Kinetic modeling of brain FDG data with input function derived from images by independent component analysis

Berradja, Khadidja; Boughanmi, Nabil; Bentourkia, M’hamed

doi:10.1109/nssmic.2009.5401614

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…a different curve shape in some subjects), but were due to problems that would be encountered with any technique that estimates the input function using such a limited number of blood samples. Several methods of PBIF (Vriens et al, 2009; Wakita et al, 2000), IDIF (Berradja et al, 2009; Naganawa et al, 2005), simultaneous estimation of the input function (Jouvie et al, 2011; Ogden et al, 2010), and bolus/constant infusion (Kimura et al, 2012) methods that rely on a single blood sample have been proposed. However, if the assay result for that single blood sample is inaccurate, the whole estimated input function will be wrong.…”

Section: Discussionmentioning

confidence: 99%

Population-based input function and image-derived input function for [11C](R)-rolipram PET imaging: Methodology, validation and application to the study of major depressive disorder

et al. 2012

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Quantitative PET studies of neuroreceptor tracers typically require that arterial input function be measured. The aim of this study was to explore the use of a population-based input function (PBIF) and an image-derived input function (IDIF) for [11C](R)-rolipram kinetic analysis, with the goal of reducing—and possibly eliminating—the number of arterial blood samples needed to measure parent radioligand concentrations. Methods A PBIF was first generated using [11C](R)-rolipram parent time-activity curves from 12 healthy volunteers (Group 1). Both invasive (blood samples) and non-invasive (body weight, body surface area, and lean body mass) scaling methods for PBIF were tested. The scaling method that gave the best estimate of the Logan-VT values was then used to determine the test-retest variability of PBIF in Group 1 and then prospectively applied to another population of 25 healthy subjects (Group 2), as well as to a population of 28 patients with major depressive disorder (Group 3). Results were also compared to those obtained with an image-derived input function (IDIF) from the internal carotid artery. In some subjects, we measured arteriovenous differences in [11C](R)-rolipram concentration to see whether venous samples could be used instead of arterial samples. Finally, we assessed the ability of IDIF and PBIF to discriminate depressed patients (MDD) and healthy subjects. Results Arterial blood-scaled PBIF gave better results than any non-invasive scaling technique. Excellent results were obtained when the blood-scaled PBIF was prospectively applied to the subjects in Group 2 (VT ratio 1.02 ± 0.05; mean ± SD) and Group 3 (VT ratio 1.03 ± 0.04). Equally accurate results were obtained for two subpopulations of subjects drawn from Groups 2 and 3 who had very differently shaped (i.e. “flatter” or “steeper”) input functions compared to PBIF (VT ratio 1.07 ± 0.04 and 0.99 ± 0.04, respectively). Results obtained via PBIF were equivalent to those obtained via IDIF (VT ratio 0.99 ± 0.05 and 1.00 ± 0.04 for healthy subjects and MDD patients, respectively). Retest variability of PBIF was equivalent to that obtained with full input function and IDIF (14.5%, 15.2%, and 14.1%, respectively). Due to [11C](R)-rolipram arteriovenous differences, venous samples could not be substituted for arterial samples. With both IDIF and PBIF, depressed patients had a 20% reduction in [11C](R)-rolipram binding as compared to control (two-way ANOVA: p=0.008 and 0.005, respectively). These results were almost equivalent to those obtained using 23 arterial samples. Conclusion Although some arterial samples are still necessary, both PBIF and IDIF are accurate and precise alternatives to full arterial input function for [11C](R)-rolipram PET studies. Both techniques give accurate results with low variability, even for clinically different groups of subjects and those with very differently shaped input functions.

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Section: Discussionmentioning

confidence: 99%

Population-based input function and image-derived input function for [11C](R)-rolipram PET imaging: Methodology, validation and application to the study of major depressive disorder

et al. 2012

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“…Over the last 15 years, a large number of studies aimed at validating IDIF methods using brain vessels have been published (Ahn et al, 2000;Asselin et al, 2004;Backes et al, 2009;Baudrexel et al, 2004;Bentourkia, 2005;Berradja et al, 2009;Bodvarsson et al, 2006;Carson et al, 2006;Chen et al, 1998Chen et al, , 2007Chen et al, , 1996Croteau et al, 2010;Fadaili el et al, 2009;Fung et al, 2009;Guo et al, 2007;Kim et al, 2001;Liptrot et al, 2004;Litton, 1997;Maroy et al, 2011;Mourik et al, 2008aMourik et al, , b, 2009Naganawa et al, 2005a, b;Nishizawa et al, 1998;Parker and Feng, 2005;Sanabria-Bohorquez et al, 2003;Sayre and Seo, 2009;Su et al, 2005;Trebossen et al, 1999;Verhaeghe et al, 2010;Wahl et al, 1999;Wang et al, 2009;Zanotti-Fregonara et al, 2007). These methods incorporated a variety of imaginative, and often clever, approaches.…”

Section: How Often Is Image-derived Input Function Used In Positron Ementioning

confidence: 99%

“…An ideal IDIF method would completely eliminate the need for blood sampling. Although some methods do require at least one blood sample to scale the image input (Berradja et al, 2009;Chen et al, 1998;Guo et al, 2007;Naganawa et al, 2005a;Sanabria-Bohorquez et al, 2003), most recently published methods are completely blood free (Backes et al, 2009;Carson et al, 2006;Croteau et al, 2010;Mourik et al, 2008a).…”

Section: Can Blood Sampling Really Be Avoided?mentioning

confidence: 99%

Image-Derived Input Function for Brain PET Studies: Many Challenges and Few Opportunities

Zanotti‐Fregonara

Chen

Liow

et al. 2011

J Cereb Blood Flow Metab

205

217

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Quantitative positron emission tomography (PET) brain studies often require that the input function be measured, typically via arterial cannulation. Image-derived input function (IDIF) is an elegant and attractive noninvasive alternative to arterial sampling. However, IDIF is also a very challenging technique associated with several problems that must be overcome before it can be successfully implemented in clinical practice. As a result, IDIF is rarely used as a tool to reduce invasiveness in patients. The aim of the present review was to identify the methodological problems that hinder widespread use of IDIF in PET brain studies. We conclude that IDIF can be successfully implemented only with a minority of PET tracers. Even in those cases, it only rarely translates into a less-invasive procedure for the patient. Finally, we discuss some possible alternative methods for obtaining less-invasive input function.

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“…These methods included various approaches to delineate arterial voxels from PET images or coregistered anatomical images and to correct for PV effects. 8 Some of these methods require one or more blood samples to scale the estimated IDIF, 9,10 but there are also some fully blood-free methods, which have recently become available. 11,12 Zanotti-Fregonara et al compared eight of these methods 13 and concluded that more accurate kinetic analysis results can be obtained with methods using blood samples to scale the AIF compared with the blood-free methods.…”

Section: Introductionmentioning

confidence: 99%

Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method

Erlandsson

Law

Larsson

et al. 2016

J Cereb Blood Flow Metab

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Kinetic analysis of F-fluorodeoxyglucose positron emission tomography data requires an accurate knowledge the arterial input function. The gold standard method to measure the arterial input function requires collection of arterial blood samples and is an invasive method. Measuring an image derived input function is a non-invasive alternative but is challenging due to partial volume effects caused by the limited spatial resolution of the positron emission tomography scanners. In this work, a practical image derived input function extraction method is presented, which only requires segmentation of the carotid arteries from MR images. The simulation study results showed that at least 92% of the true intensity could be recovered after the partial volume correction. Results from 19 subjects showed that the mean cerebral metabolic rate of glucose calculated using arterial samples and partial volume corrected image derived input function were 26.9 and 25.4 mg/min/100 g, respectively, for the grey matter and 7.2 and 6.7 mg/min/100 g for the white matter. No significant difference in the estimated cerebral metabolic rate of glucose values was observed between arterial samples and corrected image derived input function (p> 0.12 for grey matter and white matter). Hence, the presented image derived input function extraction method can be a practical alternative to noninvasively analyze dynamic F-fluorodeoxyglucose data without the need for blood sampling.

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Kinetic modeling of brain FDG data with input function derived from images by independent component analysis

Cited by 5 publications

References 9 publications

Population-based input function and image-derived input function for [11C](R)-rolipram PET imaging: Methodology, validation and application to the study of major depressive disorder

Population-based input function and image-derived input function for [11C](R)-rolipram PET imaging: Methodology, validation and application to the study of major depressive disorder

Image-Derived Input Function for Brain PET Studies: Many Challenges and Few Opportunities

Estimation of an image derived input function with MR-defined carotid arteries in FDG-PET human studies using a novel partial volume correction method

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