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Mejora de la Exactitud Cuantitativa de la Tomografía por Emisión de Positrones (PET) incorporando información específica del escáner, del paciente y de la enfermedad.
Abstract and Figures
Las imágenes médicas cuantitativas, a diferencia de las imágenes tradicionales cualitativas, permiten obtener mediciones de parámetros fisiológicos o anatómicos de manera no invasiva. Como toda técnica de medición, se encuentran sometidas a sesgos y variabilidad que pueden limitar su aplicación. En los últimos años, diferentes sociedades científicas han abordado el problema de mejorar la exactitud y la reproducibilidad de las imágenes cuantitativas, prescribiendo protocolos estandarizados de adquisición y procesamiento para diversas modalidades diagnósticas. En particular, la tomografía por emisión de positrones (PET) ha sido la modalidad diagnóstica con mayor crecimiento en las últimas dos décadas y es un componente esencial en el manejo del paciente oncológico. Las imágenes PET tienen la particularidad de ser inherentemente cuantitativas ya que permiten medir la concentración de radiactividad del radiofármaco administrado al paciente de manera no invasiva. Esto permite inferir parámetros metabólicos invivo, cosa que otras modalidades anatómicas o funcionales como la tomografía computada por rayos X o la resonancia magnética nuclear no pueden hacer. Por ejemplo, la cuantificación del metabolismo de glucosa mediante el análogo 18F-Fluorodesoxiglucosa (18F-FDG) en tumores permite aumentar el valor pronóstico del estudio y evaluar la respuesta a tratamientos tales como radioterapia, quimioterapia, hormonoterapia e inmunoterapia, entre otros, de manera cuantitativa. Sin embargo, las mediciones provenientes de distintos tomógrafos PET pueden no ser comparables. El proceso de armonización cuantitativa tiene como objetivo que dichas mediciones sean lo más parecidas posibles para un amplio rango de tamaños de estructuras. Si bien ya existen protocolos de armonización cuantitativa para PET, suelen ser costosos y complejos, lo que limita su aplicación en centros PET no académicos. Por otro lado, las características propias del paciente tales como el peso o el índice de masa corporal también generan diferencias en las mediciones. En la presente tesis, proponemos un modelo simplificado de formación de imagen para PET que incluye la resolución espacial y propiedades del ruido. Este modelo sirve de base para la implementación de algoritmos de armonización novedosos y para el diseño de algoritmos de adquisición específicos para cada paciente y región anatómica. Los algoritmos son validados para una amplia variedad de modelos de tomógrafos PET y para diferentes tamaños de pacientes. Esta tesis provee una base sólida para mejorar la reproducibilidad y la exactitud de las mediciones provenientes de imágenes PET de distintos pacientes adquiridos en distintos tomógrafos.
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Purpose:
Quantification in positron emission tomography (PET) is subject to bias due to physical and technical limitations. The goal of quantitative harmonization is to achieve comparable measurements between different scanners, thus enabling multicenter clinical trials. Clinical guidelines, such as those from the European Association of Nuclear Medicine (EANM), recommend harmonizing PET reconstructions to bring contrast recovery coefficients (CRCs) within specifications. However, these harmonized reconstructions can show quantitative biases. In this work we improve harmonization by using a novel adaptive filtering scheme. Our goal was to obtain low quantification bias and high peak signal to noise ratio (PSNR) values at the same time.
Methods:
a novel three-stage adaptive denoising filter was implemented. Filter parameters were optimized to achieve both high PSNR in a digital brain phantom and low quantitative bias of maximum CRC values (CRCmax) obtained from a National Electrical Manufacturers Association (NEMA) PET image quality phantom. The NEMA phantom was scanned on several PET/CT scanners and reconstructed without postfilters. The optimal filter settings found for a training dataset were then applied to testing reconstructions from other scanners. Harmonization limits were defined using the 95% confidence intervals across reconstructions.
Results:
Average CRCmax values close to unity (± 5%) were achieved for spheres with diameter equal or greater than 13 mm for the training dataset. PSNR values were comparable to other state-of-the-art filter results. Using the same optimal filter settings for the testing datasets, similar quantitative results were found. Lesion conspicuity was improved on clinical scans when compared with EANM reconstructions, with no visible artifacts.
Conclusions:
Our three-stage adaptive filter achieved state-of-the-art quantitative performance for PET imaging. Harmonization tolerances with lower bias and variance than EANM guidelines were achieved for a variety of scanner models. CRCmax values were close to unity and the quantification variability was reduced when compared with standard reconstructions.
Image quality in positron emission tomography (PET) is limited by the number of detected photons. Heavier patients present higher photon attenuation levels, thus increasing image noise. In this work, we propose a new method that uses the combined patient attenuation/system matrix together with a tracer uptake prediction model to optimize scan times for different bed positions in whole body scans. Our main goal is to achieve consistent noise levels across patients and anatomical regions. We propose to optimize scan times for individual bed positions, for patients of any size, based on the scanner sensitivity and patient-specific attenuation. Variable scan times for every bed position were determined by combining the system matrix, derived from the computed tomography (CT) and the scanner-specific geometric sensitivity profiles, and estimations of the global tracer uptake for each patient. The method was validated with anthropomorphic phantoms and whole-body patient 18F-FDG PET/CT scans, where variable and fixed times were compared. Phantom experiments showed that the proposed method was successful in keeping noise level constant for different attenuation setups. In real patients, image noise variability was reduced to less than one-half compared with conventional fixed-time scans at the expense of a four-fold increase in scan times between the biggest and smallest patients. Our method can homogenize image quality not only across patients of different sizes but also across different bed positions of the same patient.
Objective
The purpose of this study was to compare image quality and lesion detection capability between a digital and an analog PET/CT system in oncological patients.
Materials and methods
One hundred oncological patients (62 men, 38 women; mean age of 65 ± 12 years) were prospectively included from January–June 2018. All patients, who accepted to be scanned by two systems, consecutively underwent a single day, dual imaging protocol (digital and analog PET/CT). Three nuclear medicine physicians evaluated image quality using a 4-point scale (−1, poor; 0, fair; 1, good; 2, excellent) and detection capability by counting the number of lesions with increased radiotracer uptake. Differences were considered significant for a p value <0.05.
Results
Improved image quality in the digital over the analog system was observed in 54% of the patients (p = 0.05, 95% CI, 44.2–63.5). The percentage of interrater concordance in lesion detection capability between the digital and analog systems was 97%, with an interrater measure agreement of κ = 0.901 (p < 0.0001). Although there was no significant difference in the total number of lesions detected by the two systems (digital: 5.03 ± 10.6 vs. analog: 4.53 ± 10.29; p = 0.7), the digital system detected more lesions in 22 of 83 of PET+ patients (26.5%) (p = 0.05, 95% CI, 17.9–36.7). In these 22 patients, all lesions detected by the digital PET/CT (and not by the analog PET/CT) were < 10 mm.
Conclusion
Digital PET/CT offers improved image quality and lesion detection capability over the analog PET/CT in oncological patients, and even better for sub-centimeter lesions.
Calibration and reproducibility of quantitative 18F-fluorodeoxyglucose (FDG) PET measures are essential for adopting integral FDG-PET/CT biomarkers and response measures in multicenter clinical trials. We implemented a multicenter qualification process using NIST-traceable reference sources for scanners and dose calibrators, and similar patient and imaging protocols. We then assessed standardized uptake value (SUV) in patient test-retest studies. Methods: Five FDG-PET/CT scanners from 4 institutions (2 in a NCI-designated Comprehensive Cancer Center, 3 in a community-based network) were qualified for study use. Patients were scanned twice within 15 days, on the same scanner (n = 10); different but same model scanners within an institution (n = 2); or different model scanners at different institutions (n = 11). SUVmax values were recorded for lesions, and SUVmean for normal liver uptake. Linear mixed models with random intercept were fitted to evaluate test-retest differences in multiple lesions per patient and to estimate the concordance correlation coefficient (CCC). Bland-Altman plots and repeatability coefficients were also produced. Results: 162 lesions (82 bone, 80 soft tissue) were assessed in patients with breast cancer (n = 17) or other cancers (n = 6). Repeat scans within the same institution, using the same scanner or two scanners of the same model, had an average percent difference in SUVmax of 8% (95% CI 6%-10%). For test-retest on different scanners at different sites, the average percent difference in lesion SUVmax was 18% (95% CI 13%-24%). Normal liver uptake (SUVmean) showed an average percent difference of 5% (95% CI 3%-10%) for the same scanner model/institution and 6% (95% CI 3%-11%) for different scanners from different institutions. Protocol adherence was good; the median difference in injection-to-acquisition time was 2 minutes (range 0-11 minutes). Test-retest SUVmax variability was not explained by available information regarding protocol deviations or patient/lesion characteristics. Conclusion: FDG-PET/CT scanner qualification and calibration can yield highly reproducible test-retest tumor SUV measurements. Our data support use of different qualified scanners of the same model for serial studies. Test-retest differences from different scanner models were greater; more resolution-dependent harmonization of scanner protocols and reconstruction algorithms may be capable of reducing these differences to values closer to same-scanner results.
Positron emission tomography (PET) is a functional imaging modality widely used in clinical diagnosis. In this work, we trained a deep convolutional neural network (CNN) to improve PET image quality. Perceptual loss based on features derived from a pre-trained VGG network, instead of the conventional mean squared error, was employed as the training loss function to preserve image details. As the number of real patient data set for training is limited, we propose to pre-train the network using simulation data and fine-tune the last few layers of the network using real data sets. Results from simulation, real brain and lung data sets show that the proposed method is more effective in removing noise than the traditional Gaussian filtering method.
We investigated the block sequential regularized expectation maximization (BSREM) algorithm. ACR phantom measurements with different count statistics and 60 PET/CT research scans from the GE Discovery 600 and 690 scanners were reconstructed using BSREM and the standard-of-care OSEM algorithm. Hot concentration recovery and cold contrast recovery were measured from the phantom data. Two experienced nuclear medicine physicians reviewed the clinical images blindly. Liver SNR liver and SUVmax of the smallest lesion detected in each patient were also measured. The relationship between the maximum and mean hot concentration recovery remained monotonic below 1.5 maximum concentration recovery. The mean cold contrast recovery remained stable even for decreasing statistics with a highest absolute difference of 4% in air and 2% in bone for each reconstruction method. The D600 images resulted in an average 30% higher SNR than the D690 data for BSREM; there was no difference in SNR results between the two scanners with OSEM. The small lesion SUVmax values on the BSREM images with β of 250, 350 and 450, respectively were on average 80%, 60% and 43% (D690) and 42%, 29%, and 21% (D600) higher than in the case of OSEM. In conclusion, BSREM can outperform OSEM in terms of contrast recovery and organ uniformity over a range of PET tracers, but a task dependent regularization strength parameter (beta) selection may be necessary. To avoid image noise and artifacts, our results suggest that using higher beta values (at least 350) may be appropriate, especially if the data has low count statistics.
Positron emission tomography (PET) image denoising is a challenging task due to the presence of noise and low spatial resolution compared with other imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT). PET image noise can hamper further processing and analysis, such as segmentation and disease screening. The wavelet transform–based techniques have often been proposed for PET image denoising to handle isotropic (smooth details) features. The curvelet transform–based PET image denoising techniques have the ability to handle multi-scale and multi-directional properties such as edges and curves (anisotropic features) as compared with wavelet transform–based denoising techniques. The wavelet denoising method is not optimal for anisotropic features, whereas the curvelet denoising method sometimes has difficulty in handling isotropic features. In order to handle the weaknesses of individual wavelet and curvelet-based methods, the present research proposes an efficient PET image denoising technique based on the combination of wavelet and curvelet transforms, along with a new adaptive threshold selection to threshold the wavelet coefficients in each subband (except last level low pass (LL) residual). The proposed threshold utilizes the advantages of adaptive threshold taken from BayesShrink along with the neighborhood window concept. The present method was tested on both simulated phantom and clinical PET datasets. Experimental results show that our method has achieved better results than the existing methods such as VisuShrink, BayesShrink, NeighShrink, ModineighShrink, curvelet, and an existing wavelet curvelet-based method with respect to different noise measurement metrics, such as mean squared error (MSE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), and image quality index (IQI). Furthermore, notable performance is achieved in the case of medical applications such as gray matter segmentation and precise tumor region identification.
Oncologic 18F-FDG PET/CT acquisition and reconstruction protocols need to be optimized for both quantitative and detection tasks. To date, most studies have focused on either quantification or noise, leading to quantitative harmonization guidelines or appropriate noise levels. We developed and evaluated protocols that provide harmonized quantitation with optimal amounts of noise as a function of acquisition parameters and body mass. Methods: Multiple image acquisitions (n = 17) of the International Electrotechnical Commission/National Electrical Manufacturers Association PET image-quality phantom were performed with variable counting statistics. Phantom images were reconstructed with 3-dimensional ordered-subset expectation maximization (OSEM3D) and point-spread function (PSF) for harmonized quantification of the contrast recovery coefficient of the maximum pixel value (CRC
max
). The lowest counting statistics that resulted in compliance with European Association of Nuclear Medicine recommendations for CRC
max
and CRC
max
variability were used as optimization metrics. Image noise in the liver of 48 typical oncologic 18F-FDG PET/CT studies was analyzed with OSEM3D and PSF harmonized reconstructions. We also evaluated 164 additional 18F-FDG PET/CT reconstructed list-mode images to derive analytic expressions that predict image quality and noise variability. Phantom-to-subject translational analysis was used to derive optimized acquisition and reconstruction protocols. Results: For harmonized quantitation levels, PSF reconstructions yielded decreased noise and lower CRC
max
variability than regular OSEM3D reconstructions, suggesting they could enable a decreased activity regimen for matched performance. Conclusion: PSF reconstruction with a 7-mm postprocessing filter can provide harmonized quantification performance and acceptable image noise levels with injected activity, duration, and mass settings using a 260 MBq⋅s/kg acquisition parameter at scan time. Similarly, OSEM3D with a 5-mm postprocessing filter can provide similar performance with 401 MBq⋅s/kg.