Abstract
PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative values have been used as a surrogate marker for clinical decision-making in numerous clinical studies and trials. However, quantitative values in PET are variable depending on technical, biological, and physical factors. The variability may have a significant impact on a study outcome. Appropriate scanner calibration and quality control, standardization of imaging protocols, and any necessary harmonization strategies are essential to make use of PET as a biomarker with low bias and variability. This review summarizes benefits, limitations, and remaining challenges for harmonization of quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-18F PET imaging. This review is expected to facilitate harmonization of quantitative PET and to promote the contribution of PET-derived biomarkers to research and development in medicine.
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Introduction
Positron emission tomography (PET) can measure in vivo biological processes at the molecular level. In clinical practice, PET is an essential imaging modality for diagnosis of various diseases [1]. Furthermore, PET can be used as a research tool to elucidate human physiological and pathological processes. The advantage of PET imaging is its high quantitative accuracy [2]. Various quantitative metrics are used according to the purpose of each PET imaging. The most popular metric is a standardized uptake value (SUV), which is normalized by body weight. The SUV is calculated by the following equation:
where ACVOI is the average (or the maximum) activity concentration in the specified volume of interest (VOI), ID is the injected dose of radiopharmaceuticals, and BW is the body weight. The SUV is a unitless metric based on the assumption that human tissue density is equal to the density of water (1 g = 1 mL). Tracer uptakes in specified regions can be quantitatively evaluated with SUVs.
In treatment response assessment studies, quantitative metrics such as SUVs and their percentage change can be a surrogate marker to assess the therapeutic response [3,4,5]. For assessing the therapeutic response using pre-therapy and follow-up FDG-PET, changes in tumor SUVs have been used as primary and secondary endpoints in numerous studies and trials [4, 6, 7]. In EORTC criteria and PERCIST [8, 9], the tumor response is classified into four categories: complete metabolic response (CMR), partial metabolic response (PMR), stable metabolic disease (SMD), and progressive metabolic disease (PMD). In such cases, SUVs would be one of the key biomarkers for clinical decision-making.
However, quantitative values derived from PET are variable depending on technical, biological, and physical factors [10,11,12]. In multicenter studies using quantitative metrics, the variability of the metrics may have a significant impact on the study outcomes [13, 14]. Table 1 summarizes main factors affecting SUVs. Appropriate scanner calibration and quality control effectively reduce technical errors. Standardization of imaging protocols including patient preparation can reduce the unwanted biological bias and variability. Finally, any necessary harmonization strategies should be implemented to minimize the inter-scanner physical variability. These three-steps are key to get reliable (repeatable) and comparable (reproducible) quantitative values. Figure 1 presents the expected merits of harmonization. Harmonization leads to precise outcomes even with small datasets, because the non-pathophysiological bias and variation can be removed [15]. To appropriately use PET as an imaging biomarker, the benefits, limitations, and remaining challenges for harmonization of PET should be known. This review summarizes harmonization strategies for quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-18F emerging applications.
Harmonization strategies for whole-body PET in oncology
Several organizations have constructed scanner qualification programs for whole-body FDG-PET imaging in oncology. All programs involve standardization of imaging protocols and image quality, and some programs focus more on harmonization of quantitative metrics based on phantom experiments. Figure 2 shows representative phantoms used for harmonization of PET and Table 2 summarizes studies related to PET harmonization [13, 14, 16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]. While earlier studies focused only on the variability in SUVs, recent studies have assessed clinical outcomes for various cancers using various quantitative metrics such as textural features. PET harmonization can drive the use of PET in clinical oncology studies.
Photographs and PET images of representative phantoms used for harmonization: ACR phantom and its PET image (A), SNMMI-CTN chest phantom and its PET images (B), and NEMA NU-2 image quality phantom and its PET image (C). The PET image of the ACR phantom is reprinted from the paper by DiFilippo et al. [34]. The photograph and PET images of the SNMMI-CTN chest phantom are reprinted from the paper by Sunderland et al. [36]. These studies were originally published in JNMT and JNM, respectively.
The American College of Radiology Imaging Network (ACRIN) has verified the accuracy of average SUVs using a uniform cylindrical phantom (5.92–8.88 or 8.14 kBq/mL; 6283 or 9293 mL). A 90% circular region-of-interest (ROI) of the interior diameter was applied to the phantom image, and the acceptable average SUV was 1.0 ± 0.1. Scheuermann et al. [35] reported that 12% of scanners (12/101) they tested failed due to incorrect SUV or normalization calibrations. This result suggested that verification of accurate SUV calibration is very important in multicenter studies.
The Society of Nuclear Medicine and Molecular Imaging (SNMMI) Clinical Trial Network (CTN) uses an anthropomorphic chest phantom with fillable spheres for validating the quantitative performance of PET/CT scanners [36, 37]. The standard radioactivity concentration ratio between the spheres and background are 4:1. SUVmax for small spheres and SUVmean for background regions were measured to assess the scanner calibration accuracy and quantitative performance for small lesions [36]. The acceptance criterion for the SUVmean of the uniform background was 1.0 ± 0.1.
In 2010, the European Association of Nuclear Medicine (EANM) Research Ltd. (EANM/EARL) launched a PET/CT scanner accreditation program [38]. The program uses a NEMA NU-2 image quality phantom to measure SUV recovery coefficients in relation to sphere size (diameters: 10–37 mm). The phantom is filled with 18F solutions, and the sphere-to-background radioactivity concentration ratio is 10 (20 and 2 kBq/mL) [22]. Maximum, mean, and peak recovery coefficients are measured to check whether they are within pre-specified upper and lower limits. High-resolution scanners sometimes required down-smoothing to fit this range. Consequently, the first standard limits EARL1 were updated to EARL2 according to the progress in PET instrumentation and reconstruction technology (Fig. 3A) [38, 39].
The Radiological Society of North America/Quantitative Imaging Biomarker Alliance (RSNA/QIBA) created the FDG-PET/CT profile to characterize and reduce the variability in SUVs. The QIBA profile comprehensively covers acquisition, reconstruction and post-processing, and analysis and interpretation to obtain SUVmax with a within-subject coefficient of variation (wCV) of 10–12% [40, 41]. Conforming to this profile supports the claim that an increase in SUVmax of 39% or more, or a decrease of 28% or more, indicates that a true biological change has occurred with 95% confidence.
The Japanese Society of Nuclear Medicine (JSNM) has published standard PET imaging protocols and phantom test procedures and criteria to standardize the methods that would affect image quality and quantification accuracy. A NEMA NU-2 image quality phantom is used with a sphere-to-background radioactivity concentration ratio of 4. For SUV harmonization, the SUVmax of hot spheres must satisfy the specified range (Fig. 3B) [33, 42]. A post-smoothing process was sometimes needed for high-resolution systems to meet this range, just as for the EARL limits. In 2022, a JSNM working group proposed new upper and lower ranges for SUVmax recovery curves [33]. This SUV harmonization range was proposed based on NEMA NU-2 image quality phantom data measured with 23 PET/CT scanners. The working group also suggested image quality criteria to ensure the 10 mm sphere visibility and to reduce the intra-scanner variability of quantitative metrics: the contrast-to-noise ratio (QH,10 mm/N10mm) should be ≥ 2.5 and the coefficient-of-variance in the background (CVBG) should be ≤ 10% (or 14.1%).
Since recent PET systems with advanced reconstruction algorithms may provide higher quantitative values, downgrading image resolution may be needed to harmonize quantitative PET data to those of older PET systems. Such a harmonization process may lose the superior imaging capability of the newer PET systems. One solution to retain the high image quality provided by newer PET systems has been to make a second set of images to provide comparable quantitative values [43]. However, this introduced a cumbersome PET data handling issue, and a better strategy was needed.
To overcome the double dataset handling issue, Kelly et al. [44] developed software that can calculate the harmonized SUVs in the background while displaying PET images reconstructed by an advanced algorithm. It is possible to provide the harmonized SUVs based on a single dataset without losing the advantage of high-resolution PET images. Quak et al. [17] successfully harmonized SUVs of 517 cancer patients using the commercial software, EQ.PET (Siemens). This software can apply the harmonization method of Kelly et al. to clinical data. The harmonization process of the EQ.PET is summarized as follows. First, a maximum recovery coefficient is measured for each sphere in the NEMA NU-2 image quality phantom. These values are then compared to a reference recovery curve to calculate the root mean square error (RMSE). This comparison is repeated while increasing the full width at half maximum (FWHM) of a Gaussian filter. The FWHM value at which the minimum RMSE is obtained then is applied to PET images for harmonization.
Free vendor-neutral software is desirable for widespread application of this harmonization method. Daisaki et al. [29] successfully harmonized SUVs using the “RC Tool for Harmonization” (Nihon Medi-Physics) which semi-automatically calculates an optimal FWHM of the Gaussian filter and RAVAT (Nihon Medi-Physics) which calculates QIBA Profile-compliant quantitative values. They harmonized SUV recovery curves of 15 PET systems to the JSNM harmonization range (Fig. 4). These vendor-neutral software programs can be freely applied to PET images, irrespective of scanner models. Two datasets are not required, because RAVAT applies a pre-determined additional smoothing filter to PET images. As mentioned below, several clinical studies have used RAVAT software for harmonization of quantitative PET [45, 46].
Harmonization strategies for brain PET
For brain PET, there are many kinds of radiotracers that can measure metabolisms, protein aggregates, transporters, and receptors [47]. Depending on radiotracer characteristics, various quantitative metrics are used to assess tracer uptakes. For harmonization, brain phantoms are generally used, because image characteristics such as tracer distribution, uptake contrast ratio, and image noise levels for brain PET are different from those for whole-body PET.
In the multicenter Alzheimer’s Disease Neuroimaging Initiative (ADNI) study, Joshi et al. [48] proposed the two-step harmonization method with the Hoffman 3D brain phantom. Using the digital Hoffman phantom image smoothed with the 8 mm FWHM Gaussian filter as the target resolution, they selected an FWHM of the Gaussian filter for each scanner model. Figure 5 shows representative PET images applied with and without a scanner-specific smoothing filter. Joshi et al. reported that the scanner-specific smoothing approach effectively reduced the inter-scanner variability, while the low frequency correction was not effective. Ikari et al. [49] also used the image resolution of 8 mm FWHM as a reference level. In the Japanese-ADNI (J-ADNI) study, the image resolution was harmonized by a scanner-specific smoothing filter [50]. There are some reports using gray matter contrast recovery (RCGM) and gray-to-white matter contrast (GMWMr) of the Hoffman 3D brain phantom [49, 51, 52]. Like the harmonization approach for whole-body PET, Verwer et al. [51] proposed upper and lower limits for RCGM and GMWMr to harmonize image contrast.
Hoffman phantom images of five PET scanner models with pre- and post-smoothing. After application of the smoothing filter, the visual difference in resolution was reduced. The images are reprinted with modification from the paper by Joshi et al. Neuroimage. 2009; 46: 154–159 [48].
Even though the Hoffman 3D brain phantom is widely used, it can only simulate the distribution pattern of FDG in the brain [53]. Other phantoms might be better model for radioactivity distributions other than those of FDG. Hoye et al. [54] harmonized 11C-raclopride brain PET images measured by HRRT (high-resolution research tomograph, Siemens) and HR + (a standard clinical scanner, Siemens) using a 3D brain phantom (the Iida phantom [55]). Fahey et al. [56] evaluated image uniformity, spatial resolution, and image quality of 13 PET scanners using the SNMMI CTN brain phantom, which has a uniform section, a resolution section, and a clinical brain simulation section.
Clinical studies with harmonized PET
Quantitative values in PET can be used as a biomarker in clinical studies once harmonizing of PET data is completed. In oncology fields, PET imaging has been involved in many therapeutic studies [57,58,59,60,61,62]. Ito et al. [45] assessed anti-PD-1 therapy response to non-small cell lung cancer (NSCLC) using harmonized FDG-PET. They used nine PET/CT scanners and harmonized SUVs based on the JSNM harmonization strategy before applying EORTC criteria and PERCIST. Changes in harmonized SUVs correlated well with overall survival of NSCLC patients. Kitajima et al. [63] investigated relationships of prognosis for stage I–III breast cancer patients and pre-treatment FDG-PET/CT-derived quantitative metrics. They harmonized maximum SUVs of the image quality phantom among five PET/CT scanners according to the JSNM harmonization method. Primary tumor and nodal maximum SUVs and total lesion glycolysis (TLG), which are derived from pre-treatment FDG-PET/CT, were associated with recurrence-free and overall survivals in patients with operable breast cancer.
Among brain PET studies, Sevigny et al. [64] reported that Aducanumab, a human monoclonal antibody that selectively accumulates with Aβ aggregates, reduced Aβ plaques with amyloid PET as an adjunct marker for Aβ pathology. They harmonized amyloid PET images to be a uniform spatial resolution of 6.5 mm in-plane and 7.5 mm axially [65]. Change in amyloid PET SUVR values was used as a surrogate marker for treatment response. Senda et al. [66] performed a multicenter observational study on potential preclinical and prodromal Alzheimer’s disease. Image reconstruction parameters for each PET scanner in the observational study were determined with the Hoffman 3D brain phantom and the uniform cylindrical phantom so that all the scanners met previously established image quality criteria [49]. Senda et al. conducted brain FDG, amyloid and tau PET imaging, and measured AD t-sum values for FDG images and SUVRs for amyloid and tau images.
Post-reconstruction data-driven harmonization methods for PET
Adjusting reconstruction settings and applying additional smoothing filter are major approaches for PET harmonization. These methods are categorized into “image-based” harmonization approach, because “harmonized images” need to be generated before measuring quantitative metrics in PET images. On the other hand, several data-driven harmonization methods have been applied for quantitative metrics in PET [67].
The centiloid scale (CL) is an example of popularly used data-driven harmonization methods. The CL is a harmonized quantitative metric for amyloid PET proposed by Klunk et al. [68]. The standard CL is calculated from the standardized uptake value ratio (SUVR) of amyloid PET as follows:
where SUVRIND is an SUVR value of an individual, SUVRYC-0 is the mean SUVR of 34 young healthy controls, and SUVRAD-100 is the mean SUVR of 45 AD patients. The standard image datasets and the VOI template are available online (https://www.gaain.org). SUVR values measured using various scanners and different amyloid tracers were converted to the unified 0 to 100 scale [69,70,71]. The CL scale has been applied to a tau PET SUVR conversion by Yamao et al. [72].
Another representative data-driven approach is the ComBat harmonization method [23], which was originally proposed to reduce “batch effects” in the field of genomics [73]. ComBat directly applies to quantitative values derived from PET images; therefore, additional image data processing and any phantom data acquisition are not mandatory. Orlhac et al. [74] provided a practical guide and list of limitations when applying ComBat to image-derived quantitative metrics. Figure 6 shows simulated data before and after using ComBat. In a multicenter study, Dissaux et al. [75] applied ComBat to numerous FDG-PET/CT radiomic features derived from four different scanners. They reported two radiomic features associated with local control in NSCLC patients undergoing stereotactic body radiation therapy. Hotta et al. [76] also used ComBat to evaluate FDG-PET/CT textural features of primary tumors that were acquired by three different scanners. They evaluated the prognostic value of pretreatment FDG-PET/CT for patients with surgically treated rectal cancer. Gray-level co-occurrence matrix entropy, which presents intra-tumoral metabolic heterogeneity, was associated with overall survival and progression-free survival.
Box plot and quantitative value distributions for data of three virtual sites. After applying the ComBat harmonization method, data on the three sites showed similar distributions. The figures are reprinted with modification from the paper by Orlhac et al. J Nucl Med. 2022; 63: 172–179 [74].
PET/MR harmonization initiatives
Hybrid PET/MR systems can provide superior soft tissue contrast by MR and functional information by PET at the same position. Such combined information is useful in various fields including neurology, neuro-oncology, cardiology, and oncology [77,78,79]. The first commercial clinical PET/MR was the Ingenuity TF PET/MR (Philips), a separated-type system [80]. The Ingenuity TF PET/MR has a sequential configuration that connects two subsystems spatially separated by a shared patient bed, so simultaneous PET and MR imaging is not possible. Subsequent integrated PET/MR systems such as the Biograph mMR (Siemens), SIGNA PET/MR (GE), and uPMR790 (United Imaging) allow simultaneous imaging [81,82,83].
One of the key challenges of PET/MR imaging is MR-based attenuation correction (MR-AC). A segmentation-based method has been proposed to provide a four-compartment body segmentation including air, lung, fat, and soft tissue using the Dixon MR-sequence [84]. However, the segmentation-based methods do not account for bone structures, because a near-zero signal is obtained from bone regions due to bone having both a low spin density and a rapid T2 relaxation rate [85, 86]. Inaccurate bone consideration leads to large underestimation of PET uptakes, especially in near-bone tissues [87]. To overcome this problem, ultrashort echo-time (UTE) and zero echo-time (ZTE) MR-sequences have been developed to capture bone information, and these sequences provide more accurate attenuation maps [88, 89]. More recently, deep learning-based techniques are offering the option to improve accuracy of attenuation correction without CT images [90,91,92].
Although phantom measurements are necessary for scanner quality control and harmonization of quantitative metrics, there are no standard phantoms for PET/MR hybrid imaging. Figure 7 overviews MR-AC problems in phantom measurements. Ziegler et al. [93] reported that a strong artifact was caused by an inhomogeneous radiofrequency excitation MR signal due to a large amount of water as the phantom fluid. Errors in segmentation result in incorrect recognition of water, fat, and air. Boellaard et al. [94] noted that the lung insert was missing in some MR-AC maps. Then, it was found that phantom walls could not be visualized in standard MR-sequences, because they were made of plastic or glass materials [95]. These issues should be solved for valid phantom assessment. Although adding NaCl and NiSO4 solutions may improve the homogeneity of MR signals [93], it is still difficult to solve the problem of the missing phantom wall. Ziegler et al. [95] suggested that CT-based AC was suitable for accurate PET performance measurements of PET/MR systems when using the NEMA NU-2 image quality phantom.
![figure 7](http://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs12149-022-01820-x/MediaObjects/12149_2022_1820_Fig7_HTML.png)
© SNMMI (A), Boellaard et al. [94]. © Am. Assoc. Phys. Med. (B), and Ziegler et al. [95]. Copyright © 2015, Ziegler et al. CC BY 4.0
Overview of MR-AC problems: inhomogeneous MR signal (A), missing phantom walls (B), and segmentation errors (C). The images are reprinted with a modification from the following papers: Ziegler et al. [93].
Even though MR-AC accuracy was not verified, Laforest et al. [96] harmonized contrast recovery coefficients (CRCs) between the SIGNA PET/MR and Biograph mMR using the NEMA NU-2 image quality phantom. Six customized spheres (diameters: 8.5, 11.5, 15, 25, 32.5, and 44 mm) were used in addition to the standard six spheres (diameters: 10, 13, 17, 22, 28, and 37 mm). The CT-based AC methods were used and CRCs were evaluated together with the root-mean squared discrepancy (RMSD) to determine harmonized reconstruction parameters. Laforest et al. achieved high CRCs within the limits of EARL1 and minimized the variation between two different PET/MR scanners. Jentzen et al. [97] evaluated quantitative accuracy of 124I PET/MR images for patients with differentiated thyroid cancer using serial PET/CT data as a reference. After harmonization processing, activity concentrations of lesions in the neck were comparable between images measured with PET/CT (CT-AC) and PET/MR (MR-AC).
For evaluation of brain PET imaging, the Hoffman 3D brain phantom has been widely used as mentioned above. Since the phantom is composed of acrylic plates, MR-AC accuracy cannot be evaluated as well as for the NEMA NU-2 image quality phantom. Ribeiro et al. [98] evaluated PET image quality of PET/MR systems using the Hoffman 3D brain phantom with CT-based template AC. On the other hand, Teuho et al. [99] used another 3D brain phantom (the Iida phantom [55]) which contains the gray matter, white matter, skull, and tracheal structures with a realistic head contour. Teuho et al. evaluated the difference between CT-AC and MR-AC on visual and quantitative differences using multiple PET/MR and PET/CT systems. They noted that regional differences between PET/MR and PET/CT systems were minimized using CT-AC. Because white matter of the 3D brain phantom is composed of polymer, which is not visible in MR-AC, the Iida phantom is still insufficient for evaluation of MR-AC accuracy [100].
CT-AC is the current standard method in PET/MR phantom studies for evaluating image quality and quantitative accuracy, thereby aiming at harmonization. In human PET/MR studies, however, MR-AC is generally used to reconstruct PET images. A PET/MR multimodal phantom, which mimics the electron density and MR contrast of human tissue, is required to evaluate MR-AC accuracy, and facilitate PET/MR harmonization [100]. Harries et al. [101] developed a realistic phantom of the human head using water-saturated gypsum plaster, silicone, agarose gel, etc. Rausch et al. [102] developed a cylindrical phantom with a 3D printable MR-visible polymer. Preliminary evaluations of MR-AC accuracy may be possible using these phantoms although they still cannot provide PET signals. On the other hand, Canata et al. [103] proposed to use a patient as a phantom to assess the MR-AC accuracy using CT as the reference. PET/MR harmonization will be facilitated if a standard method that can verify the MR-AC accuracy is established.
Non-18F PET harmonization initiatives
Many novel radioisotopes have emerged for diagnostic PET imaging, theranostics, and immuno-PET imaging. One of these radioisotopes for PET is 68Ga. 68Ga-labelled tracers targeting somatostatin receptor, prostate-specific membrane antigen (PSMA), and fibroblast activation protein inhibitor (FAPI) have been used in recent clinical studies [104,105,106]. These tracers are often used in combination with 177Lu and 225Ac as part of theranostics [105, 107, 108]. 124I has been used to evaluate lesion dosimetry prior to radioiodine treatment in patients with differentiated thyroid cancer [97, 109]. Immuno-PET is a promising tool for predicting the outcome of monoclonal antibody-based cancer therapy using such radioisotopes as 64Cu, 74Br, 86Y, 89Zr, and 124I [110, 111].
It is essential to consider the physical properties of each radioisotope for harmonization of non-18F PET imaging. Table 3 lists the physical properties of typical positron emitting isotopes [112,113,114,115]. Long positron ranges due to the high energy of emitted positrons lead to blurring of the source distribution and loss of spatial resolution [116, 117]. A low positron branching ratio may result in inferior image quality and errors in quantitative uptake measurements due to the low count statistics and the cross-calibration error. In addition, some radioisotopes emit prompt or non-prompt γ rays in the decay process (Table 3). If such associated γ rays have contaminated coincidence count data in energy and coincidence timing windows, they will lead to degradation of image quality and quantitative accuracy [113].
Some studies have investigated PET image characteristics of different radioisotopes [114, 118]. Soderlund et al. [113] evaluated the image quality and spatial resolution for a set of radioisotopes (18F, 11C, 89Zr, 124I, 68Ga and 90Y). Figure 8 shows PET images of the NEMA NU-2 image quality phantom filled with the various radioisotopes. 124I and 68Ga, which have longer positron ranges, showed slight degradation of contrast recovery and resolution in small spheres compared to 18F. Other reports using small animal PET systems showed considerable image blurring due to the positron range [119, 120]. Especially for 124I, the image quality may be deteriorated due to prompt γ-rays having the energy (603 keV) that is similar to 511 keV of annihilation photons.
PET images of the NEMA NU-2 image quality phantom filled with various radioisotopes. The images were acquired by Biograph mCT (Siemens). The images are reprinted with modification from the paper by Soderlund et al. [113].
Harmonization studies on non-18F PET imaging have been conducted by several groups. Huizing et al. [121] performed 18F and 68Ga PET phantom acquisitions using 13 PET/CT systems and evaluated quantitative recovery coefficients according to the EARL strategy. While 18F recovery coefficient curves for all PET/CT systems satisfied the range for EARL1 standards, 68Ga curves were located near the lower limit of the range. After correcting the difference between 68Ga and 18F cross-calibrations, 68Ga recovery coefficient curves for most scanners satisfied the EARL1 standards. Some investigators worked on multicenter 89Zr PET harmonization studies. Makris et al. [122] and Kaalep et al. [123] used the NEMA NU-2 image quality phantom with a 10:1 sphere-to-background ratio according to the EARL protocol. Results from both studies showed that inaccuracy of the local dose calibrator cross-calibration would be a large source of bias in quantitative values. Christian et al. [124] used an anthropomorphic chest oncology phantom filled with 89Zr at a clinically relevant activity level and a 4:1 sphere-to-background ratio. They investigated optimal reconstruction parameters based on visual lesion detectability and SUVpeak recovery coefficients.
Minimizing measurement errors and cross-calibration errors is essential for harmonization of non-18F PET imaging. In most PET/CT systems, cross-calibration between the dose calibrator and a PET system is performed using 18F, which may result in inaccurate quantification with radioisotopes other than 18F. Accurate quantification for non-18F radioisotopes requires proper correction of such physical properties as branching ratio, half-life, and prompt γ rays. Bailey et al. [125] and Sanderson et al. [126] noted most PET systems underestimated 68Ga SUVs which was caused by overestimation of 68Ga radioactivity using dose calibrators with a default calibration factor setting. Appropriate 68Ga calibration factor setting is important to provide accurate quantitative values. Beattie et al. [127] recommended the use of appropriate dose calibration factors for measuring 89Zr and 124I. For 124I, they proposed to use a copper filter to remove the contribution of X-rays emitted by 124I in the 20–40 keV range.
As mentioned earlier, additional care is required for harmonization of non-18F PET. Appropriate quality control for PET systems, dose calibrators, and other associated devices should be performed considering their physical properties. Ideally, scanner validation and harmonization are conducted with the radioisotopes being used in the clinical protocol [124].
Future challenges and summary
The major harmonization approach is adjusting image reconstruction parameters and smoothing filters so that quantitative values are comparable among different scanners. It should be noted that such harmonization methods may spoil small lesion detectability. Aide et al. [128] presented a representative case before and after harmonization, which used a state-of-the-art SiPM-based PET/CT scanner for measurements (Fig. 9). To mitigate deteriorating small lesion detectability, harmonization criteria should be regularly updated in step with scanner performance improvements [11]. Because novel PET systems such as SiPM-based scanners [129, 130], total-body scanners [131, 132], and brain-dedicated systems [133, 134] have been increasingly applied to clinical studies, harmonization methods should adapt to such imaging systems.
18F-fluorocholine PET images acquired by Biograph Vision scanner (Siemens). The patient was referred for restaging after a liver graft for hepatocellular carcinoma. PET data were reconstructed with an EARL-compliant harmonized parameter (9 mm Gaussian filter) (upper) and with point-spread function modeling and 0.9 mm voxel size (lower). The red arrow indicates a tiny lung metastasis. The images are reprinted with modification from the paper by Aide et al. [128].
Many organizations have built harmonization strategies; however, international methodology harmonization is necessary to achieve harmonization of quantitative PET, because clinical studies and trials are conducted worldwide. International harmonization of the standards remains as an issue to ensure the comparability of quantitative values in PET.
This review discussed harmonization strategies for quantitative PET. To make use of PET as a biomarker, quantitative values derived from PET should be comparable between scanners, sites, and studies. By minimizing the bias and variability due to technical, biological, and physical issues, quantitative PET can precisely highlight physiological and pathological changes even with small datasets. It is expected that this review will facilitate harmonization of quantitative PET and PET-derived biomarkers contribute to research and development in medicine.
Data availability
Data presented in this review are available upon reasonable request.
References
Evangelista L, Zucchetta P, Moletta L, Serafini S, Cassarino G, Pegoraro N, et al. The role of FDG PET/CT or PET/MRI in assessing response to neoadjuvant therapy for patients with borderline or resectable pancreatic cancer: a systematic literature review. Ann Nucl Med. 2021;35:767–76.
Meikle SR, Sossi V, Roncali E, Cherry SR, Banati R, Mankoff D, et al. Quantitative PET in the 2020s: a roadmap. Phys Med Biol. 2021;66:06FRM01.
Raunig DL, McShane LM, Pennello G, Gatsonis C, Carson PL, Voyvodic JT, et al. Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. Stat Methods Med Res. 2015;24:27–67.
O’Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86.
Weber WA. Positron emission tomography as an imaging biomarker. J Clin Oncol. 2006;24:3282–92.
Han S, Kim Y, Woo S, Kim T-H, Ryu J-S. Prognostic and predictive values of interim 18F-FDG PET during neoadjuvant chemoradiotherapy for esophageal cancer: a systematic review and meta-analysis. Ann Nucl Med. 2021;35:447–57.
Mankoff DA, Pryma DA, Clark AS. Molecular imaging biomarkers for oncology clinical trials. J Nucl Med. 2014;55:525–8.
Xu J, Li Y, Xie L, Sun X, Cai Z, Wang S, et al. An evaluation of the response to neoadjuvant chemotherapy for osteosarcoma of extremities: PERCIST versus RECIST 1.1 criteria after long-term follow-up. Ann Nucl Med. 2022;36:553–61.
Guo H, Xu K, Duan G, Wen L, He Y. Progress and future prospective of FDG-PET/CT imaging combined with optimized procedures in lung cancer: toward precision medicine. Ann Nucl Med. 2022;36:1–14.
Fahey FH, Kinahan PE, Doot RK, Kocak M, Thurston H, Poussaint TY. Variability in PET quantitation within a multicenter consortium. Med Phys. 2010;37:3660–6.
Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med. 2009;50(Suppl 1):11S-20S.
Adams MC, Turkington TG, Wilson JM, Wong TZ. A systematic review of the factors affecting accuracy of SUV measurements. AJR Am J Roentgenol. 2010;195:310–20.
Quak E, le Roux P-Y, Lasnon C, Robin P, Hofman MS, Bourhis D, et al. Does PET SUV harmonization affect PERCIST response classification? J Nucl Med. 2016;57:1699–706.
Houdu B, Lasnon C, Licaj I, Thomas G, Do P, Guizard A-V, et al. Why harmonization is needed when using FDG PET/CT as a prognosticator: demonstration with EARL-compliant SUV as an independent prognostic factor in lung cancer. Eur J Nucl Med Mol Imaging. 2019;46:421–8.
Doot RK, Kurland BF, Kinahan PE, Mankoff DA. Design considerations for using PET as a response measure in single site and multicenter clinical trials. Acad Radiol. 2012;19:184–90.
Makris NE, Huisman MC, Kinahan PE, Lammertsma AA, Boellaard R. Evaluation of strategies towards harmonization of FDG PET/CT studies in multicentre trials: comparison of scanner validation phantoms and data analysis procedures. Eur J Nucl Med Mol Imaging. 2013;40:1507–15.
Quak E, le Roux P-Y, Hofman MS, Robin P, Bourhis D, Callahan J, et al. Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients. Eur J Nucl Med Mol Imaging. 2015;42:2072–82.
Lasnon C, Majdoub M, Lavigne B, Do P, Madelaine J, Visvikis D, et al. 18F-FDG PET/CT heterogeneity quantification through textural features in the era of harmonisation programs: a focus on lung cancer. Eur J Nucl Med Mol Imaging. 2016;43:2324–35.
Lasnon C, Quak E, le Roux P-Y, Robin P, Hofman MS, Bourhis D, et al. EORTC PET response criteria are more influenced by reconstruction inconsistencies than PERCIST but both benefit from the EARL harmonization program. EJNMMI Physics. 2017;4:17.
Panetta JV, Daube-Witherspoon ME, Karp JS. Validation of phantom-based harmonization for patient harmonization. Med Phys. 2017;44:3534–44.
Tsutsui Y, Daisaki H, Akamatsu G, Umeda T, Ogawa M, Kajiwara H, et al. Multicentre analysis of PET SUV using vendor-neutral software: the Japanese harmonization technology (J-Hart) study. EJNMMI Res. 2018;8:83.
Kaalep A, Sera T, Rijnsdorp S, Yaqub M, Talsma A, Lodge MA, et al. Feasibility of state of the art PET/CT systems performance harmonization. Eur J Nucl Med Mol Imaging. 2018;45:1344–61.
Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018;59:1321–8.
Namías M, Bradshaw T, Menezes VO, Machado MAD, Jeraj R. A novel approach for quantitative harmonization in PET. Phys Med Biol. 2018;63:095019.
Rubello D, Colletti PM. SUV harmonization between different hybrid PET/CT systems. Clin Nucl Med. 2018;43:811–4.
Kaneta T, Daisaki H, Ogawa M, Liu E-T, Iizuka H, Arisawa T, et al. Use of count-based image reconstruction to evaluate the variability and repeatability of measured standardised uptake values. PLoS ONE. 2018;13:e0192549.
Kaalep A, Burggraaff CN, Pieplenbosch S, Verwer EE, Sera T, Zijlstra J, et al. Quantitative implications of the updated EARL 2019 PET–CT performance standards. EJNMMI Phys. 2019;6:1–16.
Machado MAD, Menezes VO, Namías M, Vieira NS, Queiroz CC, Matheoud R, et al. Protocols for harmonized quantification and noise reduction in low-dose oncologic 18F-FDG PET/CT imaging. J Nucl Med Technol. 2019;47:47–54.
Daisaki H, Kitajima K, Nakajo M, Watabe T, Ito K, Sakamoto F, et al. Usefulness of semi-automatic harmonization strategy of standardized uptake values for multicenter PET studies. Sci Rep. 2021;11:8517.
Prenosil GA, Hentschel M, Weitzel T, Sari H, Shi K, Afshar-Oromieh A, et al. EARL compliance measurements on the biograph vision Quadra PET/CT system with a long axial field of view. EJNMMI Physics. 2022;9:26.
Monsef A, Ay MR, Sheikhzadeh P, Geramifar P, Rahmim A, Ghafarian P. Harmonization based on quantitative analysis of standardized uptake value variations across PET/CT scanners: a multicenter phantom study. Nucl Med Commun. 2022;43:1004–14.
Jiménez-Ortega E, Agüera R, Ureba A, Balcerzyk M, Wals-Zurita A, García-Gómez FJ, et al. Implications of the Harmonization of [18F]FDG-PET/CT imaging for response assessment of treatment in radiotherapy planning. Tomography. 2022;8:1097–112.
Akamatsu G, Shimada N, Matsumoto K, Daisaki H, Suzuki K, Watabe H, et al. New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies. Ann Nucl Med. 2022;36:144–61.
DiFilippo FP, Patel M, Patel S. Automated quantitative analysis of american college of radiology PET phantom images. J Nucl Med Technol. 2019;47:249–54.
Scheuermann JS, Saffer JR, Karp JS, Levering AM, Siegel BA. Qualification of PET scanners for use in multicenter cancer clinical trials: the American college of radiology imaging network experience. J Nucl Med. 2009;50:1187–93.
Sunderland JJ, Christian PE. Quantitative PET/CT scanner performance characterization based upon the society of nuclear medicine and molecular imaging clinical trials network oncology clinical simulator phantom. J Nucl Med. 2015;56:145–52.
Ulrich EJ, Sunderland JJ, Smith BJ, Mohiuddin I, Parkhurst J, Plichta KA, et al. Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans. Med Phys. 2018;45:258–76.
Kaalep A, Sera T, Oyen W, Krause BJ, Chiti A, Liu Y, et al. EANM/EARL FDG-PET/CT accreditation-summary results from the first 200 accredited imaging systems. Eur J Nucl Med Mol Imaging. 2018;45:412–22.
Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imag. 2015;42:328–54.
FDG-PET/CT Technical Committee. FDG-PET/CT as an imaging biomarker measuring response to cancer therapy. Quantitative imaging biomarkers alliance, version 1.13, technically confirmed version. QIBA, Nov 18, 2016. Available from: RSNA.ORG/QIBA
Kinahan PE, Perlman ES, Sunderland JJ, Subramaniam R, Wollenweber SD, Turkington TG, et al. The QIBA Profile for FDG PET/CT as an imaging biomarker measuring response to cancer therapy. Radiology. 2020;294:647–57.
Japanese Society of Nuclear Medicine. Standard PET imaging protocols and phantom test procedures and criteria: executive summary. 2017. Available from: http://jsnm.sakura.ne.jp/wp_jsnm/wp-content/themes/theme_jsnm/doc/StandardPETProtocolPhantom20170201.pdf (Accessed 16 Dec 2022)
Lasnon C, Desmonts C, Quak E, Gervais R, Do P, Dubos-Arvis C, et al. Harmonizing SUVs in multicentre trials when using different generation PET systems: prospective validation in non-small cell lung cancer patients. Eur J Nucl Med Mol Imaging. 2013;40:985–96.
Kelly MD, Declerck JM. SUVref: reducing reconstruction-dependent variation in PET SUV. EJNMMI Res. 2011;1:16.
Ito K, Kitajima K, Toriihara A, Ishibashi M, Nakahara T, Daisaki H, et al. 18F-FDG PET/CT for monitoring anti-PD-1 therapy in patients with non-small cell lung cancer using SUV harmonization of results obtained with various types of PET/CT scanners used at different centers. Ann Nucl Med. 2021;35:1253–63.
Kitajima K, Watabe T, Nakajo M, Ishibashi M, Daisaki H, Soeda F, et al. Tumor response evaluation in patients with malignant melanoma undergoing immune checkpoint inhibitor therapy and prognosis prediction using 18F-FDG PET/CT: multicenter study for comparison of EORTC, PERCIST, and imPERCIST. Jpn J Radiol. 2022;40:75–85.
Phelps ME. PET: the merging of biology and imaging into molecular imaging. J Nucl Med. 2000;41:661–81.
Joshi A, Koeppe RA, Fessler JA. Reducing between scanner differences in multi-center PET studies. Neuroimage. 2009;46:154–9.
Ikari Y, Akamatsu G, Nishio T, Ishii K, Ito K, Iwatsubo T, et al. Phantom criteria for qualification of brain FDG and amyloid PET across different cameras. EJNMMI Physics. 2016;3:23.
Ikari Y, Nishio T, Makishi Y, Miya Y, Ito K, Koeppe RA, et al. Head motion evaluation and correction for PET scans with 18F-FDG in the Japanese Alzheimer’s disease neuroimaging initiative (J-ADNI) multi-center study. Ann Nucl Med. 2012;26:535–44.
Verwer EE, Golla SSV, Kaalep A, Lubberink M, van Velden FHP, Bettinardi V, et al. Harmonisation of PET/CT contrast recovery performance for brain studies. Eur J Nucl Med Mol Imaging. 2021;48:2856–70.
Akamatsu G, Ikari Y, Nishio T, Nishida H, Ohnishi A, Aita K, et al. Optimization of image reconstruction conditions with phantoms for brain FDG and amyloid PET imaging. Ann Nucl Med. 2016;30:18–28.
Hoffman EJ, Cutler PD, Guerrero TM, Digby WM, Mazziotta JC. Assessment of accuracy of PET utilizing a 3-D phantom to simulate the activity distribution of [18F]fluorodeoxyglucose uptake in the human brain. J Cereb Blood Flow Metab. 1991;11:A17-25.
Hoye J, Toyonaga T, Zakiniaeiz Y, Stanley G, Hampson M, Morris ED. Harmonization of [11C]raclopride brain PET images from the HR+ and HRRT: method development and validation in human subjects. EJNMMI Physics. 2022;9:27.
Iida H, Hori Y, Ishida K, Imabayashi E, Matsuda H, Takahashi M, et al. Three-dimensional brain phantom containing bone and grey matter structures with a realistic head contour. Ann Nucl Med. 2013;27:25–36.
Fahey F, Christian P, Zukotynski K, Sexton-Stallone B, Kiss C, Clarke B, et al. Use of a qualification phantom for PET brain imaging in a multicenter consortium: a collaboration between the pediatric brain tumor consortium and the SNMMI clinical trials network. J Nucl Med. 2019;60:677–82.
Graham MS, Krebs S, Bale T, Domfe K, Lobaugh SM, Zhang Z, et al. Value of [18F]-FDG positron emission tomography in patients with recurrent glioblastoma receiving bevacizumab. Neurooncol Adv. 2020;2:1–9.
Azad N, Yu M, Davidson B, Choyke P, Chen CC, Wood BJ, et al. Translational predictive biomarker analysis of the phase 1b Sorafenib and Bevacizumab study expansion cohort. Mol Cell Proteomics. 2013;12:1621–31.
Scott AM, Mitchell PL, O’Keefe G, Saunder T, Hicks RJ, Poon A, et al. Pharmacodynamic analysis of tumour perfusion assessed by 15O-water-PET imaging during treatment with sunitinib malate in patients with advanced malignancies. EJNMMI Res. 2012;2:31.
Hristova I, Boellaard R, Vogel W, Mottaghy F, Marreaud S, Collette S, et al. Retrospective quality control review of FDG scans in the imaging sub-study of PALETTE EORTC 62072/VEG110727: a randomized, double-blind, placebo-controlled phase III trial. Eur J Nucl Med Mol Imaging. 2015;42:848–57.
Garrett CR, Siu LL, El-Khoueiry A, Buter J, Rocha-Lima CM, Marshall J, et al. Phase I dose-escalation study to determine the safety, pharmacokinetics and pharmacodynamics of brivanib alaninate in combination with full-dose cetuximab in patients with advanced gastrointestinal malignancies who have failed prior therapy. Br J Cancer. 2011;105:44–52.
Haug AR, Auernhammer CJ, Wängler B, Schmidt GP, Uebleis C, Göke B, et al. 68Ga-DOTATATE PET/CT for the Early prediction of response to somatostatin receptor-mediated radionuclide therapy in patients with well-differentiated neuroendocrine tumors. J Nucl Med. 2010;51:1349–56.
Kitajima K, Miyoshi Y, Sekine T, Takei H, Ito K, Suto A, et al. Harmonized pretreatment quantitative volume-based FDG-PET/CT parameters for prognosis of stage I-III breast cancer: Multicenter study. Oncotarget. 2021;12:95–105.
Sevigny J, Chiao P, Bussière T, Weinreb PH, Williams L, Maier M, et al. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature. 2016;537:50–6.
Sevigny J, Suhy J, Chiao P, Chen T, Klein G, Purcell D, et al. Amyloid PET screening for enrichment of early-stage alzheimer disease clinical trials. Alzheimer Dis Assoc Disord. 2016;30:1–7.
Senda M, Ishii K, Ito K, Ikeuchi T, Matsuda H, Iwatsubo T, et al. A Japanese multicenter study on PET and other biomarkers for subjects with potential preclinical and prodromal Alzheimer’s disease. J Prev Alzheimers Dis. 2021;8:1–8.
Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations. Phys Med Biol. 2020;65:24TR02.
Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD, Jagust WJ, et al. The centiloid project: standardizing quantitative amyloid plaque estimation by PET. Alzheimer Dement. 2015;11:1–15.
Navitsky M, Joshi AD, Kennedy I, Klunk WE, Rowe CC, Wong DF, et al. Standardization of amyloid quantitation with florbetapir standardized uptake value ratios to the Centiloid scale. Alzheimer Dement. 2018;14:1565–71.
Battle MR, Pillay LC, Lowe VJ, Knopman D, Kemp B, Rowe CC, et al. Centiloid scaling for quantification of brain amyloid with [18F] flutemetamol using multiple processing methods. EJNMMI Res. 2018;8:107.
Rowe CC, Doré V, Jones G, Baxendale D, Mulligan RS, Bullich S, et al. 18F-Florbetaben PET beta-amyloid binding expressed in Centiloids. Eur J Nucl Med Mol Imaging. 2017;44:2053–9.
Yamao T, Miwa K, Wagatsuma K, Shigemoto Y, Sato N, Akamatsu G, et al. Centiloid scale analysis for 18F-THK5351 PET imaging in Alzheimer’s disease. Phys Med. 2021;82:249–54.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.
Orlhac F, Eertink JJ, Cottereau A-S, Zijlstra JM, Thieblemont C, Meignan M, et al. A guide to combat harmonization of imaging biomarkers in multicenter studies. J Nucl Med. 2022;63:172–9.
Dissaux G, Visvikis D, Da-ano R, Pradier O, Chajon E, Barillot I, et al. Pretreatment 18 F-FDG PET/CT radiomics predict local recurrence in patients treated with stereotactic body radiotherapy for early-stage non-small cell lung cancer: a multicentric study. J Nucl Med. 2020;61:814–20.
Hotta M, Minamimoto R, Gohda Y, Miwa K, Otani K, Kiyomatsu T, et al. Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med. 2021;35:843–52.
Nakamoto Y, Kitajima K, Toriihara A, Nakajo M, Hirata K. Recent topics of the clinical utility of PET/MRI in oncology and neuroscience. Ann Nucl Med. 2022;36:798–803.
Ito H, Kubo H, Takahashi K, Nishijima K-I, Ukon N, Nemoto A, et al. Integrated PET/MRI scanner with oxygen-15 labeled gases for quantification of cerebral blood flow, cerebral blood volume, cerebral oxygen extraction fraction and cerebral metabolic rate of oxygen. Ann Nucl Med. 2021;35:421–8.
Ratib O, Nkoulou R. Potential applications of PET/MR imaging in cardiology. J Nucl Med. 2014;55:40S-46S.
Zaidi H, Ojha N, Morich M, Griesmer J, Hu Z, Maniawski P, et al. Design and performance evaluation of a whole-body Ingenuity TF PET-MRI system. Phys Med Biol. 2011;56:3091–106.
Delso G, Fürst S, Jakoby B, Ladebeck R, Ganter C, Nekolla SG, et al. Performance measurements of the siemens mMR integrated whole-body PET/MR scanner. J Nucl Med. 2011;52:1914–22.
Grant AM, Deller TW, Khalighi MM, Maramraju SH, Delso G, Levin CS. NEMA NU 2–2012 performance studies for the SiPM-based ToF-PET component of the GE SIGNA PET/MR system. Med Phys. 2016;43:2334–43.
Chen S, Gu Y, Yu H, Chen X, Cao T, Hu L, et al. NEMA NU2-2012 performance measurements of the United Imaging uPMR790: an integrated PET/MR system. Eur J Nucl Med Mol Imaging. 2021;48:1726–35.
Wollenweber SD, Ambwani S, Lonn AHR, Shanbhag DD, Thiruvenkadam S, Kaushik S, et al. Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction. IEEE NSS/MIC Conf Rec. 2012;2012:3019–25.
Samarin A, Burger C, Wollenweber SD, Crook DW, Burger IA, Schmid DT, et al. PET/MR imaging of bone lesions – implications for PET quantification from imperfect attenuation correction. Eur J Nucl Med Mol Imaging. 2012;39:1154–60.
Aznar MC, Sersar R, Saabye J, Ladefoged CN, Andersen FL, Rasmussen JH, et al. Whole-body PET/MRI: the effect of bone attenuation during MR-based attenuation correction in oncology imaging. Eur J Radiol. 2014;83:1177–83.
Chen Y, An H. Attenuation correction of PET/MR imaging. Magn Reson Imaging Clin N Am. 2017;25:245–55.
Berker Y, Franke J, Salomon A, Palmowski M, Donker HCW, Temur Y, et al. MRI-based attenuation correction for hybrid PET/MRI systems: A 4-class tissue segmentation technique using a combined ultrashort-echo-time/dixon MRI sequence. J Nucl Med. 2012;53:796–804.
Sekine T, ter Voert EEGW, Warnock G, Buck A, Huellner M, Veit-Haibach P, et al. Clinical evaluation of zero-echo-time attenuation correction for brain 18F-FDG PET/MRI: Comparison with atlas attenuation correction. J Nucl Med. 2016;57:1927–32.
Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep Learning MR imaging–based attenuation correction for PET/MR imaging. Radiology. 2018;286:676–84.
Leynes AP, Yang J, Wiesinger F, Kaushik SS, Shanbhag DD, Seo Y, et al. Zero-echo-time and dixon deep pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J Nucl Med. 2018;59:852–8.
Hashimoto F, Ito M, Ote K, Isobe T, Okada H, Ouchi Y. Deep-learning-based attenuation correction for brain PET with various radiotracers. Ann Nucl Med. 2021;35:691–701.
Ziegler S, Braun H, Ritt P, Hocke C, Kuwert T, Quick HH. Systematic evaluation of phantom fluids for simultaneous PET/MR hybrid imaging. J Nucl Med. 2013;54:1464–71.
Boellaard R, Rausch I, Beyer T, Delso G, Yaqub M, Quick HH, et al. Quality control for quantitative multicenter whole-body PET/MR studies: a NEMA image quality phantom study with three current PET/MR systems. Med Phys. 2015;42:5961–9.
Ziegler S, Jakoby BW, Braun H, Paulus DH, Quick HH. NEMA image quality phantom measurements and attenuation correction in integrated PET/MR hybrid imaging. EJNMMI Physics. 2015;2:181–214.
Laforest R, Khalighi M, Natsuaki Y, Rajagopal A, Chandramohan D, Byrd D, et al. Harmonization of PET image reconstruction parameters in simultaneous PET/MRI. EJNMMI Physics. 2021;8:75.
Jentzen W, Phaosricharoen J, Gomez B, Hetkamp P, Stebner V, Binse I, et al. Quantitative performance of 124I PET/MR of neck lesions in thyroid cancer patients using 124I PET/CT as reference. EJNMMI Physics. 2018;5:13.
Ribeiro D, Hallett W, Tavares AAS. Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies. EJNMMI Phys. 2021;8:41.
Teuho J, Johansson J, Linden J, Hansen AE, Holm S, Keller SH, et al. Effect of attenuation correction on regional quantification between PET/MR and PET/CT: a multicenter study using a 3-dimensional brain phantom. J Nucl Med. 2016;57:818–24.
Lennie E, Tsoumpas C, Sourbron S. Multimodal phantoms for clinical PET/MRI. EJNMMI Physics. 2021;8:62.
Harries J, Jochimsen TH, Scholz T, Schlender T, Barthel H, Sabri O, et al. A realistic phantom of the human head for PET-MRI. EJNMMI Physics. 2020;7:52.
Rausch I, Valladares A, Sundar LKS, Beyer T, Hacker M, Meyerspeer M, et al. Standard MRI-based attenuation correction for PET/MRI phantoms: a novel concept using MRI-visible polymer. EJNMMI Physics. 2021;8:18.
Catana C, Laforest R, An H, Boada F, Cao T, Faul D, et al. A path to qualification of PET/MRI scanners for multicenter brain imaging studies: evaluation of MRI-based attenuation correction methods using a patient phantom. J Nucl Med. 2022;63:615–21.
Kato A, Nakamoto Y, Ishimori T, Hayakawa N, Ueda M, Temma T, et al. Diagnostic performance of 68Ga-DOTATOC PET/CT in tumor-induced osteomalacia. Ann Nucl Med. 2021;35:397–405.
Langbein T, Weber WA, Eiber M. Future of theranostics: an outlook on precision oncology in nuclear medicine. J Nucl Med. 2019;60:13S-19S.
Kömek H, Can C, Güzel Y, Oruç Z, Gündoğan C, Yildirim ÖA, et al. 68Ga-FAPI-04 PET/CT, a new step in breast cancer imaging: a comparative pilot study with the 18F-FDG PET/CT. Ann Nucl Med. 2021;35:744–52.
Donin NM, Reiter RE. Why targeting PSMA is a game changer in the management of prostate cancer. J Nucl Med. 2018;59:177–82.
Kratochwil C, Bruchertseifer F, Giesel FL, Weis M, Verburg FA, Mottaghy F, et al. 225Ac-PSMA-617 for PSMA-targeted α-radiation therapy of metastatic castration-resistant prostate cancer. J Nucl Med. 2016;57:1941–4.
Plyku D, Hobbs RF, Wu D, Garcia C, Sgouros G, van Nostrand D. I-124 PET/CT image-based dosimetry in patients with differentiated thyroid cancer treated with I-131: correlation of patient-specific lesional dosimetry to treatment response. Ann Nucl Med. 2022;36:213–23.
Jauw YWS, O’Donoghue JA, Zijlstra JM, Hoekstra OS, der Houven M-V, van Oordt CW, Morschhauser F, et al. 89Zr-immuno-PET: toward a noninvasive clinical tool to measure target engagement of therapeutic antibodies in vivo. J Nucl Med. 2019;60:1825–32.
Wu AM. Antibodies and antimatter: the resurgence of immuno-PET. J Nucl Med. 2009;50:2–5.
Conti M, Eriksson L. Physics of pure and non-pure positron emitters for PET: a review and a discussion. EJNMMI Physics. 2016;3:8.
Soderlund AT, Chaal J, Tjio G, Totman JJ, Conti M, Townsend DW. Beyond 18F-FDG: CHARACTERIZATION of PET/CT and PET/MR scanners for a comprehensive set of positron emitters of growing application—18F, 11C, 89Zr, 124I, 68Ga, and 90Y. J Nucl Med. 2015;56:1285–91.
Meng X, Liu H, Li H, Wang S, Sun H, Wang F, et al. Evaluating the impact of different positron emitters on the performance of a clinical PET/MR system. Med Phys. 2022;49:2642–51.
Selwyn RG, Nickles RJ, Thomadsen BR, DeWerd LA, Micka JA. A new internal pair production branching ratio of 90Y: The development of a non-destructive assay for 90Y and 90Sr. Appl Radiat Isot. 2007;65:318–27.
Moses WW. Fundamental limits of spatial resolution in PET. Nucl Instrum Methods Phys Res A. 2011;648:S236–40.
Carter LM, Kesner AL, Pratt EC, Sanders VA, Massicano AVF, Cutler CS, et al. The impact of positron range on PET resolution, evaluated with phantoms and PHITS monte carlo simulations for conventional and non-conventional radionuclides. Mol Imaging Biol. 2020;22:73–84.
Braune A, Oehme L, Freudenberg R, Hofheinz F, van den Hoff J, Kotzerke J, et al. Comparison of image quality and spatial resolution between 18F, 68Ga, and 64Cu phantom measurements using a digital Biograph Vision PET/CT. EJNMMI Physics. 2022;9:58.
Disselhorst JA, Brom M, Laverman P, Slump CH, Boerman OC, Oyen WJG, et al. Image-quality assessment for several positron emitters using the NEMA NU 4–2008 standards in the siemens inveon small-animal PET scanner. J Nucl Med. 2010;51:610–7.
Teuho J, Riehakainen L, Honkaniemi A, Moisio O, Han C, Tirri M, et al. Evaluation of image quality with four positron emitters and three preclinical PET/CT systems. EJNMMI Res. 2020;10:155.
Huizing DMV, Koopman D, van Dalen JA, Gotthardt M, Boellaard R, Sera T, et al. Multicentre quantitative 68Ga PET/CT performance harmonisation. EJNMMI Physics. 2019;6:19.
Makris NE, Boellaard R, Visser EP, de Jong JR, Vanderlinden B, Wierts R, et al. Multicenter harmonization of 89Zr PET/CT performance. J Nucl Med. 2014;55:264–7.
Kaalep A, Huisman M, Sera T, Vugts D, Boellaard R. Feasibility of PET/CT system performance harmonisation for quantitative multicentre 89Zr studies. EJNMMI Physics. 2018;5:26.
Christian PE, Williams S-P, Burrell L, Castaneda P, Albiani J, Sandella N, et al. Optimization of 89Zr PET imaging for improved multisite quantification and lesion detection using an anthropomorphic phantom. J Nucl Med Technol. 2020;48:54–7.
Bailey DL, Hofman MS, Forwood NJ, O’Keefe GJ, Scott AM, van Wyngaardt WM, et al. Accuracy of dose calibrators for 68Ga PET imaging: unexpected findings in a multicenter clinical pretrial assessment. J Nucl Med. 2018;59:636–8.
Sanderson T, Solomon J, Nottage C, Dickson J. Underestimation of 68Ga PET/CT SUV caused by activity overestimation using default calibrator settings. Phys Med. 2019;59:158–62.
Beattie BJ, Pentlow KS, O’Donoghue J, Humm JL. A Recommendation for revised dose calibrator measurement procedures for 89Zr and 124I. PLoS ONE. 2014;9:e106868.
Aide N, Lasnon C, Kesner A, Levin CS, Buvat I, Iagaru A, et al. New PET technologies–embracing progress and pushing the limits. Eur J Nucl Med Mol Imaging. 2021;48:2711–26.
de Jong TL, Koopman D, van Dalen JA, Tegelaar A, van Dijk JD, Stevens H, et al. Performance of digital PET/CT compared with conventional PET/CT in oncologic patients: a prospective comparison study. Ann Nucl Med. 2022;36:756–64.
Alberts I, Sachpekidis C, Prenosil G, Viscione M, Bohn KP, Mingels C, et al. Digital PET/CT allows for shorter acquisition protocols or reduced radiopharmaceutical dose in [18F]-FDG PET/CT. Ann Nucl Med. 2021;35:485–92.
Spencer BA, Berg E, Schmall JP, Omidvari N, Leung EK, Abdelhafez YG, et al. Performance evaluation of the uEXPLORER Total-Body PET/CT scanner based on NEMA NU 2–2018 with additional tests to characterize PET scanners with a long axial field of view. J Nucl Med. 2021;62:861–70.
Prenosil GA, Sari H, Fürstner M, Afshar-Oromieh A, Shi K, Rominger A, et al. Performance characteristics of the biograph vision quadra PET/CT system with a long axial field of view using the NEMA NU 2–2018 standard. J Nucl Med. 2022;63:476–84.
Akamatsu G, Takahashi M, Tashima H, Iwao Y, Yoshida E, Wakizaka H, et al. Performance evaluation of VRAIN: a brain-dedicated PET with a hemispherical detector arrangement. Phys Med Biol. 2022;67:225011.
Onishi Y, Isobe T, Ito M, Hashimoto F, Omura T, Yoshikawa E. Performance evaluation of dedicated brain PET scanner with motion correction system. Ann Nucl Med. 2022;36:746–55.
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Akamatsu, G., Tsutsui, Y., Daisaki, H. et al. A review of harmonization strategies for quantitative PET. Ann Nucl Med 37, 71–88 (2023). https://doi.org/10.1007/s12149-022-01820-x
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DOI: https://doi.org/10.1007/s12149-022-01820-x