
  urldate = {2024-01-15},
  abstract = {Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent{\textendash}enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study included 386 patients (mean age, 52 years {$\pm$} 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence: AIF obtained from DSC-enhanced MRI (AIFDSC) and AIF measured at DCE MRI (AIFDCE). The model was trained to translate AIFDCE into AIFDSC, and after training, outputted neural-network-generated AIF (AIFgenerated DSC) with input AIFDCE. By using the three different AIFs, volume transfer constant (Ktrans), fractional volume of extravascular extracellular space (Ve), and vascular plasma space (Vp) were averaged from the tumor areas in the DCE MRI. To validate the model, intraclass correlation coefficients and areas under the receiver operating characteristic curve (AUCs) of the PK parameters in grading astrocytomas were compared by using different AIFs. Results The AIF-generated, DSC-derived PK parameters showed higher AUCs in grading astrocytomas than those derived from AIFDCE (mean Ktrans, 0.88 [95\% confidence interval \{CI\}: 0.81, 0.93] vs 0.72 [95\% CI: 0.63, 0.79], P = .04; mean Ve, 0.87 [95\% CI: 0.79, 0.92] vs 0.70 [95\% CI: 0.61, 0.77], P = .049, respectively). Ktrans and Ve showed higher intraclass correlation coefficients for AIFgenerated DSC than for AIFDCE (0.91 vs 0.38, P {$<$} .001; and 0.86 vs 0.60, P {$<$} .001, respectively). In AIF analysis, baseline signal intensity (SI), maximal SI, and wash-in slope showed higher intraclass correlation coefficients with AIFgenerated DSC than AIFDCE (0.77 vs 0.29, P {$<$} .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. Conclusion A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades. {\copyright}\,RSNA, 2020 Online supplemental material is available for this article.},
  file = {C:\Users\habib.rebbah\Zotero\storage\SZAMJA8Z\Choi et al. - 2020 - Improving the Reliability of Pharmacokinetic Param.pdf}
}

@article{christianAbsoluteMyocardialPerfusion2004a,
  title = {Absolute {{Myocardial Perfusion}} in {{Canines Measured}} by {{Using Dual-Bolus First-Pass MR Imaging}}},
  author = {Christian, Timothy F. and Rettmann, Dan W. and Aletras, Anthony H. and Liao, Steve L. and Taylor, Joni L. and Balaban, Robert S. and Arai, Andrew E.},
  year = {2004},
  month = sep,
  journal = {Radiology},
  volume = {232},
  number = {3},
  pages = {677--684},
  publisher = {{Radiological Society of North America}},
  issn = {0033-8419},
  doi = {10.1148/radiol.2323030573},
  urldate = {2024-02-08},
  abstract = {PURPOSE: To compare fluorescent microsphere measurements of myocardial blood flow (MBF) with qualitative, semiquantitative, and fully quantitative measurements of first-pass perfusion at magnetic resonance (MR) imaging. MATERIALS AND METHODS: Coronary artery occlusion or intracoronary adenosine infusion was successfully performed in 16 beagles; both procedures were performed simultaneously in one animal. MBF was assessed at microsphere analysis. First-pass myocardial perfusion MR imaging was performed during a dual-bolus administration of gadopentetate dimeglumine (0.0025 mmol/kg followed by 0.10 mmol/kg). The absolute myocardial perfusion at MR imaging was calculated by using Fermi function deconvolution methods. Qualitative, semiquantitative, and absolute myocardial perfusion MR imaging measurements were compared with microsphere MBF measurements by using paired t tests, linear correlation, and Bland-Altman analysis. RESULTS: Fully quantitative (ie, absolute) analysis of MBF at MR imaging correlated with microsphere MBF measurement (r = 0.95, P {$<$} .001) across the full range of blood flow rates encountered (from 0 to {$>$}5.0 mL/min/g). Similar close correlations were observed in endocardial and epicardial segments (representing approximately 0.85 g of the myocardium). With modest increases in MBF, qualitative measurements plateaued in the hyperemic zones. Semiquantitative measurements did not correlate with MBF as well (r = 0.69{\textendash}0.89); they plateaued around 3.0 mL/min/g. CONCLUSION: Dual-bolus MR imaging enabled accurate measurement of absolute epicardial and endocardial perfusion across a wide range of blood flow rates (0 to {$>$}5.0 mL/min/g). Use of qualitative MR imaging measures such as the contrast enhancement ratio led to substantially underestimated hyperemic blood flow measurements. {\copyright} RSNA, 2004}
}

@article{davillerDirectComparisonBayesian2021a,
  title = {Direct {{Comparison}} of {{Bayesian}} and {{Fermi Deconvolution Approaches}} for {{Myocardial Blood Flow Quantification}}: {{In}} Silico and {{Clinical Validations}}},
  shorttitle = {Direct {{Comparison}} of {{Bayesian}} and {{Fermi Deconvolution Approaches}} for {{Myocardial Blood Flow Quantification}}},
  author = {Daviller, Cl{\'e}ment and Boutelier, Timoth{\'e} and Giri, Shivraman and Ratiney, H{\'e}l{\`e}ne and Jolly, Marie-Pierre and Vall{\'e}e, Jean-Paul and Croisille, Pierre and Viallon, Magalie},
  year = {2021},
  journal = {Front Physiol},
  volume = {12},
  pages = {483714},
  issn = {1664-042X},
  doi = {10.3389/fphys.2021.483714},
  abstract = {Cardiac magnetic resonance myocardial perfusion imaging can detect coronary artery disease and is an alternative to single-photon emission computed tomography or positron emission tomography. However, the complex, non-linear MR signal and the lack of robust quantification of myocardial blood flow have hindered its widespread clinical application thus far. Recently, a new Bayesian approach was developed for brain imaging and evaluation of perfusion indexes (Kudo et al., 2014). In addition to providing accurate perfusion measurements, this probabilistic approach appears more robust than previous approaches, particularly due to its insensitivity to bolus arrival delays. We assessed the performance of this approach against a well-known and commonly deployed model-independent method based on the Fermi function for cardiac magnetic resonance myocardial perfusion imaging. The methods were first evaluated for accuracy and precision using a digital phantom to test them against the ground truth; next, they were applied in a group of coronary artery disease patients. The Bayesian method can be considered an appropriate model-independent method with which to estimate myocardial blood flow and delays. The digital phantom comprised a set of synthetic time-concentration curve combinations generated with a 2-compartment exchange model and a realistic combination of perfusion indexes, arterial input dynamics, noise and delays collected from the clinical dataset. The myocardial blood flow values estimated with the two methods showed an excellent correlation coefficient (r 2 {$>$} 0.9) under all noise and delay conditions. The Bayesian approach showed excellent robustness to bolus arrival delays, with a similar performance to Fermi modeling when delays were considered. Delays were better estimated with the Bayesian approach than with Fermi modeling. An in vivo analysis of coronary artery disease patients revealed that the Bayesian approach had an excellent ability to distinguish between abnormal and normal myocardium. The Bayesian approach was able to discriminate not only flows but also delays with increased sensitivity by offering a clearly enlarged range of distribution for the physiologic parameters.},
  langid = {english},
  pmcid = {PMC8072361},
  pmid = {33912066},
  keywords = {Bayesian,cardiovascular magnetic resonance,cardiovascular microcirculation,heart disease,ischemic lesion,myocardial blood flow,myocardial perfusion quantification,perfusion-weighted imaging},
  file = {C:\Users\habib.rebbah\Zotero\storage\NART6DI9\Daviller et al. - 2021 - Direct Comparison of Bayesian and Fermi Deconvolut.pdf}
}

@article{gatehouseAccurateAssessmentArterial2004a,
  title = {Accurate Assessment of the Arterial Input Function during High-Dose Myocardial Perfusion Cardiovascular Magnetic Resonance},
  author = {Gatehouse, Peter D. and Elkington, Andrew G. and Ablitt, Nicholas A. and Yang, Guang-Zhong and Pennell, Dudley J. and Firmin, David N.},
  year = {2004},
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {20},
  number = {1},
  pages = {39--45},
  issn = {1522-2586},
  doi = {10.1002/jmri.20054},
  urldate = {2024-02-08},
  abstract = {Purpose To develop a method for accurate measurement of the arterial input function (AIF) during high-dose, single-injection, quantitative T1-weighted myocardial perfusion cardiovascular magnetic resonance (CMR). Materials and Methods Fast injection of high-dose gadolinium with highly T1 sensitive myocardial perfusion imaging is normally incompatible with quantitative perfusion modeling because of distortion of the peak of the AIF caused by full recovery of the blood magnetization. We describe a new method that for each cardiac cycle uses a low-resolution short-axis (SA) image with a short saturation-recovery time immediately after the R-wave in order to measure the left ventricular (LV) blood pool signal, which is followed by a single SA high-resolution image with a long saturation-recovery time in order to measure the myocardial signal with high sensitivity. Fifteen subjects were studied. Using the new method, we compared the myocardial perfusion reserve (MPR) with that obtained from the dual-bolus technique (a low-dose bolus to measure the blood pool signal and a high-dose bolus to measure the myocardial signal). Results A small significant difference was found between MPRs calculated using the new method and the MPRs calculated using the dual-bolus method. Conclusion This new method for measuring the AIF introduced no major error, while removing the practical difficulties of the dual-bolus approach. This suggests that quantification of the MPR can be achieved using the simple high-dose single-bolus technique, which could also image multiple myocardial slices. J. Magn. Reson. Imaging 2004;20:39{\textendash}45. {\copyright} 2004 Wiley-Liss, Inc.},
  copyright = {Copyright {\copyright} 2004 Wiley-Liss, Inc.},
  langid = {english},
  keywords = {arterial input function,first-pass,gadolinium,heart,magnetic resonance,myocardial perfusion},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\NVGKZLAH\\Gatehouse et al. - 2004 - Accurate assessment of the arterial input function.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\3IT2FERE\\jmri.html}
}

@article{hanickeSignalStrengthSubsecond1990b,
  title = {Signal Strength in Subsecond {{FLASH}} Magnetic Resonance Imaging: {{The}} Dynamic Approach to Steady State},
  shorttitle = {Signal Strength in Subsecond {{FLASH}} Magnetic Resonance Imaging},
  author = {H{\"a}nicke, W. and Merboldt, K. D. and Chien, D. and Gyngell, M. L. and Bruhn, H. and Frahm, J.},
  year = {1990},
  journal = {Medical Physics},
  volume = {17},
  number = {6},
  pages = {1004--1010},
  issn = {2473-4209},
  doi = {10.1118/1.596452},
  urldate = {2024-01-17},
  abstract = {Subsecond fast low-angle shot (FLASH) magnetic resonance imaging (MRI) allows single shot studies of the human heart within measuring times of about 100{\textendash}300 ms depending on the data matrix. In contrast to conventional FLASH MRI subsecond applications acquire data during the approach to steady state. A detailed analysis of the saturation behavior of the signal is given for the ideal case of a rectangular slice profile. In a second step, realistic slice profiles assuming Gaussian-shaped excitation pulses were taken into account by means of a numerical solution of the Bloch equations. It turns out that the signal strength and the resulting image intensity is considerably higher than may be expected from steady-state considerations. Correspondingly optimized flip angles depend on the number of phase-encoding steps. Assuming long T1 relaxation times as, for example, encountered in muscle and brain tissue and repetition times of 5 ms or less, optimum flip angles are 12{$^\circ$}{\textendash}16{$^\circ$}. The use of even higher flip angles ({$\geq$}20{$^\circ$}) causes heavily distorted slice profiles and a dynamic increase of the effective slice thickness. Flip angles of the order of the Ernst angle (6{$^\circ$}) correspond to steady-state conditions and lead to considerable signal losses. The theoretical results are confirmed by subsecond FLASH MRI studies of the human heart using a 2.0 T whole-body system (Siemens Magnetom).},
  copyright = {{\copyright} 1990 American Association of Physicists in Medicine},
  langid = {english},
  keywords = {Audiometry,BIOMEDICAL RADIOGRAPHY,Cardiac dynamics,Digital radiography,heart,Heart,HEART,IMAGE PROCESSING,IMAGES,magnetic resonance,Magnetic resonance imaging,magnetic resonance studies,Medical image contrast,Medical imaging,Muscles,NMR IMAGING,Numerical solutions,physics,pulse sequences,PULSE TECHNIQUES,rapid imaging,RELAXATION TIME,Relaxation times,SIGNAL PROCESSING,technology,Time measurement},
  file = {C:\Users\habib.rebbah\Zotero\storage\QIUL8UFK\1.html}
}

@article{hsuNonlinearMyocardialSignal2008b,
  title = {Nonlinear Myocardial Signal Intensity Correction Improves Quantification of Contrast-Enhanced First-Pass {{MR}} Perfusion in Humans},
  author = {Hsu, Li-Yueh and Kellman, Peter and Arai, Andrew E},
  year = {2008},
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {27},
  number = {4},
  pages = {793--801},
  issn = {1522-2586},
  doi = {10.1002/jmri.21286},
  urldate = {2024-01-17},
  abstract = {Purpose To study the nonlinearity of myocardial signal intensity and gadolinium contrast concentration during first-pass perfusion MRI, and to compare quantitative perfusion estimates using nonlinear myocardial signal intensity correction. Materials and Methods The nonlinearity of signal intensity and contrast concentration was simulated by magnetization modeling and evaluated in phantom measurements. A total of 10 healthy volunteers underwent rest and stress dual-bolus perfusion studies using an echo-planar imaging sequence at both short and long saturation-recovery delay times (TD70 and TD150). Perfusion estimates were compared before and after the correction. Results The phantom data showed a linear relationship (R2 = 1.00 and 0.99) of corrected signal intensity vs. contrast concentrations. Peak myocardial contrast concentration averaged 0.64 {$\pm$} 0.10 mmol {$\cdot$} L-1 at rest and 0.91 {$\pm$} 0.21 mmol {$\cdot$} L-1 during stress for TD70 and were similar for TD150 (P = not significant [NS]). The corrections were larger for stress than rest perfusion and larger for TD150 than TD70 studies (both P {$<$} 0.01). Perfusion estimates of TD70 and TD150 stress studies were significantly different before the correction (P {$<$} 0.01) but equivalent after the correction (P = NS). Conclusion The nonlinearity between signal intensity and myocardial contrast concentration in perfusion MRI can be corrected through magnetization modeling. A nonlinear correction of myocardial signal intensity is feasible and improves quantitative perfusion analysis. J. Magn. Reson. Imaging 2008. {\copyright} 2008 Wiley-Liss, Inc.},
  copyright = {Copyright {\copyright} 2008 Wiley-Liss, Inc.},
  langid = {english},
  keywords = {contrast agent,dipyridamole,gadolinium,myocardial blood flow,myocardial perfusion,myocardial perfusion reserve},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\W4C8RC86\\Hsu et al. - 2008 - Nonlinear myocardial signal intensity correction i.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\3TJK5U2G\\jmri.html}
}

@article{jerosch-heroldMRIMyocardialPerfusion2006,
  title = {{{MRI}} of {{Myocardial Perfusion}}},
  author = {{Jerosch-Herold}, Michael and Muehling, Olaf and Wilke, Norbert},
  year = {2006},
  month = feb,
  journal = {Seminars in Ultrasound, CT and MRI},
  series = {Cardiac {{MR}} and {{CT}}},
  volume = {27},
  number = {1},
  pages = {2--10},
  issn = {0887-2171},
  doi = {10.1053/j.sult.2005.10.001},
  urldate = {2024-01-17},
  abstract = {An overwhelming number of myocardial perfusion studies are done by nuclear isotope imaging. Magnetic resonance imaging during the first pass of an injected, contrast bolus has some significant advantages for detection of blood flow deficits, namely higher spatial resolution, absence of ionizing radiation, and speed of the test. Previous clinical studies have demonstrated that excellent sensitivity and specificity can be achieved with MR myocardial perfusion imaging for detecting coronary artery disease, and assessment of patients with acute chest pain. Furthermore, an absolute quantification of myocardial blood flow is feasible, as was demonstrated by comparison of MR perfusion imaging, to measurements with isotope labeled microspheres in experimental models. An integrated assessment of perfusion, function, and viability, is thus feasible by MRI to answer important clinical challenges such as the identification of stunned or hibernating, but viable myocardium.},
  file = {C:\Users\habib.rebbah\Zotero\storage\MMQH2YTS\S0887217105000806.html}
}

@article{jerosch-heroldQuantificationMyocardialPerfusion2010,
  title = {Quantification of Myocardial Perfusion by Cardiovascular Magnetic Resonance},
  author = {{Jerosch-Herold}, Michael},
  year = {2010},
  month = oct,
  journal = {Journal of Cardiovascular Magnetic Resonance},
  volume = {12},
  number = {1},
  pages = {57},
  issn = {1532-429X},
  doi = {10.1186/1532-429X-12-57},
  urldate = {2019-02-06},
  abstract = {The potential of contrast-enhanced cardiovascular magnetic resonance (CMR) for a quantitative assessment of myocardial perfusion has been explored for more than a decade now, with encouraging results from comparisons with accepted "gold standards", such as microspheres used in the physiology laboratory. This has generated an increasing interest in the requirements and methodological approaches for the non-invasive quantification of myocardial blood flow by CMR. This review provides a synopsis of the current status of the field, and introduces the reader to the technical aspects of perfusion quantification by CMR. The field has reached a stage, where quantification of myocardial perfusion is no longer a claim exclusive to nuclear imaging techniques. CMR may in fact offer important advantages like the absence of ionizing radiation, high spatial resolution, and an unmatched versatility to combine the interrogation of the perfusion status with a comprehensive tissue characterization. Further progress will depend on successful dissemination of the techniques for perfusion quantification among the CMR community.},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\82V5J9TM\\Jerosch-Herold - 2010 - Quantification of myocardial perfusion by cardiova.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\KGA444SM\\1532-429X-12-57.html}
}

@article{kimMultisliceDualimagingSequence2006,
  title = {Multislice, Dual-Imaging Sequence for Increasing the Dynamic Range of the Contrast-Enhanced Blood Signal and {{CNR}} of Myocardial Enhancement at {{3T}}},
  author = {Kim, Daniel and Axel, Leon},
  year = {2006},
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {23},
  number = {1},
  pages = {81--86},
  issn = {1522-2586},
  doi = {10.1002/jmri.20471},
  urldate = {2018-12-15},
  abstract = {Purpose To develop a multislice, first-pass perfusion imaging sequence for increasing the effective dynamic range of the contrast-enhanced blood signal and the contrast-to-noise ratio (CNR) of myocardial wall enhancement. Materials and Methods A hybrid echo-planar imaging (EPI) pulse sequence was modified to acquire data for both the arterial input function (AIF) and the myocardium, using two different saturation-recovery time delays (TDs) and spatial resolutions, after a single saturation pulse. Five healthy subjects were scanned at 3T in three short-axis levels of the heart per heartbeat during passage of a high-dose bolus of contrast agent. The T1-weighted signal-time curve of the blood was converted to AIF using empirical conversion tables derived from phantom experiments. Results In all subjects the calculated AIF was consistently less distorted and higher for the short-TD protocol than for the long-TD protocol (peak concentration: 5.0 {$\pm$} 1.0 mM vs. 3.0 {$\pm$} 0.6 mM; P {$<$} 0.01). A combination of EPI, long TD, high-dose bolus of contrast agent, and 3T imaging yielded relatively strong peak enhancement in the myocardium (CNR = 11.9 {$\pm$} 3.3). Conclusion Our dual-imaging approach at 3T seems promising for acquiring both a relatively accurate AIF and a high CNR of myocardial wall enhancement in multiple slices per heartbeat. J. Magn. Reson. Imaging 2006. {\copyright} 2005 Wiley-Liss, Inc.},
  copyright = {Copyright {\copyright} 2005 Wiley-Liss, Inc.},
  langid = {english},
  keywords = {3T,arterial input function,EPI,heart,MRI,perfusion,T1},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\KF545E2A\\Kim et Axel - 2006 - Multislice, dual-imaging sequence for increasing t.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\56S3FK8T\\jmri.html}
}

@article{landisDeterminationMRIContrast2000b,
  title = {Determination of the {{MRI}} Contrast Agent Concentration Time Course in Vivo Following Bolus Injection: {{Effect}} of Equilibrium Transcytolemmal Water Exchange},
  shorttitle = {Determination of the {{MRI}} Contrast Agent Concentration Time Course in Vivo Following Bolus Injection},
  author = {Landis, Charles S. and Li, Xin and Telang, Frank W. and Coderre, Jeffrey A. and Micca, Peggy L. and Rooney, William D. and Latour, Lawrence L. and V{\'e}tek, G{\'a}bor and P{\'a}lyka, Ildik{\'o} and Springer Jr., Charles S.},
  year = {2000},
  journal = {Magnetic Resonance in Medicine},
  volume = {44},
  number = {4},
  pages = {563--574},
  issn = {1522-2594},
  doi = {10.1002/1522-2594(200010)44:4<563::AID-MRM10>3.0.CO;2-#},
  urldate = {2024-01-17},
  abstract = {For bolus-tracking studies, it is commonly assumed that CR concentration bears a linear relationship with the measured (usually longitudinal) 1H2O relaxation rate constant, R*1 {$\equiv$}(T1 *){\textendash}1. This requires that equilibrium transcytolemmal water exchange be in the fast exchange limit (FXL). However, though systems remain in fast exchange, the FXL will not usually obtain. Here, the consequences are considered: 1) the measurement of R1 * itself can be affected, 2) the resultant non-linear [CR]-dependence causes significant error by assuming FXL, 3) the thermodynamic [CR] (based on the space in which CR is actually distributed) can be determined, 4) transcytolemmal water permeability may be estimated, and 5) the pharmacokinetic parameters can be factored. For a 30-sec, 0.17 mmol/kg dose of GdDTPA2{\textendash}, the FXL assumption underestimates the [CR] maximum in rat thigh muscle by a factor of almost two. Similar results are obtained for a rat brain GS-9L gliosarcoma tumor model. Magn Reson Med 44:563{\textendash}574, 2000. Published 2000 Wiley-Liss, Inc.},
  copyright = {Published 2000 Wiley-Liss, Inc.},
  langid = {english},
  keywords = {bolus-tracking,concentration,contrast reagent,transcytolemmal water exchange},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\JQNNBYHV\\Landis et al. - 2000 - Determination of the MRI contrast agent concentrat.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\3FPX97B7\\1522-2594(200010)444563AID-MRM103.0.html}
}

@article{montalescot2013ESCGuidelines2013,
  title = {2013 {{ESC}} Guidelines on the Management of Stable Coronary Artery Disease: The {{Task Force}} on the Management of Stable Coronary Artery Disease of the {{European Society}} of {{Cardiology}}},
  shorttitle = {2013 {{ESC}} Guidelines on the Management of Stable Coronary Artery Disease},
  author = {Montalescot, Gilles and Sechtem, Udo and Achenbach, Stephan and Andreotti, Felicita and Arden, Chris and Budaj, Andrzej and Bugiardini, Raffaele and Crea, Filippo and Cuisset, Thomas and Di Mario, Carlo and Ferreira, J. Rafael and Gersh, Bernard J. and Gitt, Anselm K. and Hulot, Jean-Sebastien and Marx, Nikolaus and Opie, Lionel H. and Pfisterer, Matthias and Prescott, Eva and Ruschitzka, Frank and Sabat{\'e}, Manel and Senior, Roxy and Taggart, David Paul and {van der Wall}, Ernst E. and Vrints, Christiaan J. M. and {ESC Committee for Practice Guidelines} and Zamorano, Jose Luis and Achenbach, Stephan and Baumgartner, Helmut and Bax, Jeroen J. and Bueno, H{\'e}ctor and Dean, Veronica and Deaton, Christi and Erol, Cetin and Fagard, Robert and Ferrari, Roberto and Hasdai, David and Hoes, Arno W. and Kirchhof, Paulus and Knuuti, Juhani and Kolh, Philippe and Lancellotti, Patrizio and Linhart, Ales and Nihoyannopoulos, Petros and Piepoli, Massimo F. and Ponikowski, Piotr and Sirnes, Per Anton and Tamargo, Juan Luis and Tendera, Michal and Torbicki, Adam and Wijns, William and Windecker, Stephan and {Document Reviewers} and Knuuti, Juhani and Valgimigli, Marco and Bueno, H{\'e}ctor and Claeys, Marc J. and {Donner-Banzhoff}, Norbert and Erol, Cetin and Frank, Herbert and {Funck-Brentano}, Christian and Gaemperli, Oliver and {Gonzalez-Juanatey}, Jos{\'e} R. and Hamilos, Michalis and Hasdai, David and Husted, Steen and James, Stefan K. and Kervinen, Kari and Kolh, Philippe and Kristensen, Steen Dalby and Lancellotti, Patrizio and Maggioni, Aldo Pietro and Piepoli, Massimo F. and Pries, Axel R. and Romeo, Francesco and Ryd{\'e}n, Lars and Simoons, Maarten L. and Sirnes, Per Anton and Steg, Ph Gabriel and Timmis, Adam and Wijns, William and Windecker, Stephan and Yildirir, Aylin and Zamorano, Jose Luis},
  year = {2013},
  month = oct,
  journal = {Eur Heart J},
  volume = {34},
  number = {38},
  pages = {2949--3003},
  issn = {1522-9645},
  doi = {10.1093/eurheartj/eht296},
  langid = {english},
  pmid = {23996286},
  keywords = {Aged,Angina pectoris,Angina Stable,anti-ischaemic drugs,Cardiac Imaging Techniques,Cardiotonic Agents,Coronary Artery Bypass,Coronary Artery Disease,Coronary revascularization,Electrocardiography,Evidence-Based Medicine,Female,Guidelines,Humans,Male,Myocardial ischaemia,Myocardial Revascularization,Percutaneous Coronary Intervention,Primary Health Care,Prognosis,Risk Assessment,Risk factors,Risk Reduction Behavior,Stable coronary artery disease},
  file = {C:\Users\habib.rebbah\Zotero\storage\75QUCWBG\Task Force Members et al. - 2013 - 2013 ESC guidelines on the management of stable co.pdf}
}

@article{nagelMagneticResonancePerfusion2019,
  title = {Magnetic {{Resonance Perfusion}} or {{Fractional Flow Reserve}} in {{Coronary Disease}}},
  author = {Nagel, Eike and Greenwood, John P. and McCann, Gerry P. and Bettencourt, Nuno and Shah, Ajay M. and Hussain, Shazia T. and Perera, Divaka and Plein, Sven and {Bucciarelli-Ducci}, Chiara and Paul, Matthias and Westwood, Mark A. and Marber, Michael and Richter, Wolf-Stefan and Puntmann, Valentina O. and Schwenke, Carsten and {Schulz-Menger}, Jeanette and Das, Rajiv and Wong, Joyce and Hausenloy, Derek J. and Steen, Henning and Berry, Colin and {MR-INFORM Investigators}},
  year = {2019},
  month = jun,
  journal = {N Engl J Med},
  volume = {380},
  number = {25},
  pages = {2418--2428},
  issn = {1533-4406},
  doi = {10.1056/NEJMoa1716734},
  abstract = {BACKGROUND: In patients with stable angina, two strategies are often used to guide revascularization: one involves myocardial-perfusion cardiovascular magnetic resonance imaging (MRI), and the other involves invasive angiography and measurement of fractional flow reserve (FFR). Whether a cardiovascular MRI-based strategy is noninferior to an FFR-based strategy with respect to major adverse cardiac events has not been established. METHODS: We performed an unblinded, multicenter, clinical-effectiveness trial by randomly assigning 918 patients with typical angina and either two or more cardiovascular risk factors or a positive exercise treadmill test to a cardiovascular MRI-based strategy or an FFR-based strategy. Revascularization was recommended for patients in the cardiovascular-MRI group with ischemia in at least 6\% of the myocardium or in the FFR group with an FFR of 0.8 or less. The composite primary outcome was death, nonfatal myocardial infarction, or target-vessel revascularization within 1 year. The noninferiority margin was a risk difference of 6 percentage points. RESULTS: A total of 184 of 454 patients (40.5\%) in the cardiovascular-MRI group and 213 of 464 patients (45.9\%) in the FFR group met criteria to recommend revascularization (P\,=\,0.11). Fewer patients in the cardiovascular-MRI group than in the FFR group underwent index revascularization (162 [35.7\%] vs. 209 [45.0\%], P\,=\,0.005). The primary outcome occurred in 15 of 421 patients (3.6\%) in the cardiovascular-MRI group and 16 of 430 patients (3.7\%) in the FFR group (risk difference, -0.2 percentage points; 95\% confidence interval, -2.7 to 2.4), findings that met the noninferiority threshold. The percentage of patients free from angina at 12 months did not differ significantly between the two groups (49.2\% in the cardiovascular-MRI group and 43.8\% in the FFR group, P\,=\,0.21). CONCLUSIONS: Among patients with stable angina and risk factors for coronary artery disease, myocardial-perfusion cardiovascular MRI was associated with a lower incidence of coronary revascularization than FFR and was noninferior to FFR with respect to major adverse cardiac events. (Funded by the Guy's and St. Thomas' Biomedical Research Centre of the National Institute for Health Research and others; MR-INFORM ClinicalTrials.gov number, NCT01236807.).},
  langid = {english},
  pmid = {31216398},
  keywords = {Adult,Aged,Angina Stable,Coronary Angiography,Coronary Artery Disease,Female,Fractional Flow Reserve Myocardial,Humans,Magnetic Resonance Angiography,Male,Middle Aged,Risk Factors},
  file = {C:\Users\habib.rebbah\Zotero\storage\NZAT8LU3\Nagel et al. - 2019 - Magnetic Resonance Perfusion or Fractional Flow Re.pdf}
}

@article{parkerExperimentallyderivedFunctionalForm2006a,
  title = {Experimentally-Derived Functional Form for a Population-Averaged High-Temporal-Resolution Arterial Input Function for Dynamic Contrast-Enhanced {{MRI}}},
  author = {Parker, Geoff J.M. and Roberts, Caleb and Macdonald, Andrew and Buonaccorsi, Giovanni A. and Cheung, Sue and Buckley, David L. and Jackson, Alan and Watson, Yvonne and Davies, Karen and Jayson, Gordon C.},
  year = {2006},
  journal = {Magnetic Resonance in Medicine},
  volume = {56},
  number = {5},
  pages = {993--1000},
  issn = {1522-2594},
  doi = {10.1002/mrm.21066},
  urldate = {2024-01-17},
  abstract = {Rapid T1-weighted 3D spoiled gradient-echo (GRE) data sets were acquired in the abdomen of 23 cancer patients during a total of 113 separate visits to allow dynamic contrast-enhanced MRI (DCE-MRI) analysis of tumor microvasculature. The arterial input function (AIF) was measured in each patient at each visit using an automated AIF extraction method following a standardized bolus administration of gadodiamide. The AIFs for each patient were combined to obtain a mean AIF that is representative for any individual. The functional form of this general AIF may be useful for studies in which AIF measurements are not possible. Improvements in the reproducibility of DCE-MRI model parameters (Ktrans, ve, and vp) were observed when this new, high-temporal-resolution population AIF was used, indicating the potential for increased sensitivity to therapy-induced change. Magn Reson Med, 2006. {\copyright} 2006 Wiley-Liss, Inc.},
  copyright = {Copyright {\copyright} 2006 Wiley-Liss, Inc.},
  langid = {english},
  keywords = {arterial input function,cancer,dynamic contrast-enhanced (DCE)-MRI,tracer kinetic modeling,vasculature},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\FKTAGMBR\\Parker et al. - 2006 - Experimentally-derived functional form for a popul.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\WPBSYDB5\\mrm.html}
}

@phdthesis{rebbahQuantificationOptimisationImagerie2019,
  type = {These de Doctorat},
  title = {Quantification et Optimisation de l'imagerie Par R{\'e}sonnance Magn{\'e}tique de La Perfusion Cardiaque},
  author = {Rebbah, Habib},
  year = {2019},
  month = may,
  urldate = {2024-02-07},
  abstract = {Les maladies cardiovasculaires sont aujourd'hui la premi{\`e}re cause de mortalit{\'e} chez les femmes et la seconde chez les hommes en France selon les chiffres du minist{\`e}re de la sant{\'e} de 2012 et la premi{\`e}re cause d{\'e}c{\`e}s dans le monde en 2017 selon l'organisation mondiale de la sant{\'e}. Les maladies coronariennes en sont les principales repr{\'e}sentant. Elles causent entre autres des infarctus du myocarde. L'{\'e}tude de la perfusion des organes cibl{\'e}s est donc hautement recommand{\'e}e pour y d{\'e}celer des d{\'e}fauts de vascularisation. L'IRM cardiaque est une m{\'e}thode aux faibles effets secondaires qui progresse dans le diagnostic des infarctus et de la perfusion myocardique en comparaison des autres techniques d'imagerie. Elle regroupe un vaste r{\'e}pertoire de s{\'e}quences souvent combin{\'e}es entre elles permettant l'estimation de diff{\'e}rents param{\`e}tres : perfusion de premier passage, rehaussement tardif, T1, T2, etc. Les s{\'e}quences utilis{\'e}es souffrent toutefois de nombreux artefacts limitant la quantification des param{\`e}tres pharmacocin{\'e}tiques. Durant ce travail de th{\`e}se, nous avons d{\'e}velopp{\'e} de nouveaux outils de quantification du T1 permettant {\`a} la fois une meilleure estimation de ce dernier tout en r{\'e}duisant le temps d'examen. Nous avons par ailleurs mis en place des mod{\`e}les de conversion de la concentration de produit de contraste inject{\'e} en signal observ{\'e} sur les images d'IRM permettant une plus grande pr{\'e}cision de mesure. Enfin, nous avons test{\'e}s de nouvelles approches de quantification de la perfusion du premier passage permettant de r{\'e}duire l'effet d'artefact observ{\'e}s. L'am{\'e}lioration propos{\'e}e pour la quantification du T1 est faite en mod{\'e}lisant l'effet de r{\'e}cup{\'e}ration partielle de la magn{\'e}tisation longitudinale entre deux Look-Locker successives pour la s{\'e}quence MOLLI. L'ajout de nouveaux param{\`e}tres {\`a} estimer permet donc de s'affranchir du temps de pause et donc de r{\'e}duire la dur{\'e}e de l'examen au prix d'une l{\'e}g{\`e}re baisse de sensibilit{\'e}. Les tests sur simulations, fant{\^o}mes et en in vivo ont permis de valider la reproductibilit{\'e} des r{\'e}sultats obtenus, permettant d'avancer vers une quantification r{\'e}elle du T1 et donc du volume extracellulaire. Les travaux sur les signaux de perfusion ont {\'e}t{\'e} r{\'e}alis{\'e}s en supposant une saturation imparfaite ne conduisant pas {\`a} une annulation compl{\`e}te de la magn{\'e}tisation longitudinale. De plus un mod{\`e}le semi-quantitatif de la baisse du signal observ{\'e}e {\`a} l'arriv{\'e} du bolus dans les deux cavit{\'e}s cardiaques a {\'e}t{\'e} mis en place pour corriger en partie ce ph{\'e}nom{\`e}ne. Des mod{\`e}les d'estimation de la partie constante des signaux tissulaires et des signaux art{\'e}riels sont propos{\'e}s et valid{\'e}s en simulation, l'un bas{\'e} sur la d{\'e}tection d'arriv{\'e}e du bolus par calcul de distance de Wasserstein entre images temporelles et l'autre sur l'ajout de param{\`e}tres suppl{\'e}mentaires aux mod{\`e}les de d{\'e}convolution. Afin de s'affranchir des erreurs de conversion signal-concentration, nous avons test{\'e} des approches de d{\'e}convolution aveugle. L'absence de fant{\^o}me et de donn{\'e}es in vivo de r{\'e}f{\'e}rences nous a restreint {\`a} une validation en simulation. Enfin, la th{\`e}se a permis de d{\'e}velopper des algorithmes de classification morphologique utilisant la d{\'e}tection de variation de signal par distance de Wessertein et de segmentation fonctionnelle d'images cardiaques en coupe petit axe utilisant un algorithme de segmentation en super-pixels.},
  collaborator = {Mousseaux, Elie and Cuenod, Charles-Andr{\'e}},
  copyright = {Licence Etalab},
  school = {Sorbonne Paris Cit{\'e}},
  keywords = {616.120 7548,Analyse de regression,Blind deconvolution,Cardiac MRI,Coeur -- Imagerie par resonance magnetique,Deconvolution aveugle,Distance de Wasserstein,IRM cardiaque,IRM de perfusion,Nonlinear regression,Perfusion MRI,Quantification du T1,Regression non lineaire,T1 quantification,Wasserstein distance}
}

@article{scannellAIAIFArtificialIntelligencebased2023,
  title = {{{AI-AIF}}: Artificial Intelligence-Based Arterial Input Function for Quantitative Stress Perfusion Cardiac Magnetic Resonance},
  shorttitle = {{{AI-AIF}}},
  author = {Scannell, Cian M and Alskaf, Ebraham and Sharrack, Noor and Razavi, Reza and Ourselin, Sebastien and Young, Alistair A and Plein, Sven and Chiribiri, Amedeo},
  year = {2023},
  month = jan,
  journal = {European Heart Journal - Digital Health},
  volume = {4},
  number = {1},
  pages = {12--21},
  issn = {2634-3916},
  doi = {10.1093/ehjdh/ztac074},
  urldate = {2024-01-15},
  abstract = {One of the major challenges in the quantification of myocardial blood flow (MBF) from stress perfusion cardiac magnetic resonance (CMR) is the estimation of the arterial input function (AIF). This is due to the non-linear relationship between the concentration of gadolinium and the MR signal, which leads to signal saturation. In this work, we show that a deep learning model can be trained to predict the unsaturated AIF from standard images, using the reference dual-sequence acquisition AIFs (DS-AIFs) for training.A 1D U-Net was trained, to take the saturated AIF from the standard images as input and predict the unsaturated AIF, using the data from 201 patients from centre 1 and a test set comprised of both an independent cohort of consecutive patients from centre 1 and an external cohort of patients from centre 2 (n = 44). Fully-automated MBF was compared between the DS-AIF and AI-AIF methods using the Mann{\textendash}Whitney U test and Bland{\textendash}Altman analysis. There was no statistical difference between the MBF quantified with the DS-AIF [2.77 mL/min/g (1.08)] and predicted with the AI-AIF (2.79 mL/min/g (1.08), P = 0.33. Bland{\textendash}Altman analysis shows minimal bias between the DS-AIF and AI-AIF methods for quantitative MBF (bias of -0.11 mL/min/g). Additionally, the MBF diagnosis classification of the AI-AIF matched the DS-AIF in 669/704 (95\%) of myocardial segments.Quantification of stress perfusion CMR is feasible with a single-sequence acquisition and a single contrast injection using an AI-based correction of the AIF.},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\4CATR22E\\Scannell et al. - 2023 - AI-AIF artificial intelligence-based arterial inp.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\SNBAYZQF\\6880440.html}
}

@article{schabelModelConstrainedMonteCarlo2010b,
  title = {A {{Model-Constrained Monte Carlo Method}} for {{Blind Arterial Input Function Estimation}} in {{Dynamic Contrast-Enhanced MRI}}: {{I}}) {{Simulations}}},
  shorttitle = {A {{Model-Constrained Monte Carlo Method}} for {{Blind Arterial Input Function Estimation}} in {{Dynamic Contrast-Enhanced MRI}}},
  author = {Schabel, Matthias C. and Fluckiger, Jacob U. and DiBella, Edward V. R.},
  year = {2010},
  month = aug,
  journal = {Phys Med Biol},
  volume = {55},
  number = {16},
  pages = {4783--4806},
  issn = {0031-9155},
  doi = {10.1088/0031-9155/55/16/011},
  urldate = {2024-02-08},
  abstract = {Widespread adoption of quantitative pharmacokinetic modeling methods in conjunction with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has led to increased recognition of the importance of obtaining accurate patient-specific arterial input function (AIF) measurements. Ideally, DCE-MRI studies use an AIF directly measured in an artery local to the tissue of interest, along with measured tissue concentration curves, to quantitatively determine pharmacokinetic parameters. However, the numerous technical and practical difficulties associated with AIF measurement have made the use of population-averaged AIF data a popular, if suboptimal, alternative to AIF measurement. In this work, we present and characterize a new algorithm for determining the AIF solely from the measured tissue concentration curves. This Monte Carlo Blind Estimation (MCBE) algorithm estimates the AIF from subsets of D concentration-time curves drawn from a larger pool of M candidate curves via nonlinear optimization, doing so for multiple (Q) subsets and statistically averaging these repeated estimates. The MCBE algorithm can be viewed as a generalization of previously published methods that employ clustering of concentration-time curves and only estimate the AIF once. Extensive computer simulations were performed over physiologically- and experimentally-realistic ranges of imaging and tissue parameters, and the impact of choosing different values of D and Q was investigated. We found the algorithm to be robust, computationally-efficient, and capable of accurately estimating the AIF even for relatively high noise levels, long sampling intervals, and low diversity of tissue curves. With the incorporation of boostrapping initialization, we further demonstrated the ability to blindly estimate AIFs that deviate substantially in shape from the population-averaged initial guess. Pharmacokinetic parameter estimates for Ktrans, kep, vp, and ve all showed relative biases and uncertainties of less than 10\% for measurements having a temporal sampling rate of 4 seconds and a concentration measurement noise level of {$\sigma$} = 0.04 mM. A companion paper discusses the application of the MCBE algorithm to DCE-MRI data acquired in eight patients with malignant brain tumors.},
  pmcid = {PMC3533367},
  pmid = {20679691},
  file = {C:\Users\habib.rebbah\Zotero\storage\FWAUWA6Z\Schabel et al. - 2010 - A Model-Constrained Monte Carlo Method for Blind A.pdf}
}

@inproceedings{schmidAttenuationResilientAIF2007a,
  title = {Attenuation {{Resilient AIF Estimation Based}} on {{Hierarchical Bayesian Modelling}} for {{First Pass Myocardial Perfusion MRI}}},
  booktitle = {Medical {{Image Computing}} and {{Computer-Assisted Intervention}} {\textendash} {{MICCAI}} 2007},
  author = {Schmid, Volker J. and Gatehouse, Peter D. and Yang, Guang-Zhong},
  editor = {Ayache, Nicholas and Ourselin, S{\'e}bastien and Maeder, Anthony},
  year = {2007},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  pages = {393--400},
  publisher = {{Springer}},
  address = {{Berlin, Heidelberg}},
  doi = {10.1007/978-3-540-75757-3_48},
  abstract = {Non-linear attenuation of the Arterial Input Function (AIF) is a major problem in first-pass MR perfusion imaging due to the high concentration of the contrast agent in the blood pool. This paper presents a technique to reconstruct the true AIF using signal intensities in the myocardium and the attenuated AIF based on a Hierarchical Bayesian Model (HBM). With the proposed method, both the AIF and the response function are modeled as smoothed functions by using Bayesian penalty splines (P-Splines). The derived AIF is then used to estimate the impulse response of the myocardium based on deconvolution analysis. The proposed technique is validated both with simulated data using the MMID4 model and ten in vivo data sets for estimating myocardial perfusion reserve rates. The results demonstrate the ability of the proposed technique in accurately reconstructing the desired AIF for myocardial perfusion quantification. The method does not involve any MRI pulse sequence modification, and thus is expected to have wider clinical impact.},
  isbn = {978-3-540-75757-3},
  langid = {english},
  keywords = {Arterial Input Function,Cardiovascular Magnetic Resonance,Hierarchical Bayesian Model,Myocardial Perfusion,Myocardial Perfusion Reserve},
  file = {C:\Users\habib.rebbah\Zotero\storage\JLUGDWFA\Schmid et al. - 2007 - Attenuation Resilient AIF Estimation Based on Hier.pdf}
}

@article{slavinFirstPassMyocardialPerfusion2001a,
  title = {First-{{Pass Myocardial Perfusion MR Imaging}} with {{Interleaved Notched Saturation}}: {{Feasibility Study}}},
  shorttitle = {First-{{Pass Myocardial Perfusion MR Imaging}} with {{Interleaved Notched Saturation}}},
  author = {Slavin, Glenn S. and Wolff, Steven D. and Gupta, Sandeep N. and Foo, Thomas K. F.},
  year = {2001},
  month = apr,
  journal = {Radiology},
  volume = {219},
  number = {1},
  pages = {258--263},
  publisher = {{Radiological Society of North America}},
  issn = {0033-8419},
  doi = {10.1148/radiology.219.1.r01mr35258},
  urldate = {2024-01-17},
  abstract = {The authors evaluated a magnetization preparation scheme with a ``notched'' section profile for T1-weighted first-pass myocardial perfusion magnetic resonance (MR) imaging at 1.5 T. The pulse sequence consisted of a preparation sequence followed by an interleaved gradient-echo echo-planar sequence. Image contrast was evaluated in a feasibility study in 12 adult patients. The notched saturation pulse allowed long magnetization recovery times without sacrificing section coverage. Image contrast between normal and ischemic myocardium was excellent.}
}

@article{sourbronTracerKineticModelling2011,
  title = {Tracer Kinetic Modelling in {{MRI}}: Estimating Perfusion and Capillary Permeability},
  shorttitle = {Tracer Kinetic Modelling in {{MRI}}},
  author = {Sourbron, S. P. and Buckley, D. L.},
  year = {2011},
  month = dec,
  journal = {Phys. Med. Biol.},
  volume = {57},
  number = {2},
  pages = {R1},
  publisher = {{IOP Publishing}},
  issn = {0031-9155},
  doi = {10.1088/0031-9155/57/2/R1},
  urldate = {2024-01-17},
  abstract = {The tracer-kinetic models developed in the early 1990s for dynamic contrast-enhanced MRI (DCE-MRI) have since become a standard in numerous applications. At the same time, the development of MRI hardware has led to increases in image quality and temporal resolution that reveal the limitations of the early models. This in turn has stimulated an interest in the development and application of a second generation of modelling approaches. They are designed to overcome these limitations and produce additional and more accurate information on tissue status. In particular, models of the second generation enable separate estimates of perfusion and capillary permeability rather than a single parameter Ktrans that represents a combination of the two. A variety of such models has been proposed in the literature, and development in the field has been constrained by a lack of transparency regarding terminology, notations and physiological assumptions. In this review, we provide an overview of these models in a manner that is both physically intuitive and mathematically rigourous. All are derived from common first principles, using concepts and notations from general tracer-kinetic theory. Explicit links to their historical origins are included to allow for a transfer of experience obtained in other fields (PET, SPECT, CT). A classification is presented that reveals the links between all models, and with the models of the first generation. Detailed formulae for all solutions are provided to facilitate implementation. Our aim is to encourage the application of these tools to DCE-MRI by offering researchers a clearer understanding of their assumptions and requirements.},
  langid = {english}
}

@article{toftsEstimatingKineticParameters1999a,
  title = {Estimating Kinetic Parameters from Dynamic Contrast-Enhanced T1-Weighted {{MRI}} of a Diffusable Tracer: {{Standardized}} Quantities and Symbols},
  shorttitle = {Estimating Kinetic Parameters from Dynamic Contrast-Enhanced T1-Weighted {{MRI}} of a Diffusable Tracer},
  author = {Tofts, Paul S. and Brix, Gunnar and Buckley, David L. and Evelhoch, Jeffrey L. and Henderson, Elizabeth and Knopp, Michael V. and Larsson, Henrik B.W. and Lee, Ting-Yim and Mayr, Nina A. and Parker, Geoffrey J.M. and Port, Ruediger E. and Taylor, June and Weisskoff, Robert M.},
  year = {1999},
  journal = {Journal of Magnetic Resonance Imaging},
  volume = {10},
  number = {3},
  pages = {223--232},
  issn = {1522-2586},
  doi = {10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S},
  urldate = {2024-01-18},
  abstract = {We describe a standard set of quantity names and symbols related to the estimation of kinetic parameters from dynamic contrast-enhanced T1-weighted magnetic resonance imaging data, using diffusable agents such as gadopentetate dimeglumine (Gd-DTPA). These include a) the volume transfer constant Ktrans (min-1); b) the volume of extravascular extracellular space (EES) per unit volume of tissue ve (0 {$<$} ve {$<$} 1); and c) the flux rate constant between EES and plasma kep (min-1). The rate constant is the ratio of the transfer constant to the EES (kep = Ktrans/ve). Under flow-limited conditions Ktrans equals the blood plasma flow per unit volume of tissue; under permeability-limited conditions Ktrans equals the permeability surface area product per unit volume of tissue. We relate these quantities to previously published work from our groups; our future publications will refer to these standardized terms, and we propose that these be adopted as international standards. J. Magn. Reson. Imaging 10:223{\textendash}232, 1999. {\copyright} 1999 Wiley-Liss, Inc.},
  copyright = {Copyright {\copyright} 1999 Wiley-Liss, Inc.},
  langid = {english},
  keywords = {extravascular extracellular space,Gd-DTPA,perfusion,permeability,rate constant,transfer constant},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\V7DQJHDE\\Tofts et al. - 1999 - Estimating kinetic parameters from dynamic contras.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\3DLQKAJP\\(SICI)1522-2586(199909)103223AID-JMRI23.0.html}
}

@article{toftsMeasurementBloodbrainBarrier1991,
  title = {Measurement of the Blood-Brain Barrier Permeability and Leakage Space Using Dynamic {{MR}} Imaging. 1. {{Fundamental}} Concepts},
  author = {Tofts, Paul S. and Kermode, Allan G.},
  year = {1991},
  journal = {Magnetic Resonance in Medicine},
  volume = {17},
  number = {2},
  pages = {357--367},
  issn = {1522-2594},
  doi = {10.1002/mrm.1910170208},
  urldate = {2024-01-18},
  abstract = {Leakage of Gd-DTPA through a defective blood-brain barrier is measured quantitatively using dynamic MRI scanning, in which repeated scans are made after a bolus injection. Image registration artifacts are minimized; a dose of 0.1 mM/kg and an IR sequence enable enhancement to be measured quantitatively. The triexponential enhancement curve is fitted to a theoretical model based on compartmental analysis. The transfer constant, or permeability surface area product per unit volume of tissue (k), and leakage space per unit volume of tissue (y) are measured. Estimates for a quickly enhancing multiple sclerosis lesion are k = 0.050 min-1, v1 = 21\%; for a slow one k = 0.013 min-1, v1 = 49\%. This implies permeability in the range 4-17 {\texttimes} 10-6 cm s-1, in broad agreement with other physiological methods. The method is noninvasive and can be used to make serial measurements in patients and in experimental animal models. The time course of pathological aspects of diseases with blood-brain barrier breakdown, such as multiple sclerosis, tumors, and infections (e.g., HIV) can be studied, along with their response to therapy. The measurements are of physiological variables and are therefore independent of imaging equipment and field. {\copyright} 1991 Academic Press, Inc.},
  copyright = {Copyright {\copyright} 1991 Wiley-Liss, Inc., A Wiley Company},
  langid = {english},
  file = {C:\Users\habib.rebbah\Zotero\storage\AD3DJFBH\mrm.html}
}

@article{vanhertenPhysicsinformedNeuralNetworks2022a,
  title = {Physics-Informed Neural Networks for Myocardial Perfusion {{MRI}} Quantification},
  author = {{van Herten}, Rudolf L. M. and Chiribiri, Amedeo and Breeuwer, Marcel and Veta, Mitko and Scannell, Cian M.},
  year = {2022},
  month = may,
  journal = {Medical Image Analysis},
  volume = {78},
  pages = {102399},
  issn = {1361-8415},
  doi = {10.1016/j.media.2022.102399},
  urldate = {2024-01-15},
  abstract = {Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.},
  keywords = {Myocardial perfusion MRI,Physics informed neural networks,Small data,Tracer-kinetic modelling},
  file = {C\:\\Users\\habib.rebbah\\Zotero\\storage\\F33LQDG7\\van Herten et al. - 2022 - Physics-informed neural networks for myocardial pe.pdf;C\:\\Users\\habib.rebbah\\Zotero\\storage\\EDTIECZS\\S1361841522000512.html}
}
