Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans

Magnetic resonance biomarker assessment software (MR-BIAS): an automated open-source tool for the ISMRM/NIST system phantom

Objective.To provide an open-source software for repeatable and efficient quantification ofT₁andT₂relaxation times with the ISMRM/NIST system phantom. Quantitative magnetic resonance imaging (qMRI) biomarkers have the potential to improve disease detection, staging and monitoring of treatment response. Reference objects, such as the system phantom, play a major role in translating qMRI methods into the clinic. The currently available open-source software for ISMRM/NIST system phantom analysis, Phantom Viewer (PV), includes manual steps that are subject to variability.Approach.We developed the Magnetic Resonance BIomarker Assessment Software (MR-BIAS) to automatically extract system phantom relaxation times. The inter-observer variability (IOV) and time efficiency of MR-BIAS and PV was observed in six volunteers analysing three phantom datasets. The IOV was measured with the coefficient of variation (CV) of percent bias (%bias) inT₁andT₂with respect to NMR reference values. The accuracy of MR-BIAS was compared to a custom script from a published study of twelve phantom datasets. This included comparison of overall bias and %bias for variable inversion recovery (T₁^VIR), variable flip angle (T₁^VFA) and multiple spin-echo (T₂^MSE) relaxation models.Main results.MR-BIAS had a lower mean CV withT₁^VIR(0.03%) andT₂^MSE(0.05%) in comparison to PV withT₁^VIR(1.28%) andT₂^MSE(4.55%). The mean analysis duration was 9.7 times faster for MR-BIAS (0.8 min) than PV (7.6 min). There was no statistically significant difference in the overall bias, or the %bias for the majority of ROIs, as calculated by MR-BIAS or the custom script for all models.Significance.MR-BIAS has demonstrated repeatable and efficient analysis of the ISMRM/NIST system phantom, with comparable accuracy to previous studies. The software is freely available to the MRI community, providing a framework to automate required analysis tasks, with the flexibility to explore open questions and accelerate biomarker research.

A review of harmonization strategies for quantitative PET

PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative values have been used as a surrogate marker for clinical decision-making in numerous clinical studies and trials. However, quantitative values in PET are variable depending on technical, biological, and physical factors. The variability may have a significant impact on a study outcome. Appropriate scanner calibration and quality control, standardization of imaging protocols, and any necessary harmonization strategies are essential to make use of PET as a biomarker with low bias and variability. This review summarizes benefits, limitations, and remaining challenges for harmonization of quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-¹⁸F PET imaging. This review is expected to facilitate harmonization of quantitative PET and to promote the contribution of PET-derived biomarkers to research and development in medicine.

Comparison of protocols with respiratory-gated (4D) motion compensation in PET/CT: open-source package for quantification of phantom image quality

BACKGROUND

Patient's breathing affects the quality of chest images acquired with positron emission tomography/computed tomography (PET/CT) studies. Movement correction is required to optimize PET quantification in clinical settings. We present a reproducible methodology to compare the impact of different movement compensation protocols on PET image quality. Static phantom images were set as reference values, and recovery coefficients (RCs) were calculated from motion compensated images for the phantoms in respiratory movement. Image quality was evaluated in terms of: (1) volume accuracy (VA) with the NEMA phantom; (2) concentration accuracy (CA) by six refillable inserts within the electron density CIRS phantom; and (3) spatial resolution (R) with the Jaszczak phantom. Three different respiratory patterns were applied to the phantoms. We developed an open-source package to automatically analyze VA, CA and R. We compared 10 different movement compensation protocols available in the Philips Gemini TF-64 PET/CT (4-, 6-, 8- and 10-time bins, 20%-, 30%-, 40%-window width in Inhale and Exhale).

RESULTS

The homemade package provided RC values for VA, CA and R of 102 PET images in less than 5 min. Results of the comparison of the 10 different protocols demonstrated the feasibility of the proposed method for quantifying the variations observed qualitatively. Overall, prospective protocols showed better motion compensation than retrospective. The best performance was obtained for the protocol Exhale 30% (0.3 s after maximum Exhale position and window width of 30%) with RC[Formula: see text], RC[Formula: see text] and RC[Formula: see text]. Among retrospective protocols, 8 Phase protocol showed the best performance.

CONCLUSION

We provided an open-source package able to automatically evaluate the impact of motion compensation methods on PET image quality. A setup based on commonly available experimental phantoms is recommended. Its application for the comparison of 10 time-based approaches showed that Exhale 30% protocol had the best performance. The proposed framework is not specific to the phantoms and protocols presented on this study.

Advanced quantitative evaluation of PET systems using the ACR phantom and NiftyPET software

PURPOSE

A novel phantom-imaging platform, a set of software tools, for automated and high-precision imaging of the American College of Radiology (ACR) positron emission tomography (PET) phantom for PET/magnetic resonance (PET/MR) and PET/computed tomography (PET/CT) systems is proposed.

METHODS

The key feature of this platform is the vector graphics design that facilitates the automated measurement of the knife-edge response function and hence image resolution, using composite volume of interest templates in a 0.5 mm resolution grid applied to all inserts of the phantom. Furthermore, the proposed platform enables the generation of an accurate μ $\mu$ -map for PET/MR systems with a robust alignment based on two-stage image registration using specifically designed PET templates. The proposed platform is based on the open-source NiftyPET software package used to generate multiple list-mode data bootstrap realizations and image reconstructions to determine the precision of the two-stage registration and any image-derived statistics. For all the analyses, iterative image reconstruction was employed with and without modeled shift-invariant point spread function and with varying iterations of the ordered subsets expectation maximization (OSEM) algorithm. The impact of the activity outside the field of view (FOV) was assessed using two acquisitions of 30 min each, with and without the activity outside the FOV.

RESULTS

The utility of the platform has been demonstrated by providing a standard and an advanced phantom analysis including the estimation of spatial resolution using all cylindrical inserts. In the imaging planes close to the edge of the axial FOV, we observed deterioration in the quantitative accuracy, reduced resolution (FWHM increased by 1-2 mm), reduced contrast, and background uniformity due to the activity outside the FOV. Although it slows convergence, the PSF reconstruction had a positive impact on resolution and contrast recovery, but the degree of improvement depended on the regions. The uncertainty analysis based on bootstrap resampling of raw PET data indicated high precision of the two-stage registration.

CONCLUSIONS

We demonstrated that phantom imaging using the proposed methodology with the metric of spatial resolution and multiple bootstrap realizations may be helpful in more accurate evaluation of PET systems as well as in facilitating fine tuning for optimal imaging parameters in PET/MR and PET/CT clinical research studies.

New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

Not only visual interpretation for lesion detection, staging, and characterization, but also quantitative treatment response assessment are key roles for ¹⁸F-FDG PET in oncology. In multicenter oncology PET studies, image quality standardization and SUV harmonization are essential to obtain reliable study outcomes. Standards for image quality and SUV harmonization range should be regularly updated according to progress in scanner performance. Accordingly, the first aim of this study was to propose new image quality reference levels to ensure small lesion detectability. The second aim was to propose a new SUV harmonization range and an image noise criterion to minimize the inter-scanner and intra-scanner SUV variabilities. We collected a total of 37 patterns of images from 23 recent PET/CT scanner models using the NEMA NU2 image quality phantom. PET images with various acquisition durations of 30-300 s and 1800 s were analyzed visually and quantitatively to derive visual detectability scores of the 10-mm-diameter hot sphere, noise-equivalent count (NEC_phantom), 10-mm sphere contrast (Q_H,10 mm), background variability (N_10 mm), contrast-to-noise ratio (Q_H,10 mm/N_10 mm), image noise level (CV_BG), and SUVmax and SUVpeak for hot spheres (10-37 mm diameters). We calculated a reference level for each image quality metric, so that the 10-mm sphere can be visually detected. The SUV harmonization range and the image noise criterion were proposed with consideration of overshoot due to point-spread function (PSF) reconstruction. We proposed image quality reference levels as follows: Q_H,10 mm/N_10 mm ≥ 2.5 and CV_BG ≤ 14.1%. The 10th-90th percentiles in the SUV distributions were defined as the new SUV harmonization range. CV_BG ≤ 10% was proposed as the image noise criterion, because the intra-scanner SUV variability significantly depended on CV_BG. We proposed new image quality reference levels to ensure small lesion detectability. A new SUV harmonization range (in which PSF reconstruction is applicable) and the image noise criterion were also proposed for minimizing the SUV variabilities. Our proposed new standards will facilitate image quality standardization and SUV harmonization of multicenter oncology PET studies. The reliability of multicenter oncology PET studies will be improved by satisfying the new standards.

Practical joint reconstruction of activity and attenuation with autonomous scaling for time-of-flight PET

Recent research has showed that attenuation images can be determined from emission data, jointly with activity images, up to a scaling constant when utilizing the time-of-flight (TOF) information. We aim to develop practical CT-less joint reconstruction for clinical TOF PET scanners to obtain quantitatively accurate activity and attenuation images. In this work, we present a joint reconstruction of activity and attenuation based on MLAA (maximum likelihood reconstruction of attenuation and activity) with autonomous scaling determination and joint TOF scatter estimation from TOF PET data. Our idea for scaling is to use a selected volume of interest (VOI) in a reconstructed attenuation image with known attenuation, e.g. a liver in patient imaging. First, we construct a unit attenuation medium which has a similar, though not necessarily the same, support to the imaged emission object. All detectable LORs intersecting the unit medium have an attenuation factor of e -1≈ 0.3679, i.e. the line integral of linear attenuation coefficients is one. The scaling factor can then be determined from the difference between the reconstructed attenuation image and the known attenuation within the selected VOI normalized by the unit attenuation medium. A four-step iterative joint reconstruction algorithm is developed. In each iteration, (1) first the activity is updated using TOF OSEM from TOF list-mode data; (2) then the attenuation image is updated using XMLTR-a extended MLTR from non-TOF LOR sinograms; (3) a scaling factor is determined based on the selected VOI and both activity and attenuation images are updated using the estimated scaling; and (4) scatter is estimated using TOF single scatter simulation with the jointly reconstructed activity and attenuation images. The performance of joint reconstruction is studied using simulated data from a generic whole-body clinical TOF PET scanner and a long axial FOV research PET scanner as well as 3D experimental data from the PennPET Explorer scanner. We show that the proposed joint reconstruction with proper autonomous scaling provides low bias results comparable to the reference reconstruction with known attenuation.

Benefit of Improved Performance with State-of-the Art Digital PET/CT for Lesion Detection in Oncology

The latest digital whole-body PET scanners provide a combination of higher sensitivity and improved spatial and timing resolution. We performed a lesion detectability study on two generations of Biograph PET/CT scanners, the mCT Flow and the Vision, to study the impact of improved physical performance on clinical performance. Our hypothesis was that the improved performance of the Vision would result in improved lesion detectability, allowing shorter imaging times or, equivalently, a lower injected dose. Methods: Data were acquired with the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network torso phantom combined with a 20-cm-diameter cylindrical phantom. Spherical lesions were emulated by acquiring sphere-in-air data and combining them with the phantom data to generate combined datasets with embedded lesions of known contrast. Two sphere sizes and uptakes were used: 9.89-mm-diameter spheres with 6:1 (lung) and 3:1 (cylinder) local activity concentration uptakes and 4.95-mm-diameter spheres with 9.6:1 (lung) and 4.5:1 (cylinder) local activity concentration uptakes. Standard image reconstruction was performed: an ordinary Poisson ordered-subsets expectation maximization algorithm with point-spread function and time-of-flight modeling and postreconstruction smoothing with a 5-mm gaussian filter. The Vision images were also generated without any postreconstruction smoothing. Generalized scan statistics methodology was used to estimate the area under the localized receiver-operating-characteristic curve (ALROC). Results: The higher sensitivity and improved time-of-flight performance of the Vision leads to reduced contrast in the background noise nodule distribution. Measured lesion contrast is also higher on the Vision because of its improved spatial resolution. Hence, the ALROC is noticeably higher for the Vision than for the mCT Flow. Conclusion: Improved overall performance of the Vision provides a factor of 4-6 reduction in imaging time (or injected dose) over the mCT Flow when using the ALROC metric for lesions at least 9.89 mm in diameter. Smaller lesions are barely detected in the mCT Flow, leading to even higher ALROC gains with the Vision. The improved spatial resolution of the Vision also leads to a higher measured contrast that is closer to the real uptake, implying improved quantification. Postreconstruction smoothing, however, reduces this improvement in measured contrast, thereby reducing the ALROC for small, high-uptake lesions.

Numerical observer study of lesion detectability for a long axial field-of-view whole-body PET imager using the PennPET Explorer

This work uses lesion detectability to characterize the performance of long axial field of view (AFOV) PET scanners which have increased sensitivity compared to clinical scanners. Studies were performed using the PennPET Explorer, a 70 cm long AFOV scanner built at the University of Pennsylvania, for small lesions distributed in a uniform water-filled cylinder (simulations and measurements), an anthropomorphic torso phantom (measurement), and a human subject (measurement). The lesion localization and detection task was quantified numerically using a generalized scan statistics methodology. Detectability was studied as a function of background activity distribution, scan duration for a single bed position, and axial location of the lesions. For the cylindrical phantom, the areas under the localization receiver operating curve (ALROCs) of lesions placed at various axial locations in the scanner were greater than 0.8-a value considered to be clinically acceptable (i.e. 80% probability of detecting lesion)-for scan times of 60 s or longer for standard-of-care (SoC) clinical dose levels. 10 mm diameter lesions placed in the anthropomorphic phantom and human subject resulted in ALROCs of 0.8 or greater for scan times longer than 30 s in the lung region and 60 s in the liver region, also for SoC doses. ALROC results from all three activity distributions show similar trends as a function of counts detected per axial location. These results will be used to guide decisions on imaging parameters, such as scan time and patient dose, when imaging patients in a single bed position on long AFOV systems and can also be applied to clinical scanners with consideration of the sensitivity differences.

Development and Validation of an 18 F-Fluorodeoxyglucose-Positron Emission Tomography With Computed Tomography-Based Tool for the Evaluation of Joint Counts and Disease Activity in Patients With Rheumatoid Arthritis

OBJECTIVE

Clinical joint count assessment is important for detecting synovitis but its reliability is a subject of controversy. This study was undertaken to assess the correlation of positron emission tomography (PET)-derived parameters in 68 joints with disease activity and to compare the reliability of joint counts between PET with computed tomography (CT) and clinical assessment in patients with rheumatoid arthritis (RA).

METHODS

We enrolled 91 patients with active RA (69 in a development group and 22 in a validation group) who underwent concurrent ¹⁸ F-fluorodeoxyglucose (¹⁸ F-FDG)-PET-CT and clinical disease activity evaluation. PET-derived parameters were compared with disease activity assessed using clinical joint count parameters. A Disease Activity Score (DAS) using counts of PET-positive joints was developed, and then validation studies were performed in an independent group.

RESULTS

The number of PET-positive joints (of 28 and 68 joints) was significantly correlated with the swollen joint count (SJC) and tender joint count (TJC) and the DAS in 28 joints using the erythrocyte sedimentation rate (DAS28-ESR). Intraobserver and interobserver reliability of PET for the affected joint counts were excellent. Interobserver reliability between nuclear medicine physicians and rheumatologists was good for the SJC and TJC in both 28 joints and 68 joints. After multivariate analyses, including ESR and patient's global assessment of disease activity (PtGA) in addition to PET-derived parameters, the PET/DAS was derived as (0.063 × number of PET-positive joints in 28 joints) + (0.011 × ESR) + (0.030 × PtGA). A significant correlation between the PET/DAS and the DAS28-ESR was confirmed in the validation group (P < 0.001).

CONCLUSION

PET-CT could serve as a sensitive and reliable method in the evaluation of disease activity in RA patients, and may be applicable as a research tool, particularly in clinical trials.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.