Physics:Arterial input function

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Arterial input function (AIF), also known as a plasma input function, refers to the concentration of tracer in blood-plasma in an artery measured over time. The oldest record on PubMed shows that AIF was used by Harvey et al.[1] in 1962 to measure the exchange of materials between red blood cells and blood plasma, and by other researchers in 1983 for positron emission tomography (PET) studies.[2][3] Nowadays, kinetic analysis is performed in various medical imaging techniques, which requires an AIF as one of the inputs to the mathematical model, for example, in dynamic PET imaging,[4] or perfusion CT,[5] or dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).[6][7]

How is AIF obtained

An example of an arterial input function showing the concentration of tracer in blood plasma over time.

AIF can be obtained in several different ways, for example, using the invasive method of continuous arterial sampling using an online blood monitor,[8] using the invasive method of arterial blood samples obtained at discrete time points post-injection,[4] using a minimally invasive method using a population-based AIF where an input function in a subject is estimated partly from the prior information obtained from a previous population and partly from the blood information from the subject itself obtained at the time of scanning,[9] or using an image-derived arterial input function (IDAIF) obtained by placing a region of interest (ROI) over an artery and calibrating the resulting curves against venous blood samples obtained during the later phases (30 to 60 minutes) of the dynamic scan[10] when venous and arterial tracer concentrations become equal.[4]


These four methods are briefly described as follows:

Continuous arterial sampling

Continuous arterial blood sampling is invasive, painful, and uncomfortable for the patients. Continuous arterial sampling was obtained in postmenopausal women imaged using [18F]NaF for bone studies.[11]

Discrete arterial sampling

Discrete arterial blood sampling is invasive, painful, and uncomfortable for the patients. Cook et al. measured discrete blood samples and compared them to continuous arterial sampling in postmenopausal women imaged using [18F]NaF for bone studies.[11] Another study in head and neck cancer patients imaged using [18F]FLT PET, and numerous other studies, obtained discrete arterial samples for the estimation of arterial input function.[12]


Population-based method

These plots show the average arterial plasma concentration residual curve at 1-second intervals, derived from direct arterial sampling in nine postmenopausal women and normalized to an injected activity of 100 MBq. (a) The first graph presents the residual curve from 0 to 60 seconds, (b) The second graph displays the curve from 60 to 1800 seconds. The black dots represent the mean measured values across the nine participants, while the red line indicates the fitted curve (based on the Equation below). To standardize the data, the timing of each individual curve was adjusted so that the peak tracer concentration aligned exactly 30 seconds post-injection.[13]

A population-based input function generally relies on the dataset previously obtained by other researchers in a specific set of populations, and average values are used. The methods generally provide better results if a large number of datasets is used and based on the assumption that the input function in a new patient in this sub-group of the population will be insignificantly different from the population average values. In a neuroinflammation study, the author using a population-based input function in healthy volunteers and liver-transplanted patients imaged using [18F]GE-180 PET.[14] In another study, healthy controls and patients with Parkinson's and Alzheimer's disease were imaged using [18F]FEPPA PET.[15] Zanotti-Fregonara et al.[16] thoroughly reviewed the literature on the arterial input function used for brain PET imaging and suggested the possibility of population-based arterial input functions as a potential alternative to invasive arterial sampling. However, Blake et al.[17] derived a semi-population based method from healthy postmenopausal women imaged using [18F]NaF for bone studies[17] based on the observation that the later part of the arterial input function can be constructed from the venous blood samples, as the venous and arterial blood concentration of tracer is equal 30 minutes after the injection. They derived the peak of the curve from a previous study that obtained continuous arterial sampling, and the later part of the curve from the venous blood samples of the individual patient in whom an AIF is to be estimated. When combined, a semi-population based arterial input function is obtained as a result. A mathematical function that accurately reproduces this curve in units of kBq/ml in three separate time intervals is presented:

A mathematical function that accurately reproduces this curve in units of kBq/ml in three separate time intervals is presented





Semi-population model explanation: For a typical injected dose of 100 MBq, the area under the plasma concentration curve estimated using the model closely matches that from the original measured data, showing only a minor difference. To construct a one-hour arterial input function (AIF) for an individual, a standardized early-phase residual curve (covering 0–30 minutes) is scaled to the person's injected activity and combined with an exponential curve fitted to venous plasma samples taken between 30 and 60 minutes after injection.

The late-phase exponential typically contributes around 75–80% of the total AIF area over 60 minutes. This means the method captures most of the individual variation necessary to estimate tracer uptake (Ki), which is especially useful in treatment studies like those involving osteoporosis. The method assumes that treatment effects mainly alter the slower, late-phase component and have minimal impact on the early-phase peak related to rapid tracer delivery. As a result, Ki values derived using this method may differ slightly from those obtained via direct arterial sampling.

However, the model used to generate this input function was developed from a small, specific population and may not apply to all groups. It also isn't suitable for estimating bone blood flow (K1), as it uses an average early-phase response that might not reflect individual tracer kinetics immediately after injection. Accurate K1 estimation requires either arterial sampling or an image-derived method that captures arterial tracer levels within the first few minutes, corrected for imaging effects.

A major advantage of this approach is that it enables Ki measurements at multiple skeletal sites using short static scans taken during a single session, without the need for continuous dynamic scanning. Compared with other input function methods, this approach provides more precise Ki estimates with lower measurement errors.

Alternative implementations have used heart imaging instead of blood sampling to calibrate the residual function. Some versions multiply the terminal exponential rather than add to it, which may better adjust for differences in body size. In contrast, the additive approach might be more suitable when treatment or disease significantly affects the late-phase kinetics. Studies comparing these methods have shown that both produce consistent Ki values, and combining this approach with static scanning can substantially reduce overall imaging time.

Image-derived method

An image-derived arterial input function (IDAIF) obtained by measuring the tracer counts over the aorta,[4] carodit artery,[18] or radial artery[8] offers an alternative to invasive arterial blood sampling. An IDAIF at the aorta can be determined by measuring the tracer counts over the left ventricle, ascending aorta, and abdominal aorta and this has been previously validated by various researchers.[10][4]

The arterial time-activity curve (TAC) from the image data requires corrections for metabolites formed over time, differences between whole blood and plasma activity, which are not constant over time, correction for partial volume errors (PVE) due to the small size of the ROI, spill-over errors due to activity from neighbouring tissues outside the ROI,[19] error due to patient movement, and noise introduced due to the limited number of counts acquired in each image time frame because of the short time frames. These errors are corrected using late venous blood samples,[4][10] and the resulting curve is called an arterial input function (AIF). There are numerous methods tried by researchers over the years.[20][21][22][23]

See also

References

  1. ↑ HARVEY, RB (1962). "Renal extraction of para-aminohippurate and creatinine measured by continuous in vivo sampling of arterial and renal-vein blood.". Ann N Y Acad Sci 102 (1): 46–54. doi:10.1111/j.1749-6632.1962.tb13624.x. PMID 13960801. Bibcode: 1962NYASA.102...46H. 
  2. ↑ Herscovitch, P (1983). "Brain blood flow measured with intravenous H2(15)O. I. Theory and error analysis". J. Nucl. Med. 24 (9): 782–9. PMID 6604139. 
  3. ↑ Henze, E.; Huang, S. C.; Ratib, O.; Hoffman, E.; Phelps, M. E.; Schelbert, H. R. (1983). "Measurements of regional tissue and blood-pool radiotracer concentrations from serial tomographic images of the heart.". J Nucl Med 24 (11): 987–96. PMID 6605418. 
  4. ↑ 4.0 4.1 4.2 4.3 4.4 4.5 Cook, Gary J. R.; Lodge, Martin A.; Marsden, Paul K.; Dynes, Angela; Fogelman, Ignac (1999). "Non-invasive assessment of skeletal kinetics using fluorine-18 fluoride positron emission tomography: evaluation of image and population-derived arterial input functions". European Journal of Nuclear Medicine and Molecular Imaging 26 (11): 1424–1429. doi:10.1007/s002590050474. ISSN 1619-7070. PMID 10552083. 
  5. ↑ Lui, Y.W.; Tang, E.R.; Allmendinger, A.M.; Spektor, V. (2010). "Evaluation of CT Perfusion in the Setting of Cerebral Ischemia: Patterns and Pitfalls". American Journal of Neuroradiology 31 (9): 1552–1563. doi:10.3174/ajnr.a2026. ISSN 0195-6108. PMID 20190208. 
  6. ↑ Schabel, Matthias C. (2012-01-31). "A unified impulse response model for DCE-MRI". Magnetic Resonance in Medicine 68 (5): 1632–1646. doi:10.1002/mrm.24162. ISSN 0740-3194. PMID 22294448. 
  7. ↑ Tanuj Puri, Sarah Wiscombe, Sally Marshall, John Simpson, Josephine Naish, Pete Thelwall. Changes in pulmonary vascular properties in a human model of acute lung injury measured using DCE-MRI, In 20th Annual Scientific Meeting of the British Chapter of International Society for Magnetic Resonance in Medicine (ISMRM), Edinburgh, UK, September 2014
  8. ↑ 8.0 8.1 Marques, Tiago Reis; Ashok, Abhishekh H.; Angelescu, Ilinca; Borgan, Faith; Myers, Jim; Lingford-Hughes, Anne; Nutt, David J.; Veronese, Mattia et al. (2020-04-15). "GABA-A receptor differences in schizophrenia: a positron emission tomography study using [11CRo154513"]. Molecular Psychiatry 26 (6): 2616–2625. doi:10.1038/s41380-020-0711-y. ISSN 1359-4184. PMID 32296127. 
  9. ↑ Blake, Glen Mervyn; Siddique, Musib; Puri, Tanuj; Frost, Michelle Lorraine; Moore, Amelia Elizabeth; Cook, Gary James R.; Fogelman, Ignac (2012). "A semipopulation input function for quantifying static and dynamic 18F-fluoride PET scans" (in en). Nuclear Medicine Communications 33 (8): 881–888. doi:10.1097/MNM.0b013e3283550275. ISSN 0143-3636. PMID 22617486. 
  10. ↑ 10.0 10.1 10.2 Puri, Tanuj; Blake, Glen M.; Siddique, Musib; Frost, Michelle L.; Cook, Gary J.R.; Marsden, Paul K.; Fogelman, Ignac; Curran, Kathleen M. (2011). "Validation of new image-derived arterial input functions at the aorta using 18F-fluoride positron emission tomography" (in en). Nuclear Medicine Communications 32 (6): 486–495. doi:10.1097/MNM.0b013e3283452918. ISSN 0143-3636. PMID 21386733. 
  11. ↑ 11.0 11.1 Cook, Gary J. R.; Lodge, Martin A.; Blake, Glen M.; Marsden, Paul K.; Fogelman, Ignac (2010-02-18). "Differences in Skeletal Kinetics Between Vertebral and Humeral Bone Measured by 18F-Fluoride Positron Emission Tomography in Postmenopausal Women". Journal of Bone and Mineral Research 15 (4): 763–769. doi:10.1359/jbmr.2000.15.4.763. ISSN 0884-0431. PMID 10780868. 
  12. ↑ Hackett, Sara L; Liu, Dan; Chalkidou, Anastasia; Marsden, Paul; Landau, David; Fenwick, John D (2013). "Estimation of input functions from dynamic [18FFLT PET studies of the head and neck with correction for partial volume effects"] (in en). EJNMMI Research 3 (1): 84. doi:10.1186/2191-219X-3-84. ISSN 2191-219X. PMID 24369816. 
  13. ↑ Puri, Tanuj; Frost, Michelle L.; Moore, Amelia E. B.; Cook, Gary J. R.; Blake, Glen M. (2023). "Input function and modeling for determining bone metabolic flux using [18F sodium fluoride PET imaging: A step-by-step guide"] (in en). Medical Physics 50 (4): 2071–2088. doi:10.1002/mp.16125. ISSN 2473-4209. https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.16125. 
  14. ↑ Buchert, Ralph; Dirks, Meike; Schütze, Christian; Wilke, Florian; Mamach, Martin; Wirries, Ann-Katrin; Pflugrad, Henning; Hamann, Linda et al. (2020-04-23). "Reliable quantification of 18F-GE-180 PET neuroinflammation studies using an individually scaled population-based input function or late tissue-to-blood ratio" (in en). European Journal of Nuclear Medicine and Molecular Imaging 47 (12): 2887–2900. doi:10.1007/s00259-020-04810-1. ISSN 1619-7070. PMID 32322915. 
  15. ↑ Mabrouk, Rostom; Strafella, Antonio P.; Knezevic, Dunja; Ghadery, Christine; Mizrahi, Romina; Gharehgazlou, Avideh; Koshimori, Yuko; Houle, Sylvain et al. (2017-05-17). Garg, Pradeep. ed. "Feasibility study of TSPO quantification with [18FFEPPA using population-based input function"] (in en). PLOS ONE 12 (5). doi:10.1371/journal.pone.0177785. ISSN 1932-6203. PMID 28545084. Bibcode: 2017PLoSO..1277785M. 
  16. ↑ Zanotti-Fregonara, Paolo; Chen, Kewei; Liow, Jeih-San; Fujita, Masahiro; Innis, Robert B (2011-08-03). "Image-Derived Input Function for Brain PET Studies: Many Challenges and Few Opportunities". Journal of Cerebral Blood Flow & Metabolism 31 (10): 1986–1998. doi:10.1038/jcbfm.2011.107. ISSN 0271-678X. PMID 21811289. 
  17. ↑ 17.0 17.1 Blake, Glen Mervyn; Siddique, Musib; Puri, Tanuj; Frost, Michelle Lorraine; Moore, Amelia Elizabeth; Cook, Gary James R.; Fogelman, Ignac (August 2012). "A semipopulation input function for quantifying static and dynamic 18F-fluoride PET scans" (in en). Nuclear Medicine Communications 33 (8): 881–888. doi:10.1097/MNM.0b013e3283550275. ISSN 0143-3636. PMID 22617486. 
  18. ↑ Sari, Hasan; Erlandsson, Kjell; Law, Ian; Larsson, Henrik BW; Ourselin, Sebastien; Arridge, Simon; Atkinson, David; Hutton, Brian F (2017). "Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method" (in en). Journal of Cerebral Blood Flow & Metabolism 37 (4): 1398–1409. doi:10.1177/0271678X16656197. ISSN 0271-678X. PMID 27342321. 
  19. ↑ Nuyts, J (1996). "Three-Dimensional Correction for Spillover and Recovery of Myocardial PET Images". Journal of Nuclear Medicine 37 (5): 767–74. PMID 8965143. 
  20. ↑ van der Weerdt, Arno P.; Boellaard, Ronald; Visser, Frans C.; Lammertsma, Adriaan A. (2007-02-27). "Accuracy of 3D acquisition mode for myocardial FDG PET studies using a BGO-based scanner". European Journal of Nuclear Medicine and Molecular Imaging 34 (9): 1439–1446. doi:10.1007/s00259-007-0367-8. ISSN 1619-7070. PMID 17333179. 
  21. ↑ Puri, Tanuj; Blake, Glen M.; Siddique, Musib; Frost, Michelle L.; Cook, Gary J.R.; Marsden, Paul K.; Fogelman, Ignac; Curran, Kathleen M. (June 2011). "Validation of new image-derived arterial input functions at the aorta using 18F-fluoride positron emission tomography". Nuclear Medicine Communications 32 (6): 486–495. doi:10.1097/mnm.0b013e3283452918. ISSN 0143-3636. PMID 21386733. 
  22. ↑ Lee, Jae-Hoon; Liow, Jeih-San; Paul, Soumen; Morse, Cheryl L.; Haskali, Mohammad B.; Manly, Lester; Shcherbinin, Sergey; Ruble, J. Craig et al. (2020-03-14). "PET quantification of brain O-GlcNAcase with [18FLSN3316612 in healthy human volunteers"]. EJNMMI Research 10 (1): 20. doi:10.1186/s13550-020-0616-4. ISSN 2191-219X. PMID 32172476. 
  23. ↑ Ringheim, Anna; Campos Neto, Guilherme de Carvalho; Anazodo, Udunna; Cui, Lumeng; da Cunha, Marcelo Livorsi; Vitor, Taise; Martins, Karine Minaif; Miranda, Ana Cláudia Camargo et al. (2020-02-24). "Kinetic modeling of 68Ga-PSMA-11 and validation of simplified methods for quantification in primary prostate cancer patients". EJNMMI Research 10 (1): 12. doi:10.1186/s13550-020-0594-6. ISSN 2191-219X. PMID 32140850. 



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