Effects of cold sphere walls in PET phantom measurements on the volume reproducing threshold

Quantifying partial volume effect in SPECT and planar imaging: optimizing region of interest for activity concentration estimation in different sphere sizes

INTRODUCTION

To quantify the partial volume effect in single photon emission tomography (SPECT) and planar images of Carlson phantom as well as providing an optimum region of interest (ROI) required to more accurately estimate the activity concentration for different sphere sizes.

METHODS

131 I solution with the 161.16 kBq/ml concentration was uniformly filled into the different spheres of Carlson phantom (cold background condition) with the diameters of 7.3, 9.2, 11.4, 14.3, 17.9, 22.4 and 29.9 mm, and there was no background activity. In the hot background condition, the spheres were filled with the solution of 131 I with the 1276.5 kBq/ml addition to the background activity concentration of 161.16 kBq/ml in all the phantoms. The spheres were mounted inside the phantom and underwent SPECT and planar images. ROI was drawn closely on the boundary of each sphere image and it was extended to extract the true count.

RESULTS

In the cold background condition, the recovery coefficient (RC) value for SPECT images ranged between 0.8 and 1.03. However, in planar imaging, the RC value was 0.72 for the smallest sphere size and it increased for larger spheres until 0.98 for 29.9 mm. In the hot background condition, the RC value for sphere diameters larger than 20 mm was overestimated more than in the cold background condition. The ROI/size required to more accurately determine activity concentration for the cold background ranged from 1.18 to 2.7. However, in the hot background condition, this ratio varied from 1.34 to 4.05.

CONCLUSION

In the quantification of partial volume effects, the spill-out effect seems to play a crucial role in the distribution of the image counts beyond the boundaries of the image pixels. However, more investigations are needed to accurately characterize limitations regarding the object size, background levels, and other factors.

Gamma camera imaging characteristics of 166Ho and 99mTc used in Selective Internal Radiation Therapy

BACKGROUND

The administration of a 166Ho scout dose is available as an alternative to 99mTc particles for pre-treatment imaging in Selective Internal Radiation Therapy (SIRT). It has been reported that the 166Ho scout dose may be more accurate for the prediction of microsphere distribution and the associated therapy planning. The aim of the current study is to compare the scintigraphic imaging characteristics of both isotopes, considering the objectives of the pre-treatment imaging using clinically geared phantoms.

METHODS

Planar and SPECT/CT images were obtained using a NEMA image quality phantom in different phantom setups and another body-shaped phantom with several inserts. The influence of collimator type, count statistics, dead time effects, isotope properties and patient obesity on spatial resolution, contrast recovery and the detectability of small activity accumulations was investigated. Furthermore, the effects of the imaging characteristics on personalized dosimetry are discussed.

RESULTS

The images with 99mTc showed up to 3 mm better spatial resolution, up to two times higher contrast recovery and significantly lower image noise than those with 166Ho. The contrast-to-noise ratio was up to five times higher for 99mTc than for 166Ho. Only when using 99mTc all activity-filled spheres could be distinguished from the activity-filled background. The measurements mimicking an obese patient resulted in a degraded image quality for both isotopes.

CONCLUSIONS

Our measurements demonstrate better scintigraphic imaging properties for 99mTc compared to 166Ho in terms of spatial resolution, contrast recovery, image noise, and lesion detectability. While the 166Ho scout dose promises better prediction of the microsphere distribution, it is important to consider the inferior imaging characteristics of 166Ho, which may affect individualized treatment planning in SIRT.

Setup and characterisation according to NEMA NU 4 of thephenoPET scanner, a PET system dedicated for plant sciences

Objective.ThephenoPET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber. Here, we present the setup ofphenoPET, its data processing and image reconstruction together with its performance.Approach.The performance characterization follows the national electrical manufacturers association (NEMA) standard for small animal PET systems with a number of adoptions due to the vertical oriented bore of a PET for plant sciences. In addition temperature stability and spatial resolution with a hot rod phantom are addressed.Main results.The spatial resolution for a22Na point source at a radial distance of 5 mm to the center of the field-of-view (FOV) is 1.45 mm, 0.82 mm and 1.88 mm with filtered back projection in radial, tangential and axial direction, respectively. A hot rod phantom with18F gives a spatial resolution of up to 1.6 mm. The peak noise-equivalent count rates are 550 kcps @ 35.08 MBq, 308 kcps @ 33 MBq and 45 kcps @ 40.60 MBq for the mouse, rat and monkey size scatter phantoms, respectively. The scatter fractions for these phantoms are 12.63%, 22.64% and 55.90%. We observe a peak sensitivity of up to 3.6% and a total sensitivity of up toSA,tot= 2.17%. For the NEMA image quality phantom we observe a uniformity of %STD= 4.22% with ordinary Poisson maximum likelihood expectation-maximization with 52 iterations. Here, recovery coefficients of 0.12, 0.64, 0.89, 0.93 and 0.91 for 1 mm, 2 mm, 3 mm, 4 mm and 5 mm rods are obtained and spill-over ratios of 0.08 and 0.14 for the water-filled and air-filled inserts, respectively.Significance.ThephenoPET and its laboratory are now in routine operation for the administration of [11C]CO2and non-invasive measurement of transport and allocation of11C-labelled photoassimilates in plants.

A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [18F]FDG-PET/CT parameters

BACKGROUND

In patients with non-small cell lung cancer (NSCLC), accuracy of [18F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment.

METHODS

Monocentric retrospective analysis of pretherapeutic [18F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%).

RESULTS

N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM.

CONCLUSIONS

A machine learning model based on routinely available variables from [18F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available.

Comparison of image quality and spatial resolution between 18F, 68Ga, and 64Cu phantom measurements using a digital Biograph Vision PET/CT

BACKGROUND

PET nuclides can have a considerable influence on the spatial resolution and image quality of PET/CT scans, which can influence diagnostics in oncology, for example. The individual impact of the positron energy of ¹⁸F, ⁶⁸Ga, and ⁶⁴Cu on spatial resolution and image quality was compared for PET/CT scans acquired using a clinical, digital scanner.

METHODS

A Jaszczak phantom and a NEMA PET body phantom were filled with ¹⁸F-FDG, ⁶⁸Ga-HCl, or ⁶⁴Cu-HCl, and PET/CT scans were performed on a Siemens Biograph Vision. Acquired images were analyzed regarding spatial resolution and image quality (recovery coefficients (RC), coefficient of variation within the background, contrast recovery coefficient (CRC), contrast-noise ratio (CNR), and relative count error in the lung insert). Data were compared between scans with different nuclides.

RESULTS

We found that image quality was comparable between ¹⁸F-FDG and ⁶⁴Cu-HCl PET/CT measurements featuring similar maximal endpoint energies of the positrons. In comparison, RC, CRC, and CNR were degraded in ⁶⁸Ga-HCl data despite similar count rates. In particular, the two smallest spheres of 10 mm and 13 mm diameter revealed lower RC, CRC, and CNR values. The spatial resolution was similar between ¹⁸F-FDG and ⁶⁴Cu-HCl but up to 18% and 23% worse compared with PET/CT images of the NEMA PET body phantom filled with ⁶⁸Ga-HCl.

CONCLUSIONS

The positron energy of the PET nuclide influences the spatial resolution and image quality of a digital PET/CT scan. The image quality and spatial resolution of ⁶⁸Ga-HCl PET/CT images were worse than those of ¹⁸F-FDG or ⁶⁴Cu-HCl despite similar count rates.

EARL compliance measurements on the biograph vision Quadra PET/CT system with a long axial field of view

Background

Our aim was to determine sets of reconstruction parameters for the Biograph Vision Quadra (Siemens Healthineers) PET/CT system that result in quantitative images compliant with the European Association of Nuclear Medicine Research Ltd. (EARL) criteria. Using the Biograph Vision 600 (Siemens Healthineers) PET/CT technology but extending the axial field of view to 106 cm, gives the Vision Quadra currently an around fivefold higher sensitivity over the Vision 600 with otherwise comparable spatial resolution. Therefore, we also investigated how the number of incident positron decays—i.e., exposure—affects EARL compliance. This will allow estimating a minimal acquisition time or a minimal applied dose in clinical scans while retaining data comparability.

Methods

We measured activity recovery curves on a NEMA IEC body phantom filled with an aqueous ¹⁸F solution and a sphere to background ratio of 10–1 according to the latest EARL guidelines. Reconstructing 3570 image sets with varying OSEM PSF iterations, post-reconstruction Gaussian filter full width at half maximum (FWHM), and varying exposure from 59 kDecays/ml (= 3 s frame duration) to 59.2 MDecays/ml (= 1 h), allowed us to determine sets of parameters to achieve compliance with the current EARL 1 and EARL 2 standards. Recovery coefficients (RCs) were calculated for the metrics RC_max, RC_mean, and RC_peak, and the respective recovery curves were analyzed for monotonicity. The background’s coefficient of variation (COV) was also calculated.

Results

Using 6 iterations, 5 subsets and 7.8 mm Gauss filtering resulted in optimal EARL1 compliance and recovery curve monotonicity in all analyzed frames, except in the 3 s frames. Most robust EARL2 compliance and monotonicity were achieved with 2 iterations, 5 subsets, and 3.6 mm Gauss FWHM in frames with durations between 30 s and 10 min. RC_peak only impeded EARL2 compliance in the 10 s and 3 s frames.

Conclusions

While EARL1 compliance was robust over most exposure ranges, EARL2 compliance required exposures between 1.2 MDecays/ml to 11.5 MDecays/ml. The Biograph Vision Quadra’s high sensitivity makes frames as short as 10 s feasible for comparable quantitative images. Lowering EARL2 RC_max limits closer to unity would possibly even permit shorter frames.

Quantitative evaluation of PSMA PET imaging using a realistic anthropomorphic phantom and shell-less radioactive epoxy lesions

BACKGROUND

Positron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm-16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated.

RESULTS

The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM's PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined "apex"), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions [Formula: see text] 6 mm in diameter (R2 = 0.979-0.996 vs. R2 = 0.115-0.527, respectively).

CONCLUSION

An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200-400 and OSEM with 2-5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

Brain partial volume correction with point spreading function reconstruction in high-resolution digital PET: comparison with an MR-based method in FDG imaging

OBJECTIVE

In quantitative positron emission tomography (PET) of the brain, partial volume effect due mainly to the finite spatial resolution of the PET scanner (> 3 mm full width at half maximum [FWHM]) is a primary source of error in the measurement of tracer uptake, especially in small structures such as the cerebral cortex (typically < 3 mm thickness). The aim of this study was to evaluate the partial volume correction (PVC) performance of point spread function-incorporated reconstruction (PSF reconstruction) in combination with the latest digital PET scanner. This evaluation was performed through direct comparisons with magnetic resonance imaging (MR)-based PVC (used as a reference method) in a human brain study.

METHODS

Ten healthy subjects underwent brain ¹⁸F-FDG PET (30-min acquisition) on a digital PET/CT system (Siemens Biograph Vision, 3.5-mm FWHM scanner resolution at the center of the field of view) and anatomical T1-weighted MR imaging for MR-based PVC. PSF reconstruction was applied with a wide range of iterations (4 to 256; 5 subsets). FDG uptake in the cerebral cortex was evaluated using the standardized uptake value ratio (SUVR) and compared between PSF reconstruction and MR-based PVC.

RESULTS

Cortical structures were visualized by PSF reconstruction with several tens of iterations and were anatomically well matched with the MR-derived cortical segments. Higher numbers of iterations resulted in higher cortical SUVRs, which approached those of MR-based PVC (1.76), although even with the maximum number of iterations they were still smaller by 16% (1.47), corresponding to approximately 1.5-mm FWHM of the effective spatial resolution.

CONCLUSION

With the latest digital PET scanner, PSF reconstruction can be used as a PVC technique in brain PET, albeit with suboptimal resolution recovery. A relative advantage of PSF reconstruction is that it can be applied not only to cerebral cortical regions, but also to various small structures such as small brain nuclei that are hardly visualized on anatomical T1-weighted imaging, and thus hardly recovered by MR-based PVC.

KRAS mutation effects on the 2-[18F]FDG PET uptake of colorectal adenocarcinoma metastases in the liver

Background

Deriving individual tumor genomic characteristics from patient imaging analysis is desirable. We explore the predictive value of 2-[18F]FDG uptake with regard to the KRAS mutational status of colorectal adenocarcinoma liver metastases (CLM).

Methods

2-[18F]FDG PET/CT images, surgical pathology and molecular diagnostic reports of 37 patients who underwent PET/CT-guided biopsy of CLM were reviewed under an IRB-approved retrospective research protocol. Sixty CLM in 39 interventional PET scans of the 37 patients were segmented using two different auto-segmentation tools implemented in different commercially available software packages. PET standard uptake values (SUV) were corrected for: (1) partial volume effect (PVE) using cold wall-corrected contrast recovery coefficients derived from phantom spheres with variable diameter and (2) variability of arterial tracer supply and variability of uptake time after injection until start of PET scan derived from the tumor-to-blood standard uptake ratio (SUR) approach. The correlations between the KRAS mutational status and the mean, peak and maximum SUV were investigated using Student’s t test, Wilcoxon rank sum test with continuity correction, logistic regression and receiver operation characteristic (ROC) analysis. These correlation analyses were also performed for the ratios of the mean, peak and maximum tumor uptake to the mean blood activity concentration at the time of scan: SUR_MEAN, SUR_PEAK and SUR_MAX, respectively.

Results

Fifteen patients harbored KRAS missense mutations (KRAS+), while another 3 harbored KRAS gene amplification. For 31 lesions, the mutational status was derived from the PET/CT-guided biopsy. The Student’s t test p values for separating KRAS mutant cases decreased after applying PVE correction to all uptake metrics of each lesion and when applying correction for uptake time variability to the SUR metrics. The observed correlations were strongest when both corrections were applied to SUR_MAX and when the patients harboring gene amplification were grouped with the wild type: p ≤ 0.001; ROC area under the curve = 0.77 and 0.75 for the two different segmentations, respectively, with a mean specificity of 0.69 and sensitivity of 0.85.

Conclusion

The correlations observed after applying the described corrections show potential for assigning probabilities for the KRAS missense mutation status in CLM using 2-[18F]FDG PET images.

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

Background

Asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies.

Methods

This is a retrospective study of 50 patients with NSCLC (female 20; median age 69 years) undergoing pretherapeutic FDG-PET/CT (median 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF_4/16 (iterations 4; subsets 16; in-plane filter 2.0, 6.4 or 9.5 mm), TOF_4/8 (4 it; 8 ss; filter 2.0/6.0/9.5 mm), PSF + TOF_2/17 (2 it; 17 ss; filter 2.0/7.0/10.0 mm) or Bayesian-penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5-mm FWHM were retrospectively smoothed to achieve 7-mm FWHM. List mode data were rebinned for acquisition times of 120/90/60 s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases.

Results

Reconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF_4/8, PSF + TOF_2/17, Q.Clear and the reference algorithm TOF_4/16, ASP differences was lowest at FWHM of 7 versus 7 mm. Proportions of discordant cases (ASP > 19.5% vs. ≤ 19.5%) were also lowest at 7 mm (TOF_4/8, 2%; PSF + TOF_2/17, 4%; Q.Clear, 10%). Smoothing of 5-mm data to 7-mm FWHM significantly reduced discordant cases (TOF_4/8, 38% reduced to 2%; PSF + TOF_2/17, 12% to 4%; Q.Clear, 10% to 6%), resulting in proportions comparable to original 7-mm data. Shorter acquisition time only increased proportions of discordant cases at < 90 s.

Conclusions

ASP differences were mainly determined by reconstructed spatial resolution, and multicenter studies should aim at comparable FWHM (e.g., 7 mm; determined by in-plane filter width). This reduces discordant cases (high vs. low ASP) to an acceptable proportion for TOF and PSF + TOF of < 5% (Q.Clear: 10%). Data with better resolution (i.e., lower FWHM) could be retrospectively smoothed to the desired FWHM, resulting in a comparable number of discordant cases.

AAR-LN-DQ: Automatic anatomy recognition based disease quantification in thoracic lymph node zones via FDG PET/CT images without Nodal Delineation

PURPOSE

The derivation of quantitative information from medical images in a practical manner is essential for quantitative radiology (QR) to become a clinical reality, but still faces a major hurdle because of image segmentation challenges. With the goal of performing disease quantification in lymph node (LN) stations without explicit nodal delineation, this paper presents a novel approach for disease quantification (DQ) by automatic recognition of LN zones and detection of malignant lymph nodes within thoracic LN zones via positron emission tomography/computed tomography (PET/CT) images. Named AAR-LN-DQ, this approach decouples DQ methods from explicit nodal segmentation via an LN recognition strategy involving a novel globular filter and a deep neural network called SegNet.

METHOD

The methodology consists of four main steps: (a) Building lymph node zone models by automatic anatomy recognition (AAR) method. It incorporates novel aspects of model building that relate to finding an optimal hierarchy for organs and lymph node zones in the thorax. (b) Recognizing lymph node zones by the built lymph node models. (c) Detecting pathologic LNs in the recognized zones by using a novel globular filter (g-filter) and a multi-level support vector machine (SVM) classifier. Here, we make use of the general globular shape of LNs to first localize them and then use a multi-level SVM classifier to identify pathologic LNs from among the LNs localized by the g-filter. Alternatively, we designed a deep neural network called SegNet which is trained to directly recognize pathologic nodes within AAR localized LN zones. (d) Disease quantification based on identified pathologic LNs within localized zones. A fuzzy disease map is devised to express the degree of disease burden at each voxel within the identified LNs to simultaneously handle several uncertain phenomena such as PET partial volume effects, uncertainty in localization of LNs, and gradation of disease content at the voxel level. We focused on the task of disease quantification in patients with lymphoma based on PET/CT acquisitions and devised a method of evaluation. Model building was carried out using 42 near-normal patient datasets via contrast-enhanced CT examinations of their thorax. PET/CT datasets from an additional 63 lymphoma patients were utilized for evaluating the AAR-LN-DQ methodology. We assess the accuracy of the three main processes involved in AAR-LN-DQ via fivefold cross validation: lymph node zone recognition, abnormal lymph node localization, and disease quantification.

RESULTS

The recognition and scale error for LN zones were 12.28 mm ± 1.99 and 0.94 ± 0.02, respectively, on normal CT datasets. On abnormal PET/CT datasets, the sensitivity and specificity of pathologic LN recognition were 84.1% ± 0.115 and 98.5% ± 0.003, respectively, for the g-filter-SVM strategy, and 91.3% ± 0.110 and 96.1% ± 0.016, respectively, for the SegNet method. Finally, the mean absolute percent errors for disease quantification of the recognized abnormal LNs were 8% ± 0.09 and 14% ± 0.10 for the g-filter-SVM method and the best SegNet strategy, respectively.

CONCLUSIONS

Accurate disease quantification on PET/CT images without performing explicit delineation of lymph nodes is feasible following lymph node zone and pathologic LN localization. It is very useful to perform LN zone recognition by AAR as this step can cover most (95.8%) of the abnormal LNs and drastically reduce the regions to search for abnormal LNs. This also improves the specificity of deep networks such as SegNet significantly. It is possible to utilize general shape information about LNs such as their globular nature via g-filter and to arrive at high recognition rates for abnormal LNs in conjunction with a traditional classifier such as SVM. Finally, the disease map concept is effective for estimating disease burden, irrespective of how the LNs are identified, to handle various uncertainties without having to address them explicitly one by one.

3D printing of radioactive phantoms for nuclear medicine imaging

Background

For multicenter clinical studies, PET/CT and SPECT/CT scanners need to be validated to ensure comparability between various scanner types and brands. This validation is usually performed using hollow phantoms filled with radioactive liquids. In recent years, 3D printing technology has gained increasing popularity for manufacturing of phantoms, as it is cost-efficient and allows preparation of phantoms of almost any shape. So far, however, direct 3D printing with radioactive building materials has not yet been reported. The aim of this work was to develop a procedure for preparation of ^99mTc-containing building materials and demonstrate successful application of this material for 3D printing of several test objects.

Method

The desired activity of a [^99mTc]pertechnetate solution eluted from a ⁹⁹Mo/^99mTc-generator was added to the liquid 3D building material, followed by a minute amount of trioctylphosphine. The resulting two-phase mixture was thoroughly mixed. Following separation of the phases and chemical removal of traces of water, the radioactive building material was diluted with the required volume of non-radioactive building material and directly used for 3D printing.

Results

Using our optimized extraction protocol with trioctylphosphine as complex-forming phase transfer agent, technetium-99m was efficiently transferred from the aqueous ⁹⁹Mo/^99mTc-generator eluate into the organic liquid resin monomer. The observed radioactivity concentration ratio between the organic phase and the water phase was > 2000:1. The radioactivity was homogeneously distributed in the liquid resin monomer. We did not note differences in the 3D printing behavior of the radiolabeled and the unlabeled organic liquid resin monomers. Radio-TLC and SPECT studies showed homogenous 2D and 3D distribution of radioactivity throughout the printed phantoms. The radioactivity was stably bound in the resin, apart from a small amount of surface-extractable radioactivity under harsh conditions (ethanol at 50 °C).

Conclusions

3D printing of radioactive phantoms using ^99mTc-containing building materials is feasible. Compared to the classical fillable phantoms, 3D printing with radioactive building materials allows manufacturing of phantoms without cold walls and in almost any shape. Related procedures with longer-lived radionuclides will enable production of phantoms for scanner validation and quality control.

Comments on the NEMA NU 4-2008 Standard on Performance Measurement of Small Animal Positron Emission Tomographs

The National Electrical Manufacturers Association’s (NEMA) NU 4-2008 standard specifies methodology for evaluating the performance of small-animal PET scanners. The standard’s goal is to enable comparison of different PET scanners over a wide range of technologies and geometries used. In this work, we discuss if the NEMA standard meets these goals and we point out potential flaws and improvements to the standard.For the evaluation of spatial resolution, the NEMA standard mandates the use of filtered backprojection reconstruction. This reconstruction method can introduce star-like artifacts for detectors with an anisotropic spatial resolution, usually caused by parallax error. These artifacts can then cause a strong dependence of the resulting spatial resolution on the size of the projection window in image space, whose size is not fully specified in the NEMA standard. If the PET ring has detectors which are perpendicular to a Cartesian axis, then the resolution along this axis will typically improve with larger projection windows.We show that the standard’s equations for the estimation of the random rate for PET systems with intrinsic radioactivity are circular and not satisfiable. However, a modified version can still be used to determine an approximation of the random rates under the assumption of negligible random rates for small activities and a constant scatter fraction. We compare the resulting estimated random rates to random rates obtained using a delayed coincidence window and two methods based on the singles rates. While these methods give similar estimates, the estimation method based on the NEMA equations overestimates the random rates.In the NEMA standard’s protocol for the evaluation of the sensitivity, the standard specifies to axially step a point source through the scanner and to take a different scan for each source position. Later, in the data analysis section, the standard does not specify clearly how the different scans have to be incorporated into the analysis, which can lead to unclear interpretations of publicized results.The standard’s definition of the recovery coefficients in the image quality phantom includes the maximum activity in a region of interest, which causes a positive correlation of noise and recovery coefficients. This leads to an unintended trade-off between desired uniformity, which is negatively correlated with variance (i.e., noise), and recovery.With this work, we want to start a discussion on possible improvements in a next version of the NEMA NU-4 standard.

Reconstructed spatial resolution and contrast recovery with Bayesian penalized likelihood reconstruction (Q.Clear) for FDG-PET compared to time-of-flight (TOF) with point spread function (PSF)

Background

Bayesian penalized likelihood reconstruction for PET (e.g., GE Q.Clear) aims at improving convergence of lesion activity while ensuring sufficient signal-to-noise ratio (SNR). This study evaluated reconstructed spatial resolution, maximum/peak contrast recovery (CRmax/CRpeak) and SNR of Q.Clear compared to time-of-flight (TOF) OSEM with and without point spread function (PSF) modeling.

Methods

The NEMA IEC Body phantom was scanned five times (3 min scan duration, 30 min between scans, background, 1.5–3.9 kBq/ml F18) with a GE Discovery MI PET/CT (3-ring detector) with spheres filled with 8-, 4-, or 2-fold the background activity concentration (SBR 8:1, 4:1, 2:1). Reconstruction included Q.Clear (beta, 150/300/450), “PSF+TOF_4/16” (iterations, 4; subsets, 16; in-plane filter, 2.0 mm), “OSEM+TOF_4/16” (identical parameters), “PSF+TOF_2/17” (2 it, 17 ss, 2.0 mm filter), “OSEM+TOF_2/17” (identical), “PSF+TOF_4/8” (4 it, 8 ss, 6.4 mm), and “OSEM+TOF_2/8” (2 it, 8 ss, 6.4 mm). Spatial resolution was derived from 3D sphere activity profiles. RC as (sphere activity concentration [AC]/true AC). SNR as (background mean AC/background AC standard deviation).

Results

Spatial resolution of Q.Clear₁₅₀ was significantly better than all conventional algorithms at SBR 8:1 and 4:1 (Wilcoxon, each p < 0.05). At SBR 4:1 and 2:1, the spatial resolution of Q.Clear_300/450 was similar or inferior to PSF+TOF_4/16 and OSEM+TOF_4/16. Small sphere CRpeak generally underestimated true AC, and it was similar for Q.Clear_150/300/450 as with PSF+TOF_4/16 or PSF+TOF_2/17 (i.e., relative differences < 10%). Q.Clear provided similar or higher CRpeak as OSEM+TOF_4/16 and OSEM+TOF_2/17 resulting in a consistently better tradeoff between CRpeak and SNR with Q.Clear. Compared to PSF+TOF_4/8/OSEM+TOF_2/8, Q.Clear_150/300/450 showed lower SNR but higher CRpeak.

Conclusions

Q.Clear consistently improved reconstructed spatial resolution at high and medium SBR compared to PSF+TOF and OSEM+TOF, but only with beta = 150. However, this is at the cost of inferior SNR with Q.Clear₁₅₀ compared to Q.Clear_300/450 and PSF+TOF_4/16/PSF+TOF_2/17 while CRpeak for the small spheres did not improve considerably. This suggests that Q.Clear_300/450 may be advantageous for the 3-ring detector configuration because the tradeoff between CR and SNR with Q.Clear_300/450 was superior to PSF+TOF_4/16, OSEM+TOF_4/16, and OSEM+TOF_2/17. However, it requires validation by systematic evaluation in patients at different activity and acquisition protocols.

The impact of iterative reconstruction protocol, signal-to-background ratio and background activity on measurement of PET spatial resolution

OBJECTIVES

The present study aims to assess the impact of acquisition time, different iterative reconstruction protocols as well as image context (including contrast levels and background activities) on the measured spatial resolution in PET images.

METHODS

Discovery 690 PET/CT scanner was used to quantify spatial resolutions in terms of full width half maximum (FWHM) as derived (i) directly from capillary tubes embedded in air and (ii) indirectly from 10 mm-diameter sphere of the NEMA phantom. Different signal-to-background ratios (SBRs), background activity levels and acquisition times were applied. The emission data were reconstructed using iterative reconstruction protocols. Various combinations of iterations and subsets (it × sub) were evaluated.

RESULTS

For capillary tubes, improved FWHM values were obtained for higher it × sub, with improved performance for PSF algorithms relative to non-PSF algorithms. For the NEMA phantom, by increasing acquisition times from 1 to 5 min, intrinsic FWHM for reconstructions with it × sub 32 (54) was improved by 15.3% (13.2%), 15.1% (13.8%), 14.5% (12.8%) and 13.7% (12.7%) for OSEM, OSEM + PSF, OSEM + TOF and OSEM + PSF + TOF, respectively. Furthermore, for all reconstruction protocols, the FWHM improved with more impact for higher it × sub.

CONCLUSION

Our results indicate that PET spatial resolution is greatly affected by SBR, background activity and the choice of the reconstruction protocols.

Resolution modeling in projection space using a factorized multi-block detector response function for PET image reconstruction

Positron emission tomography (PET) images usually suffer from limited resolution and statistical uncertainties. However, a technique known as resolution modeling (RM) can be used to improve image quality by accurately modeling the system's detection process within the iterative reconstruction. In this study, we present an accurate RM method in projection space based on a simulated multi-block detector response function (DRF) and evaluate it on the Siemens hybrid MR-BrainPET system. The DRF is obtained using GATE simulations that consider nearly all the possible annihilation photons from the field-of-view (FOV). Intrinsically, the multi-block DRF allows the block crosstalk to be modeled. The RM blurring kernel is further generated by factorizing the blurring matrix of one line-of-response (LOR) into two independent detector responses, which can then be addressed with the DRF. Such a kernel is shift-variant in 4D projection space without any distance or angle compression, and is integrated into the image reconstruction for the BrainPET insert with single instruction multiple data (SIMD) and multi-thread support. Evaluation of simulations and measured data demonstrate that the reconstruction with RM yields significantly improved resolutions and reduced mean squared error (MSE) values at different locations of the FOV, compared with reconstruction without RM. Furthermore, the shift-variant RM kernel models the varying blurring intensity for different LORs due to the depth-of-interaction (DOI) dependencies, thus avoiding severe edge artifacts in the images. Additionally, compared to RM in single-block mode, the multi-block mode shows significantly improved resolution and edge recovery at locations beyond 10 cm from the center of BrainPET insert in the transverse plane. However, the differences have been observed to be low for patient data between single-block and multi-block mode RM, due to the brain size and location as well as the geometry of the BrainPET insert. In conclusion, the RM method proposed in this study can yield better reconstructed images in terms of resolution and MSE value, compared to conventional reconstruction without RM.

Quantification of global lung inflammation using volumetric 18F-FDG PET/CT parameters in locally advanced non-small-cell lung cancer patients treated with concurrent chemoradiotherapy: a comparison of photon and proton radiation therapy

INTRODUCTION

Radiation pneumonitis is a major dose-limiting complication in thoracic radiation therapy (RT) and presents clinically in the first few months after RT. We evaluated the feasibility of quantifying pulmonary parenchymal glycolysis (PG) as a surrogate of global lung inflammation and radiation-induced pulmonary toxicity using a novel semiautomatic lung segmentation technique in non-small-cell lung cancer (NSCLC) patients and compared PG in patients treated with photon or proton RT.

PATIENTS AND METHODS

We evaluated 18 consecutive locally advanced NSCLC patients who underwent pretreatment and post-treatment F-FDG PET/CT treated with definitive (median: 66.6 Gy; 1.8 Gy fractions) photon or proton RT between 2010 and 2014. Lung volume segmentation was conducted using 3D Slicer by performing simple thresholding. Pulmonary PG was calculated by summing F-FDG uptake in the whole lung.

RESULTS

In nine patients treated with photon RT, significant increases in PG in both ipsilateral (mean difference: 1400±510; P=0.02) and contralateral (mean difference: 1200±450; P=0.03) lungs were noted. In nine patients treated with proton therapy, no increase in pulmonary PG was observed in either the ipsilateral (P=0.30) or contralateral lung (P=0.98).

CONCLUSION

We observed a significant increase in global lung inflammation bilaterally as measured by quantification of PG. However, no significant change in global lung inflammation was noted after proton therapy. Future larger studies are needed to determine whether this difference correlates with lower risks of radiation pneumonitis in NSCLC patients treated with proton therapy.

Correlation between FMISO-PET based hypoxia in the primary tumour and in lymph node metastases in locally advanced HNSCC patients

•

We investigated correlation between hypoxia in the primary tumour and LN before and during RCTx.

•

The Correlation between primary tumour and LN hypoxia is stronger in patients with large LN compared to the entire cohort.

•

We advise to perform a comprehensive evaluation of hypoxia in the primary tumour and LN.

Purpose

This secondary analysis of the prospective study on repeat [¹⁸F]fluoromisonidazole (FMISO)-PET in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) assessed the correlation of hypoxia in the primary tumour and lymph node metastases (LN) prior to and during primary radiochemotherapy.

Methods

This analysis included forty-five LN-positive HNSCC patients having undergone FMISO-PET/CTs at baseline, and at week 1, 2 and 5 of radiochemotherapy. The quantitative FMISO-PET/CT parameters maximum standardised uptake value (SUV_max, corrected for partial volume effect) and peak tumour-to-background ratio (TBR_peak) were estimated in the primary tumour as well as in index and large LN, respectively. Statistical analysis was performed using the Spearman correlation coefficient ρ.

Results

In 15 patients with large LN (FDG-PET positive volume >5 ml), there was a significant correlation between the hypoxia measured in the primary tumour and the large LN at three out of four time-points using the TBR_peak (baseline: ρ = 0.57, p = 0.006; week 2: ρ = 0.64, p = 0.003 and week 5: ρ = 0.68, p = 0.001). For the entire cohort (N = 45) only assessed prior to the treatment, there was a statistically significant, though weak correlation between FMISO-SUV_max of the primary tumour and the index LN (ρ = 0.36, p = 0.015).

Conclusions

We observed a significant correlation between FMISO-based hypoxia in the primary tumour and large lymph node(s) in advanced stage HNSCC patients. However, since most patients only had relatively small hypoxic lymph node metastases, a comprehensive assessment of the primary tumour and lymph node hypoxia is essential.

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-d-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R² = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy.

Disease quantification on PET/CT images without explicit object delineation

PURPOSE

The derivation of quantitative information from images in a clinically practical way continues to face a major hurdle because of image segmentation challenges. This paper presents a novel approach, called automatic anatomy recognition-disease quantification (AAR-DQ), for disease quantification (DQ) on positron emission tomography/computed tomography (PET/CT) images. This approach explores how to decouple DQ methods from explicit dependence on object (e.g., organ) delineation through the use of only object recognition results from our recently developed automatic anatomy recognition (AAR) method to quantify disease burden.

METHOD

The AAR-DQ process starts off with the AAR approach for modeling anatomy and automatically recognizing objects on low-dose CT images of PET/CT acquisitions. It incorporates novel aspects of model building that relate to finding an optimal disease map for each organ. The parameters of the disease map are estimated from a set of training image data sets including normal subjects and patients with metastatic cancer. The result of recognition for an object on a patient image is the location of a fuzzy model for the object which is optimally adjusted for the image. The model is used as a fuzzy mask on the PET image for estimating a fuzzy disease map for the specific patient and subsequently for quantifying disease based on this map. This process handles blur arising in PET images from partial volume effect entirely through accurate fuzzy mapping to account for heterogeneity and gradation of disease content at the voxel level without explicitly performing correction for the partial volume effect. Disease quantification is performed from the fuzzy disease map in terms of total lesion glycolysis (TLG) and standardized uptake value (SUV) statistics. We also demonstrate that the method of disease quantification is applicable even when the "object" of interest is recognized manually with a simple and quick action such as interactively specifying a 3D box ROI. Depending on the degree of automaticity for object and lesion recognition on PET/CT, DQ can be performed at the object level either semi-automatically (DQ-MO) or automatically (DQ-AO), or at the lesion level either semi-automatically (DQ-ML) or automatically.

RESULTS

We utilized 67 data sets in total: 16 normal data sets used for model building, and 20 phantom data sets plus 31 patient data sets (with various types of metastatic cancer) used for testing the three methods DQ-AO, DQ-MO, and DQ-ML. The parameters of the disease map were estimated using the leave-one-out strategy. The organs of focus were left and right lungs and liver, and the disease quantities measured were TLG, SUV_Mean, and SUV_Max. On phantom data sets, overall error for the three parameters were approximately 6%, 3%, and 0%, respectively, with TLG error varying from 2% for large "lesions" (37 mm diameter) to 37% for small "lesions" (10 mm diameter). On patient data sets, for non-conspicuous lesions, those overall errors were approximately 19%, 14% and 0%; for conspicuous lesions, these overall errors were approximately 9%, 7%, 0%, respectively, with errors in estimation being generally smaller for liver than for lungs, although without statistical significance.

CONCLUSIONS

Accurate disease quantification on PET/CT images without performing explicit delineation of lesions is feasible following object recognition. Method DQ-MO generally yields more accurate results than DQ-AO although the difference is statistically not significant. Compared to current methods from the literature, almost all of which focus only on lesion-level DQ and not organ-level DQ, our results were comparable for large lesions and were superior for smaller lesions, with less demand on training data and computational resources. DQ-AO and even DQ-MO seem to have the potential for quantifying disease burden body-wide routinely via the AAR-DQ approach.

A prospective study of the feasibility of FDG-PET/CT imaging to quantify radiation-induced lung inflammation in locally advanced non-small cell lung cancer patients receiving proton or photon radiotherapy

PURPOSE

This prospective study assessed the feasibility of ¹⁸F-2-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) to quantify radiation-induced lung inflammation in patients with locally advanced non-small cell lung cancer (NSCLC) who received radiotherapy (RT), and compared the differences in inflammation in the ipsilateral and contralateral lungs following proton and photon RT.

METHODS

Thirty-nine consecutive patients with NSCLC underwent FDG-PET/CT imaging before and after RT on a prospective study. A novel quantitative approach utilized regions of interest placed around the anatomical boundaries of the lung parenchyma and provided lung mean standardized uptake value (SUVmean), global lung glycolysis (GLG), global lung parenchymal glycolysis (GLPG) and total lung volume (LV). To quantify primary tumor metabolic response to RT, an adaptive contrast-oriented thresholding algorithm was applied to measure metabolically active tumor volume (MTV), tumor uncorrected SUVmean, tumor partial volume corrected SUVmean (tumor-PVC-SUVmean), and total lesion glycolysis (TLG). Parameters of FDG-PET/CT scans before and after RT were compared using two-tailed paired t-tests.

RESULTS

All tumor parameters after either proton or photon RT decreased significantly (p < 0.001). Among the 21 patients treated exclusively with proton RT, no significant increase in PVC-SUVmean or PVC-GLPG was observed in ipsilateral lungs after the PVC parameters of primary tumor were subtracted (p = 0.114 and p = 0.453, respectively). Also, there were no significant increases in SUVmean or GLG of contralateral lungs of patients who received proton RT (p = 0.841, p = 0.241, respectively). In contrast, among the nine patients who received photon RT, there was a statistically significant increase in PVC-GLPG of ipsilateral lung (p < 0.001) and in GLG of contralateral (p = 0.036) lung. In the subset of nine patients who received a combined proton and photon RT, there was a statistically significant increase in PVC-GLPG of ipsilateral lung (p < 0.001).

CONCLUSION

Our data suggest less induction of inflammatory response in both the ipsilateral and contralateral lungs of patients treated with proton compared to photon or combined proton-photon RT.

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R² = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning.

FDG PET/CT in cancer therapy monitoring

Objectives: We developed and tested a software tool for computer-assisted analysis of FDG-PET/CT in cancer therapy monitoring. The tool provides automatic semi-quantitative analysis of a baseline scan together with up to two follow-up scans (standardized uptake values, glycolytic volume). The tool also supports visual analysis by local spatial registration which allows display of tumor lesions with the same orientation in all scans. The tool’s stability and accuracy was tested at typical everyday image quality. Patients, methods: Ten unselected cancer patients in whom three FDG PET/CT scans had been performed were included. A total of 18 lesions were analyzed. Results: Automatic lesion tracking worked properly in all lesions but one. In this lesion local coregistration had to be adjusted manually tuwhich, however, is easily performed with the tool. Semi-automatic lesion segmentation and fully automatic semi-quantitative analysis worked properly in all cases. Computer-assisted analysis was significantly less time consuming than manual analysis. Conclusions: The novel software tool appears useful for analysis of FDGPET/ CT in cancer therapy monitoring in clinical routine patient care.

Quantitative performance evaluation of 124I PET/MRI lesion dosimetry in differentiated thyroid cancer

The aim was to investigate the quantitative performance of ¹²⁴I PET/MRI for pre-therapy lesion dosimetry in differentiated thyroid cancer (DTC). Phantom measurements were performed on a PET/MRI system (Biograph mMR, Siemens Healthcare) using ¹²⁴I and ¹⁸F. The PET calibration factor and the influence of radiofrequency coil attenuation were determined using a cylindrical phantom homogeneously filled with radioactivity. The calibration factor was 1.00  ±  0.02 for ¹⁸F and 0.88  ±  0.02 for ¹²⁴I. Near the radiofrequency surface coil an underestimation of less than 5% in radioactivity concentration was observed. Soft-tissue sphere recovery coefficients were determined using the NEMA IEC body phantom. Recovery coefficients were systematically higher for ¹⁸F than for ¹²⁴I. In addition, the six spheres of the phantom were segmented using a PET-based iterative segmentation algorithm. For all ¹²⁴I measurements, the deviations in segmented lesion volume and mean radioactivity concentration relative to the actual values were smaller than 15% and 25%, respectively. The effect of MR-based attenuation correction (three- and four-segment µ-maps) on bone lesion quantification was assessed using radioactive spheres filled with a K₂HPO₄ solution mimicking bone lesions. The four-segment µ-map resulted in an underestimation of the imaged radioactivity concentration of up to 15%, whereas the three-segment µ-map resulted in an overestimation of up to 10%. For twenty lesions identified in six patients, a comparison of ¹²⁴I PET/MRI to PET/CT was performed with respect to segmented lesion volume and radioactivity concentration. The interclass correlation coefficients showed excellent agreement in segmented lesion volume and radioactivity concentration (0.999 and 0.95, respectively). In conclusion, it is feasible that accurate quantitative ¹²⁴I PET/MRI could be used to perform radioiodine pre-therapy lesion dosimetry in DTC.

The first MICCAI challenge on PET tumor segmentation

INTRODUCTION

Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge.

MATERIALS AND METHODS

Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n = 19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n = 157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value.

RESULTS

Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods.

CONCLUSION

The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.

A Study of the Feasibility of FDG-PET/CT to Systematically Detect and Quantify Differential Metabolic Effects of Chronic Tobacco Use in Organs of the Whole Body-A Prospective Pilot Study

RATIONALE AND OBJECTIVES

The aim of this study was to assess the feasibility of ¹⁸F-fluorodeoxyglucose (FDG)-positron emission tomography/computed tomography (PET/CT) to systematically detect and quantify differential effects of chronic tobacco use in organs of the whole body.

MATERIALS AND METHODS

Twenty healthy male subjects (10 nonsmokers and 10 chronic heavy smokers) were enrolled. Subjects underwent whole-body FDG-PET/CT, diagnostic unenhanced chest CT, mini-mental state examination, urine testing for oxidative stress, and serum testing. The organs of interest (thyroid, skin, skeletal muscle, aorta, heart, lung, adipose tissue, liver, spleen, brain, lumbar spinal bone marrow, and testis) were analyzed on FDG-PET/CT images to determine their metabolic activities using standardized uptake value (SUV) or metabolic volumetric product (MVP). Measurements were compared between subject groups using two-sample t tests or Wilcoxon rank-sum tests as determined by tests for normality. Correlational analyses were also performed.

RESULTS

FDG-PET/CT revealed significantly decreased metabolic activity of lumbar spinal bone marrow (MVPmean: 29.8 ± 9.7 cc vs 40.8 ± 11.6 cc, P = 0.03) and liver (SUVmean: 1.8 ± 0.2 vs 2.0 ± 0.2, P = 0.049) and increased metabolic activity of visceral adipose tissue (SUVmean: 0.35 ± 0.10 vs 0.26 ± 0.06, P = 0.02) in chronic smokers compared to nonsmokers. Normalized visceral adipose tissue volume was also significantly decreased (P = 0.04) in chronic smokers. There were no statistically significant differences in the metabolic activity of other assessed organs.

CONCLUSIONS

Subclinical organ effects of chronic tobacco use are detectable and quantifiable on FDG-PET/CT. FDG-PET/CT may, therefore, play a major role in the study of systemic toxic effects of tobacco use in organs of the whole body for clinical or research purposes.

Simulating effects of brain atrophy in longitudinal PET imaging with an anthropomorphic brain phantom

In longitudinal positron emission tomography (PET), the presence of volumetric changes over time can lead to an overestimation or underestimation of the true changes in the quantified PET signal due to the partial volume effect (PVE) introduced by the limited spatial resolution of existing PET cameras and reconstruction algorithms. Here, a 3D-printed anthropomorphic brain phantom with attachable striata in three sizes was designed to enable controlled volumetric changes. Using a method to eliminate the non-radioactive plastic wall, and manipulating BP levels by adding different number of events from list-mode acquisitions, we investigated the artificial volume dependence of BP due to PVE, and potential bias arising from varying BP. Comparing multiple reconstruction algorithms we found that a high-resolution ordered-subsets maximization algorithm with spatially variant point-spread function resolution modeling provided the most accurate data. For striatum, the BP changed by 0.08% for every 1% volume change, but for smaller volumes such as the posterior caudate the artificial change in BP was as high as 0.7% per 1% volume change. A simple gross correction for striatal volume is unsatisfactory, as the amplitude of the PVE on the BP differs depending on where in the striatum the change occurred. Therefore, to correctly interpret age-related longitudinal changes in the BP, we must account for volumetric changes also within a structure, rather than across the whole volume. The present 3D-printing technology, combined with the wall removal method, can be implemented to gain knowledge about the predictable bias introduced by the PVE differences in uptake regions of varying shape.

Optimisation of reconstruction, volumetry and dosimetry for 99mTc-SPECT and 90Y-PET images: Towards reliable dose-volume histograms for selective internal radiation therapy with 90Y-microspheres

PURPOSE

In Selective Internal Radiation Therapy (SIRT), ^99mTc-MAA SPECT images are commonly used to predict microspheres distribution but recent works used ⁹⁰Y-microspheres PET images. Nevertheless, evaluation of the predictive power of ^99mTc-MAA has been hampered by the lack of reliable comparisons between ^99mTc-SPECT and ⁹⁰Y-PET images. Our aim was to determine the "in situ" optimisation procedure in order to reliably compare ^99mTc-SPECT and ⁹⁰Y-PET images and achieve optimal personal dosimetry.

METHODS

We acquired ^99mTc-SPECT/CT and ⁹⁰Y-PET/CT images of NEMA and Jaszczak phantoms. We found the best reconstruction parameters for quantification and for volume estimations. We determined adaptive threshold curves on the volumetric reconstruction. We copied the optimised volumes on the quantitative reconstruction, named here the "cross volumes" technique. Finally, we compared ^99mTc-SPECT and ⁹⁰Y-PET Dose Volume Histograms.

RESULTS

Our "in situ" optimisation procedure decreased errors on volumes and quantification (from -44.2% and -15.8% to -3.4% and -3.28%, respectively, for the 26.5mL PET phantom sphere). Moreover, ^99mTc-SPECT and ⁹⁰Y-PET DVHs were equivalent only after the optimisation procedure (difference in mean dose <5% for the three biggest spheres).

CONCLUSIONS

This work showed that a preliminary "in situ" phantom study was necessary to optimise volumes and quantification of ^99mTc-SPECT and ⁹⁰Y-PET images and allowed to achieve a reliable comparison between patient treatment planning and post implant dosimetry, notably by the use of the "cross volumes" technique. Methodology developed in this work will enable robust evaluations of the predictive power of ^99mTc-SPECT, as well as dose-response relationship and side effects in SIRT treatments.

Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data

PURPOSE

Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making.

METHODS

To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis.

RESULTS

On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively.

CONCLUSION

Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.

A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions

PURPOSE

The aim of this study was to implement a Dirichlet process mixture (DPM) model for automatic tumor edge identification on (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) images by optimizing the parameters on which the algorithm depends, to validate it experimentally, and to test its robustness.

METHODS

The DPM model belongs to the class of the Bayesian nonparametric models and uses the Dirichlet process prior for flexible nonparametric mixture modeling, without any preliminary choice of the number of mixture components. The DPM algorithm implemented in the statistical software package R was used in this work. The contouring accuracy was evaluated on several image data sets: on an IEC phantom (spherical inserts with diameter in the range 10-37 mm) acquired by a Philips Gemini Big Bore PET-CT scanner, using 9 different target-to-background ratios (TBRs) from 2.5 to 70; on a digital phantom simulating spherical/uniform lesions and tumors, irregular in shape and activity; and on 20 clinical cases (10 lung and 10 esophageal cancer patients). The influence of the DPM parameters on contour generation was studied in two steps. In the first one, only the IEC spheres having diameters of 22 and 37 mm and a sphere of the digital phantom (41.6 mm diameter) were studied by varying the main parameters until the diameter of the spheres was obtained within 0.2% of the true value. In the second step, the results obtained for this training set were applied to the entire data set to determine DPM based volumes of all available lesions. These volumes were compared to those obtained by applying already known algorithms (Gaussian mixture model and gradient-based) and to true values, when available.

RESULTS

Only one parameter was found able to significantly influence segmentation accuracy (ANOVA test). This parameter was linearly connected to the uptake variance of the tested region of interest (ROI). In the first step of the study, a calibration curve was determined to automatically generate the optimal parameter from the variance of the ROI. This "calibration curve" was then applied to contour the whole data set. The accuracy (mean discrepancy between DPM model-based contours and reference contours) of volume estimation was below (1 ± 7)% on the whole data set (1 SD). The overlap between true and automatically segmented contours, measured by the Dice similarity coefficient, was 0.93 with a SD of 0.03.

CONCLUSIONS

The proposed DPM model was able to accurately reproduce known volumes of FDG concentration, with high overlap between segmented and true volumes. For all the analyzed inserts of the IEC phantom, the algorithm proved to be robust to variations in radius and in TBR. The main advantage of this algorithm was that no setting of DPM parameters was required in advance, since the proper setting of the only parameter that could significantly influence the segmentation results was automatically related to the uptake variance of the chosen ROI. Furthermore, the algorithm did not need any preliminary choice of the optimum number of classes to describe the ROIs within PET images and no assumption about the shape of the lesion and the uptake heterogeneity of the tracer was required.

SPEQTACLE: An automated generalized fuzzy C-means algorithm for tumor delineation in PET

PURPOSE

Accurate tumor delineation in positron emission tomography (PET) images is crucial in oncology. Although recent methods achieved good results, there is still room for improvement regarding tumors with complex shapes, low signal-to-noise ratio, and high levels of uptake heterogeneity.

METHODS

The authors developed and evaluated an original clustering-based method called spatial positron emission quantification of tumor-Automatic Lp-norm estimation (SPEQTACLE), based on the fuzzy C-means (FCM) algorithm with a generalization exploiting a Hilbertian norm to more accurately account for the fuzzy and non-Gaussian distributions of PET images. An automatic and reproducible estimation scheme of the norm on an image-by-image basis was developed. Robustness was assessed by studying the consistency of results obtained on multiple acquisitions of the NEMA phantom on three different scanners with varying acquisition parameters. Accuracy was evaluated using classification errors (CEs) on simulated and clinical images. SPEQTACLE was compared to another FCM implementation, fuzzy local information C-means (FLICM) and fuzzy locally adaptive Bayesian (FLAB).

RESULTS

SPEQTACLE demonstrated a level of robustness similar to FLAB (variability of 14% ± 9% vs 14% ± 7%, p = 0.15) and higher than FLICM (45% ± 18%, p < 0.0001), and improved accuracy with lower CE (14% ± 11%) over both FLICM (29% ± 29%) and FLAB (22% ± 20%) on simulated images. Improvement was significant for the more challenging cases with CE of 17% ± 11% for SPEQTACLE vs 28% ± 22% for FLAB (p = 0.009) and 40% ± 35% for FLICM (p < 0.0001). For the clinical cases, SPEQTACLE outperformed FLAB and FLICM (15% ± 6% vs 37% ± 14% and 30% ± 17%, p < 0.004).

CONCLUSIONS

SPEQTACLE benefitted from the fully automatic estimation of the norm on a case-by-case basis. This promising approach will be extended to multimodal images and multiclass estimation in future developments.

On the Reliability of Automatic Volume Delineation in Low-Contrast [(18)F]FMISO-PET Imaging

Hypoxia is a marker of poor prognosis in malignant tumors independent from the selected therapeutic method and the therapy should be intensified in such tumors. Hypoxia imaging with positron emission tomography (PET) is limited by low contrast to noise ratios with every available tracer. In radiation oncology appropriate delineation is required to allow therapy and intensification. While manual segmentation results are highly dependent from experience and observers condition (high inter- and intra observer variability), threshold- and gradient-based algorithms for automatic segmentation frequently fail in low contrast data sets. Likewise, calibration of these algorithms using phantoms is not useful. Complex computational models such as swarm intelligence-based algorithms are promising tools for optimized segmentation results and allow observer independent interpretation of multimodal and multidimensional imaging data.

Multicenter evaluation of single-photon emission computed tomography quantification with third-party reconstruction software

Reliable and reproducible quantification is essential in many clinical situations. Previously, single-photon emission computed tomography (SPECT) has not been considered a quantitative imaging modality, but recent advances in reconstruction algorithm development have made SPECT quantitative. In this study, we investigate the reproducibility of SPECT quantification with phantoms in a multicenter setting using novel third-party reconstruction software. A total of five hospitals and eight scanners (three GE scanners and five Siemens scanners) participated in the study. A Jaszczak phantom without inserts was used to calculate counts to activity concentration conversion factors. The quantitative accuracy was tested using the NEMA-IEC phantom with six spherical inserts (diameters from 10 to 37 mm) filled to an 8 : 1 insert-background concentration ratio. Phantom studies were reconstructed at one central location using HERMES HybridRecon applying corrections for attenuation, collimator-detector response, and scatter. Spherical volumes of interest with the same diameter as the inserts were drawn on the images and recovery coefficients for the spheres were calculated. The coefficient of variation (CoV) of the NEMA-IEC phantom recovery coefficients ranged from ∼19 to 5% depending on the insert diameter so that the lowest CoV was obtained with the largest spheres. The intersite CoV was almost equal to intrasite CoV. In conclusion, quantitative SPECT is reproducible in a multicenter setting with third-party reconstruction software.

A novel phantom technique for evaluating the performance of PET auto-segmentation methods in delineating heterogeneous and irregular lesions

BACKGROUND

Positron Emission Tomography (PET)-based automatic segmentation (PET-AS) methods can improve tumour delineation for radiotherapy treatment planning, particularly for Head and Neck (H&N) cancer. Thorough validation of PET-AS on relevant data is currently needed. Printed subresolution sandwich (SS) phantoms allow modelling heterogeneous and irregular tracer uptake, while providing reference uptake data. This work aimed to demonstrate the usefulness of the printed SS phantom technique in recreating complex realistic H&N radiotracer uptake for evaluating several PET-AS methods.

METHODS

Ten SS phantoms were built from printouts representing 2mm-spaced slices of modelled H&N uptake, printed using black ink mixed with 18F-fluorodeoxyglucose, and stacked between 2mm thick plastic sheets. Spherical lesions were modelled for two contrasted uptake levels, and irregular and spheroidal tumours were modelled for homogeneous, and heterogeneous uptake including necrotic patterns. The PET scans acquired were segmented with ten custom PET-AS methods: adaptive iterative thresholding (AT), region growing, clustering applied to 2 to 8 clusters, and watershed transform-based segmentation. The difference between the resulting contours and the ground truth from the image template was evaluated using the Dice Similarity Coefficient (DSC), Sensitivity and Positive Predictive value.

RESULTS

Realistic H&N images were obtained within 90 min of preparation. The sensitivity of binary PET-AS and clustering using small numbers of clusters dropped for highly heterogeneous spheres. The accuracy of PET-AS methods dropped between 4% and 68% for irregular lesions compared to spheres of the same volume. For each geometry and uptake modelled with the SS phantoms, we report the number of clusters resulting in optimal segmentation. Radioisotope distributions representing necrotic uptakes proved most challenging for most methods. Two PET-AS methods did not include the necrotic region in the segmented volume.

CONCLUSIONS

Printed SS phantoms allowed identifying advantages and drawbacks of the different methods, determining the most robust PET-AS for the segmentation of heterogeneities and complex geometries, and quantifying differences across methods in the delineation of necrotic lesions. The printed SS phantom technique provides key advantages in the development and evaluation of PET segmentation methods and has a future in the field of radioisotope imaging.

Impact of point spread function modelling and time of flight on FDG uptake measurements in lung lesions using alternative filtering strategies

BACKGROUND

The use of maximum standardised uptake value (SUVmax) is commonplace in oncology positron emission tomography (PET). Point spread function (PSF) modelling and time-of-flight (TOF) reconstructions have a significant impact on SUVmax, presenting a challenge for centres with defined protocols for lesion classification based on SUVmax thresholds. This has perhaps led to the slow adoption of these reconstructions. This work evaluated the impact of PSF and/or TOF reconstructions on SUVmax, SUVpeak and total lesion glycolysis (TLG) under two different schemes of post-filtering.

METHODS

Post-filters to match voxel variance or SUVmax were determined using a NEMA NU-2 phantom. Images from 68 consecutive lung cancer patients were reconstructed with the standard iterative algorithm along with TOF; PSF modelling - Siemens HD·PET (HD); and combined PSF modelling and TOF - Siemens ultraHD·PET (UHD) with the two post-filter sets. SUVmax, SUVpeak, TLG and signal-to-noise ratio of tumour relative to liver (SNR(T-L)) were measured in 74 lesions for each reconstruction. Relative differences in uptake measures were calculated, and the clinical impact of any changes was assessed using published guidelines and local practice.

RESULTS

When matching voxel variance, SUVmax increased substantially (mean increase +32% and +49% for HD and UHD, respectively), potentially impacting outcome in the majority of patients. Increases in SUVpeak were less notable (mean increase +17% and +23% for HD and UHD, respectively). Increases with TOF alone were far less for both measures. Mean changes to TLG were <10% for all algorithms for either set of post-filters. SNR(T-L) were greater than ordered subset expectation maximisation (OSEM) in all reconstructions using both post-filtering sets.

CONCLUSIONS

Matching image voxel variance with PSF and/or TOF reconstructions, particularly with PSF modelling and in small lesions, resulted in considerable increases in SUVmax, inhibiting the use of defined protocols for lesion classification based on SUVmax. However, reduced partial volume effects may increase lesion detectability. Matching SUVmax in phantoms translated well to patient studies for PSF reconstruction but less well with TOF, where a small positive bias was observed in patient images. Matching SUVmax significantly reduced voxel variance and potential variability of uptake measures. Finally, TLG may be less sensitive to reconstruction methods compared with either SUVmax or SUVpeak.

The influence of different signal-to-background ratios on spatial resolution and F18-FDG-PET quantification using point spread function and time-of-flight reconstruction

BACKGROUND

F18-fluorodeoxyglucose positron-emission tomography (FDG-PET) reconstruction algorithms can have substantial influence on quantitative image data used, e.g., for therapy planning or monitoring in oncology. We analyzed radial activity concentration profiles of differently reconstructed FDG-PET images to determine the influence of varying signal-to-background ratios (SBRs) on the respective spatial resolution, activity concentration distribution, and quantification (standardized uptake value [SUV], metabolic tumor volume [MTV]).

METHODS

Measurements were performed on a Siemens Biograph mCT 64 using a cylindrical phantom containing four spheres (diameter, 30 to 70 mm) filled with F18-FDG applying three SBRs (SBR1, 16:1; SBR2, 6:1; SBR3, 2:1). Images were reconstructed employing six algorithms (filtered backprojection [FBP], FBP + time-of-flight analysis [FBP + TOF], 3D-ordered subset expectation maximization [3D-OSEM], 3D-OSEM + TOF, point spread function [PSF], PSF + TOF). Spatial resolution was determined by fitting the convolution of the object geometry with a Gaussian point spread function to radial activity concentration profiles. MTV delineation was performed using fixed thresholds and semiautomatic background-adapted thresholding (ROVER, ABX, Radeberg, Germany).

RESULTS

The pairwise Wilcoxon test revealed significantly higher spatial resolutions for PSF + TOF (up to 4.0 mm) compared to PSF, FBP, FBP + TOF, 3D-OSEM, and 3D-OSEM + TOF at all SBRs (each P < 0.05) with the highest differences for SBR1 decreasing to the lowest for SBR3. Edge elevations in radial activity profiles (Gibbs artifacts) were highest for PSF and PSF + TOF declining with decreasing SBR (PSF + TOF largest sphere; SBR1, 6.3%; SBR3, 2.7%). These artifacts induce substantial SUVmax overestimation compared to the reference SUV for PSF algorithms at SBR1 and SBR2 leading to substantial MTV underestimation in threshold-based segmentation. In contrast, both PSF algorithms provided the lowest deviation of SUVmean from reference SUV at SBR1 and SBR2.

CONCLUSIONS

At high contrast, the PSF algorithms provided the highest spatial resolution and lowest SUVmean deviation from the reference SUV. In contrast, both algorithms showed the highest deviations in SUVmax and threshold-based MTV definition. At low contrast, all investigated reconstruction algorithms performed approximately equally. The use of PSF algorithms for quantitative PET data, e.g., for target volume definition or in serial PET studies, should be performed with caution - especially if comparing SUV of lesions with high and low contrasts.

Delineation gross tumor volume based on positron emission tomography images by a numerical approximation method

OBJECTIVE

A scheme, named SUV_Shape, for the gross tumor volume (GTV) delineation on positron emission tomography (PET) images was designed by a numerical approximation method, and evaluated during this study.

METHODS

Twenty-one vacuous plastic balls of different shapes and sizes, their volumes ranged from 0.56 to 179.50 mL and were confirmed by a BL610 balance (Sartorius, Canada), consisted of four group models. Every group model was filled with a specific activity [18F]-FDG solution (55.1, 38.2, 23.7, and 36.3 kBq/mL) represented tumor, and fixed at the bottom of a barrel which was filled with unlike [18F]-FDG solution (5.4, 6.8, 8.1, and 4.0 kBq/mL, correspondingly) represented the background. The PET data of them were acquired by two-dimensional and three-dimensional mode in a PET/CT scanner (Discovery ST8, GE Healthcare, USA). The volume of each ball was measured by SUV_Shape, and the BL610 balance, labeled as GTVs and GTVt, respectively. Five rabbits implanted VX2 squamous carcinomas were acquired by [18F]-FDG PET/CT. These rabbits were mercy killed within 24 h after PET/CT acquisition. VX2 tumors were surgically removed, and their volumes were measured by SUV_Shape, and caliper, labeled as GTVs and GTVt. The Spearman's ρ between GTVs and GTVt were done.

RESULTS

The tumor-background ratios in four groups of phantom models were 10.3, 5.6, 2.9, and 9.0, respectively. The relationship between GTVt and GTVs for phantom models was significant (Spearman's ρ > 0.95, P < 0.01), regardless of the different acquisition modes. Twelve VX2 tumor nodes or masses were measured; their GTVt ranged from 0.11 to 29.26 mL. The relationship between GTVt and GTVs was significant (Spearman's ρ = 0.893, P < 0.01) for animal tumor models.

CONCLUSIONS

The SUV_Shape scheme could delineate tumors based on their radiopharmaceutical-avid PET images.

Cold wall effect eliminating method to determine the contrast recovery coefficient for small animal PET scanners using the NEMA NU-4 image quality phantom

The contrast recovery coefficients (CRC) were evaluated for five different small animal PET scanners: GE Explore Vista, Genisys4, MiniPET-2, nanoScan PC and Siemens Inveon. The NEMA NU-4 2008 performance test with the suggested image quality phantom (NU4IQ) does not allow the determination of the CRC values for the hot regions in the phantom. This drawback of NU4IQ phantom motivated us to develop a new method for this purpose. The method includes special acquisition and reconstruction protocols using the original phantom, and results in an artificially merged image enabling the evaluation of CRC values. An advantageous feature of this method is that it stops the cold wall effect from distorting the CRC calculation. Our suggested protocol results in a set of CRC values contributing to the characterization of small animal PET scanners. GATE simulations were also performed to validate the new method and verify the evaluated CRC values. We also demonstrated that the numerical values of this parameter depend on the actual object contrast of the hot region(s) and this mainly comes from the spillover effect. This effect was also studied while analysing the background activity level around the hot rods. We revealed that the calculated background mean values depended on the target contrast in a scanner specific manner. Performing the artificially merged imaging procedure and additional simulations using the micro hollow sphere (MHS) phantom geometry, we also proved that the inactive wall around the hot spheres can have a remarkable impact on the calculated CRC. In conclusion, we have shown that the proposed artificial merging procedure and the commonly used NU4IQ phantom prescribed by the NEMA NU-4 can easily deliver reliable CRC data otherwise unavailable for the NU4IQ phantom in the conventional protocol or the MHS phantom.

A review on segmentation of positron emission tomography images

Positron Emission Tomography (PET), a non-invasive functional imaging method at the molecular level, images the distribution of biologically targeted radiotracers with high sensitivity. PET imaging provides detailed quantitative information about many diseases and is often used to evaluate inflammation, infection, and cancer by detecting emitted photons from a radiotracer localized to abnormal cells. In order to differentiate abnormal tissue from surrounding areas in PET images, image segmentation methods play a vital role; therefore, accurate image segmentation is often necessary for proper disease detection, diagnosis, treatment planning, and follow-ups. In this review paper, we present state-of-the-art PET image segmentation methods, as well as the recent advances in image segmentation techniques. In order to make this manuscript self-contained, we also briefly explain the fundamentals of PET imaging, the challenges of diagnostic PET image analysis, and the effects of these challenges on the segmentation results.

Influence of cold walls on PET image quantification and volume segmentation: a phantom study

PURPOSE

Commercially available fillable plastic inserts used in positron emission tomography phantoms usually have thick plastic walls, separating their content from the background activity. These "cold" walls can modify the intensity values of neighboring active regions due to the partial volume effect, resulting in errors in the estimation of standardized uptake values. Numerous papers suggest that this is an issue for phantom work simulating tumor tissue, quality control, and calibration work. This study aims to investigate the influence of the cold plastic wall thickness on the quantification of 18F-fluorodeoxyglucose on the image activity recovery and on the performance of advanced automatic segmentation algorithms for the delineation of active regions delimited by plastic walls.

METHODS

A commercial set of six spheres of different diameters was replicated using a manufacturing technique which achieves a reduction in plastic walls thickness of up to 90%, while keeping the same internal volume. Both sets of thin- and thick-wall inserts were imaged simultaneously in a custom phantom for six different tumor-to-background ratios. Intensity values were compared in terms of mean and maximum standardized uptake values (SUVs) in the spheres and mean SUV of the hottest 1 ml region (SUVmax, SUVmean, and SUVpeak). The recovery coefficient (RC) was also derived for each sphere. The results were compared against the values predicted by a theoretical model of the PET-intensity profiles for the same tumor-to-background ratios (TBRs), sphere sizes, and wall thicknesses. In addition, ten automatic segmentation methods, written in house, were applied to both thin- and thick-wall inserts. The contours obtained were compared to computed tomography derived gold standard ("ground truth"), using five different accuracy metrics.

RESULTS

The authors' results showed that thin-wall inserts achieved significantly higher SUVmean, SUVmax, and RC values (up to 25%, 16%, and 25% higher, respectively) compared to thick-wall inserts, which was in agreement with the theory. This effect decreased with increasing sphere size and TBR, and resulted in substantial (>5%) differences between thin- and thick-wall inserts for spheres up to 30 mm diameter and TBR up to 4. Thinner plastic walls were also shown to significantly improve the delineation accuracy for the majority of the segmentation methods tested, by increasing the proportion of lesion voxels detected, although the errors in image quantification remained non-negligible.

CONCLUSIONS

This study quantified the significant effect of a 90% reduction in the thickness of insert walls on SUV quantification and PET-based boundary detection. Mean SUVs inside the inserts and recovery coefficients were particularly affected by the presence of thick cold walls, as predicted by a theoretical approach. The accuracy of some delineation algorithms was also significantly improved by the introduction of thin wall inserts instead of thick wall inserts. This study demonstrates the risk of errors deriving from the use of cold wall inserts to assess and compare the performance of PET segmentation methods.

An automatic method for accurate volume delineation of heterogeneous tumors in PET

PURPOSE

Accurate volumetric tumor delineation is of increasing importance in radiation treatment planning. Many tumors exhibit only moderate tracer uptake heterogeneity and delineation methods using an adaptive threshold lead to robust results. These methods use a tumor reference value R (e.g., ROI maximum) and the tumor background Bg to compute the volume reproducing threshold. This threshold corresponds to an isocontour which defines the tumor boundary. However, the boundaries of strongly heterogeneous tumors can not be described by an isocontour anymore and therefore conventional threshold methods are not suitable for accurate delineation. The aim of this work is the development and validation of a delineation method for heterogeneous tumors.

METHODS

The new method (voxel-specific threshold method, VTM) can be considered as an extension of an adaptive threshold method (lesion-specific threshold method, LTM), where instead of a lesion-specific threshold for the whole ROI, a voxel-specific threshold is computed by determining for each voxel Bg and R in the close vicinity of the voxel. The absolute threshold for the considered voxel is then given by Tabs=T×(R-Bg)+Bg, where T=0.39 was determined with phantom measurements.

VALIDATION

30 clinical datasets from patients with non-small-cell lung cancer were used to generate 30 realistic anthropomorphic software phantoms of tumors with different heterogeneities and well-known volumes and boundaries. Volume delineation was performed with VTM and LTM and compared with the known lesion volumes and boundaries.

RESULTS

In contrast to LTM, VTM was able to reproduce the true tumor boundaries accurately, independent of the heterogeneity. The deviation of the determined volume from the true volume was (0.8±4.2)% for VTM and (11.0±16.4)% for LTM.

CONCLUSIONS

In anthropomorphic software phantoms, the new method leads to promising results and to a clear improvement of volume delineation in comparison to conventional background-corrected thresholding. In the next step, the suitability for clinical routine will be further investigated.

Use of wall-less ¹⁸F-doped gelatin phantoms for improved volume delineation and quantification in PET/CT

Positron emission tomography (PET) with (18)F-FDG is a valuable tool for staging, planning treatment, and evaluating the treatment response for many different types of tumours. The correct volume estimation is of utmost importance in these situations. To date, the most common types of phantoms used in volume quantification in PET utilize fillable, hollow spheres placed in a circular or elliptical cylinder made of polymethyl methacrylate. However, the presence of a non-radioactive sphere wall between the hotspot and the background activity in images of this type of phantom could cause inaccuracies. To investigate the influence of the non-active walls, we developed a phantom without non-active sphere walls for volume delineation and quantification in PET. Three sizes of gelatin hotspots were moulded and placed in a Jaszczak phantom together with hollow plastic spheres of the same sizes containing the same activity concentration. (18)F PET measurements were made with zero background activity and with tumour-to-background ratios of 12.5, 10, 7.5, and 5. The background-corrected volume reproducing threshold, Tvol, was calculated for both the gelatin and the plastic spheres. It was experimentally verified that the apparent background dependence of Tvol, i.e., a decreasing Tvol with increasing background fraction, was not present for wall-less spheres; the opposite results were seen in plastic, hollow spheres in commercially-available phantoms. For the types of phantoms commonly used in activity quantification, the estimation of Tvol using fillable, hollow, plastic spheres with non-active walls would lead to an overestimate of the tumour volume, especially for small volumes in a high activity background.