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1.
J Nucl Med ; 60(Suppl 2): 38S-44S, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31481588

RESUMO

The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y.

3.
Phys Med Biol ; 2019 Aug 15.
Artigo em Inglês | MEDLINE | ID: mdl-31416053

RESUMO

We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photon interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality of our approach is to exploit simulations to obtain reference data, in combination with a variability reduction that the network ensembles offer, thus, removing the need of extensive per-detector calibration measurements. This procedure delivers an ensemble valid for any detector of the same design. We show the capability of the ensemble to solve the 3D positioning problem through testing four different detector designs with Monte Carlo data, measurements from physical detectors and reconstructed images from the MindView scanner. Network ensembles allow the detector to achieve a 2-2.4~mm FWHM, depending on its design, and the associated reconstructed images present improved SNR, CNR and SSIM when compared to those based on the MindView built-in positioning algorithm.

4.
Artigo em Inglês | MEDLINE | ID: mdl-31280350

RESUMO

Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.

5.
Sci Rep ; 9(1): 9743, 2019 Jul 05.
Artigo em Inglês | MEDLINE | ID: mdl-31278324

RESUMO

Radiogenomics aims at investigating the relationship between imaging radiomic features and gene expression alterations. This study addressed the potential prognostic complementary value of contrast enhanced computed tomography (CE-CT) radiomic features and gene expression data in primary colorectal cancers (CRC). Sixty-four patients underwent CT scans and radiomic features were extracted from the delineated tumor volume. Gene expression analysis of a small set of genes, previously identified as relevant for CRC, was conducted on surgical samples from the same tumors. The relationships between radiomic and gene expression data was assessed using the Kruskal-Wallis test. Multiple testing was not performed, as this was a pilot study. Cox regression was used to identify variables related to overall survival (OS) and progression free survival (PFS). ABCC2 gene expression was correlated with N (p = 0.016) and M stages (p = 0.022). Expression changes of ABCC2, CD166, CDKNV1 and INHBB genes exhibited significant correlations with some radiomic features. OS was associated with Ratio 3D Surface/volume (p = 0.022) and ALDH1A1 expression (p = 0.042), whereas clinical stage (p = 0.004), ABCC2 expression (p = 0.035), and EntropyGLCM_E (p = 0.0031), were prognostic factors for PFS. Combining CE-CT radiomics with gene expression analysis and histopathological examination of primary CRC could provide higher prognostic stratification power, leading to improved patient management.

6.
Artigo em Inglês | MEDLINE | ID: mdl-31170066

RESUMO

Standard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most of these methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified loglikelihood, and therefore break the data statistics. In this work we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a maximizer which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions.

7.
Radiother Oncol ; 133: 16-19, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30935573

RESUMO

Hypoxia is a major risk factor of prostate cancer radioresistance. We evaluated hypoxia non-invasively, using 18F-Misonidazole PET/CT prior to radiotherapy and after a dose of 20 Gy in intermediate-risk prostate cancer patients. Decreased hypoxic volumes were observed in all patients, suggesting that radiotherapy induces early prostate tumor reoxygenation.

8.
Ann Vasc Surg ; 58: 16-23, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-30684612

RESUMO

BACKGROUND: To date, clinical and experimental studies on stent graft (SG) migration have focused on aortic morphology and blood flow. However, thoracic endovascular aortic repair (TEVAR) is not an instant fixation of the SG in the aortic lumen but rather a continuous process of deformation and three-dimensional change in the configuration and the geometry of the SG. The aim of this study was to analyze the geometric evolution of the aortic SG in the proximal attachment zone at midterm follow-up and its impact on the SG migration. METHODS: Sixty-two patients underwent TEVAR for thoracic aortic aneurysm from 2007 till 2013. Thirty patients were treated and had a complete clinical and morphological follow-up at 1 month and 3 years. We calculated the SG radius of curvature (RC) change at the proximal attachment zone "P" on the postoperative computed tomography scan at 1 month and 3 years. RESULTS: There were 19 atheromatous aneurysms, 8 postdissection aneurysms, and 3 posttraumatic aneurysms. Two patients were treated at zone 1, seven at zone 2, and twenty-one at zone 3. The median decrease of the RC at "P" was 11 mm (interquartile range, 6.5 mm; range, 1-29 mm. A greater decrease in RC was identified in patients with hostile proximal neck having a large diameter (P = 0.006), short neck length (P = 0.04), and neck thrombus grade II and III (P = 0.02). In the migration group, the RC of "P" decreased significantly at 3 years (27.5 mm vs 18.25 mm; P = 0.03). Three patients had type I endoleak and showed a decrease of the RC at "P" (42 vs 13 mm; 28 vs 15 mm; 24 vs 9 mm). CONCLUSIONS: The SG seems to have geometric changes in the proximal attachment zone over time. The increase of SG curvature might be a predictor for SG migration and may prompt prophylactic reintervention.


Assuntos
Aneurisma da Aorta Torácica/cirurgia , Implante de Prótese Vascular/efeitos adversos , Implante de Prótese Vascular/instrumentação , Prótese Vascular , Procedimentos Endovasculares/efeitos adversos , Procedimentos Endovasculares/instrumentação , Migração de Corpo Estranho/etiologia , Falha de Prótese , Stents , Idoso , Aneurisma da Aorta Torácica/diagnóstico por imagem , Aortografia/métodos , Angiografia por Tomografia Computadorizada , Endoleak/etiologia , Feminino , Migração de Corpo Estranho/diagnóstico por imagem , Humanos , Masculino , Pessoa de Meia-Idade , Desenho de Prótese , Estudos Retrospectivos , Fatores de Risco , Fatores de Tempo , Resultado do Tratamento
9.
Eur J Nucl Med Mol Imaging ; 46(4): 864-877, 2019 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-30535746

RESUMO

PURPOSE: The aim of this study was to validate previously developed radiomics models relying on just two radiomics features from 18F-fluorodeoxyglucose positron emission tomography (PET) and magnetic resonance imaging (MRI) images for prediction of disease free survival (DFS) and locoregional control (LRC) in locally advanced cervical cancer (LACC). METHODS: Patients with LACC receiving chemoradiotherapy were enrolled in two French and one Canadian center. Pre-treatment imaging was performed for each patient. Multicentric harmonization of the two radiomics features was performed with the ComBat method. The models for DFS (using the feature from apparent diffusion coefficient (ADC) MRI) and LRC (adding one PET feature to the DFS model) were tuned using one of the French cohorts (n = 112) and applied to the other French (n = 50) and the Canadian (n = 28) external validation cohorts. RESULTS: The DFS model reached an accuracy of 90% (95% CI [79-98%]) (sensitivity 92-93%, specificity 87-89%) in both the French and the Canadian cohorts. The LRC model reached an accuracy of 98% (95% CI [90-99%]) (sensitivity 86%, specificity 100%) in the French cohort and 96% (95% CI [80-99%]) (sensitivity 83%, specificity 100%) in the Canadian cohort. Accuracy was significantly lower without ComBat harmonization (82-85% and 71-86% for DFS and LRC, respectively). The best prediction using standard clinical variables was 56-60% only. CONCLUSIONS: The previously developed PET/MRI radiomics predictive models were successfully validated in two independent external cohorts. A proposed flowchart for improved management of patients based on these models should now be confirmed in future larger prospective studies.

11.
Artigo em Inglês | MEDLINE | ID: mdl-30315873

RESUMO

PURPOSE: Inverse planning is an integral part of modern low-dose-rate (LDR) brachytherapy. Current clinical planning systems do not exploit the total dose information and largely use the AAPM TG-43 dosimetry formalism to ensure clinically acceptable planning times. Thus, sub-optimal plans may be derived due to TG-43-related dose overestimation and non-conformity with dose distribution requirements. The purpose of this study was to propose an inverse planning approach that can improve planning quality by combining dose-volume information and precision without compromising the overall execution times. METHODS AND MATERIALS: The dose map was generated by accumulating precomputed Monte Carlo Dose Kernels (MCDKs) for each candidate source implantation site. The Monte Carlo (MC) computational burden was reduced by using GPU acceleration allowing accurate dosimetry calculations to be performed in the intraoperative environment. The proposed DVH-FSA optimization algorithm was evaluated using clinical plans that were delivered to 18 patients who underwent LDR prostate brachytherapy. RESULTS: Our method generated plans in 37.5 ± 3.2 s with similar prostate dose coverage, improved prostate dose homogeneity up to 6.1%, and lower dose to the urethra up to 4.0%. CONCLUSIONS: A DVH-based optimization algorithm using MC dosimetry was developed. The inclusion of the DVH requirements allowed for increased control over the optimization outcome. The optimal plan's quality was further improved by considering tissue heterogeneity.

12.
Artigo em Inglês | MEDLINE | ID: mdl-30113888

RESUMO

OBJECTIVE: We present a new hybrid edge and region-based parametric deformable model, or active surface, for prostate volume segmentation in transrectal ultrasound (TRUS) images. METHODS: Our contribution is threefold. First, we develop a new edge detector derived from the radial bas-relief approach, allowing for better scalar prostate edge detection in low contrast configurations. Second, we combine an edge-based force derived from the proposed edge detector with a new region-based force driven by the Bhattacharyya gradient flow and adapted to the case of parametric active surfaces. Finally, we develop a quasi-automatic initialization technique for deformable models by analyzing the profiles of the proposed edge detector response radially to obtain initial landmark points towards which an initial surface model is warped. RESULTS: We validate our method on a set of 36 TRUS images for which manual delineations were performed by two expert radiation oncologists, using a wide variety of quantitative metrics. The proposed hybrid model achieved state-of-the art segmentation accuracy. CONCLUSION: Results demonstrate the interest of the proposed hybrid framework for accurate prostate volume segmentation. SIGNIFICANCE: This paper presents a modular framework for accurate prostate volume segmentation in TRUS, broadening the range of available strategies to tackle this open problem.

13.
Phys Med Biol ; 63(18): 185005, 2018 Sep 10.
Artigo em Inglês | MEDLINE | ID: mdl-30113313

RESUMO

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multi-dimensional data. The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture. The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer's software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speed-up increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.

14.
Acad Radiol ; 2018 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-30072293

RESUMO

RATIONALE AND OBJECTIVES: The aim of our study was to assess the relationships between textural features extracted from contrast enhanced (CE) and noncontrast enhanced (NCE) computed tomography (CT) images of primary colorectal cancer, in order to identify radiomics features more likely to provide potential complementary information regarding outcome. MATERIALS AND METHODS: Sixty-one patients with primary colorectal cancer underwent both CE-CT and NCE-CT scans within the same acquisition. First-order and textural features (with three different methods for grey-level discretization) were extracted from the tumor volume in both modalities and their correlation was assessed with Spearman's rank correlation (rs). Significance was assessed at p < 0.05 with correction for multiple comparisons. Kaplan-Meier estimation and log-rank tests were used to identify features associated with long term patient survival. RESULTS: Moderate positive correlations were observed between CE-CT and NCE-CT histogram-derived entropy (EntropyHist) and area under the curve (CHAUC) (rs = 0.49, p < 0.001 and rs= 0.45, p < 0.001, respectively). Some second and third order textural features were found highly correlated between CE-CT and NCE-CT, such as small zone-size emphasis SZSE (rs = 0.729, p < 0.001) and zone-size percentage (rs = 0.770, p < 0.001). Grey-levels discretization methods influenced these correlations. A few of the third order NCE-CT and CE-CT features were significantly associated with survival. CONCLUSION: Some radiomics features with moderate correlations between nonenhanced and enhanced CT images were found to be associated with survival, thus suggesting that complementary prognostic value may be extracted from both modalities when available.

15.
Med Phys ; 45(7): 3043-3051, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-29772057

RESUMO

PURPOSE: Respiratory motion reduces the sensitivity and specificity of medical images especially in the thoracic and abdominal areas. It may affect applications such as cancer diagnostic imaging and/or radiation therapy (RT). Solutions to this issue include modeling of the respiratory motion in order to optimize both diagnostic and therapeutic protocols. Personalized motion modeling required patient-specific four-dimensional (4D) imaging which in the case of 4D computed tomography (4D CT) acquisition is associated with an increased dose. The goal of this work was to develop a global respiratory motion model capable of relating external patient surface motion to internal structure motion without the need for a patient-specific 4D CT acquisition. METHODS: The proposed global model is based on principal component analysis and can be adjusted to a given patient anatomy using only one or two static CT images in conjunction with a respiratory synchronized patient external surface motion. It is based on the relation between the internal motion described using deformation fields obtained by registering 4D CT images and patient surface maps obtained either from optical imaging devices or extracted from CT image-based patient skin segmentation. 4D CT images of six patients were used to generate the global motion model which was validated by adapting it on four different patients having skin segmented surfaces and two other patients having time of flight camera acquired surfaces. The reproducibility of the proposed model was also assessed on two patients with two 4D CT series acquired within 2 weeks of each other. RESULTS: Profile comparison shows the efficacy of the global respiratory motion model and an improvement while using two CT images in order to adapt the model. This was confirmed by the correlation coefficient with a mean correlation of 0.9 and 0.95 while using one or two CT images respectively and when comparing acquired to model generated 4D CT images. For the four patients with segmented surfaces, expert validation indicates an error of 2.35 ± 0.26 mm compared to 6.07 ± 0.76 mm when using a simple interpolation between full inspiration (FI) and full expiration (FE) CT only; i.e., without specific modeling of the respiratory motion. For the two patients with acquired surfaces, this error was of 2.48 ± 0.18 mm. In terms of reproducibility, model error changes of 0.12 and 0.17 mm were measured for the two patients concerned. CONCLUSIONS: The framework for the derivation of a global respiratory motion model was developed. A single or two static CT images and associated patient surface motion, as a surrogate measure, are only needed to personalize the model. This model accuracy and reproducibility were assessed by comparing acquired vs model generated 4D CT images. Future work will consist of assessing extensively the proposed model for radiotherapy applications.


Assuntos
Tomografia Computadorizada Quadridimensional/métodos , Movimento , Respiração , Tórax/diagnóstico por imagem , Humanos , Doses de Radiação
16.
IEEE Trans Med Imaging ; 37(4): 871-880, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29610067

RESUMO

We present a multi-scale approach of tumor modeling in order to predict its evolution during radiotherapy. Within this context we focus on three different scales of tumor modeling: microscopic (individual cells in a voxel), mesoscopic (population of cells in a voxel) and macroscopic (whole tumor), with transition interfaces between these three scales. At the cellular level, the description is based on phase transfer probabilities in the cellular cycle. At the mesoscopic scale we represent populations of cells according to different stages in a cell cycle. Finally, at the macroscopic scale, the tumor description is based on the use of FDG PET image voxels. These three scales exist naturally: biological data are collected at the macroscopic scale, but the pathological behavior of the tumor is based on an abnormal cell-cycle at the microscopic scale. On the other hand, the introduction of a mesoscopic scale is essential in order to reduce the gap between the two extreme, in terms of resolution, description levels. It also reduces the computational burden of simulating a large number of individual cells. As an application of the proposed multi-scale model, we simulate the effect of oxygen on tumor evolution during radiotherapy. Two consecutive FDG PET images of 17 rectal cancer patients undergoing radiotherapy are used to simulate the tumor evolution during treatment. The simulated results are compared with those obtained on a third FDG PET image acquired two weeks after the beginning of the treatment.

17.
Oncotarget ; 9(11): 10005-10015, 2018 Feb 09.
Artigo em Inglês | MEDLINE | ID: mdl-29515786

RESUMO

Purpose: Hypoxia is a major factor in prostate cancer aggressiveness and radioresistance. Predicting which patients might be bad candidates for radiotherapy may help better personalize treatment decisions in intermediate-risk prostate cancer patients. We assessed spatial distribution of 18F-Misonidazole (FMISO) PET/CT uptake in the prostate prior to radiotherapy treatment. Materials and Methods: Intermediate-risk prostate cancer patients about to receive high-dose (>74 Gy) radiotherapy to the prostate without hormonal treatment were prospectively recruited between 9/2012 and 10/2014. Prior to radiotherapy, all patients underwent a FMISO PET/CT as well as a MRI and 18F-choline-PET. 18F-choline and FMISO-positive volumes were semi-automatically determined using the fuzzy locally adaptive Bayesian (FLAB) method. In FMISO-positive patients, a dynamic analysis of early tumor uptake was performed. Group differences were assessed using the Wilcoxon signed rank test. Parameters were correlated using Spearman rank correlation. Results: Of 27 patients (median age 76) recruited to the study, 7 and 9 patients were considered positive at 2.5h and 3.5h FMISO PET/CT respectively. Median SUVmax and SUVmax tumor to muscle (T/M) ratio were respectively 3.4 and 3.6 at 2.5h, and 3.2 and 4.4 at 3.5h. The median FMISO-positive volume was 1.1 ml. Conclusions: This is the first study regarding hypoxia imaging using FMISO in prostate cancer showing that a small FMISO-positive volume was detected in one third of intermediate-risk prostate cancer patients.

18.
Eur Psychiatry ; 50: 21-27, 2018 04.
Artigo em Inglês | MEDLINE | ID: mdl-29398564

RESUMO

We present the first results of the MINDVIEW project. An innovative imaging system for the human brain examination, allowing simultaneous acquisition of PET/MRI images, has been designed and constructed. It consists of a high sensitivity and high resolution PET scanner integrated in a novel, head-dedicated, radio frequency coil for a 3T MRI scanner. Preliminary measurements from the PET scanner show sensitivity 3 times higher than state-of-the-art PET systems that will allow safe repeated studies on the same patient. The achieved spatial resolution, close to 1 mm, will enable differentiation of relevant brain structures for schizophrenia. A cost-effective and simple method of radiopharmaceutical production from 11C-carbon monoxide and a mini-clean room has been demonstrated. It has been shown that 11C-raclopride has higher binding potential in a new VAAT null mutant mouse model of schizophrenia compared to wild type control animals. A significant reduction in TSPO binding has been found in gray matter in a small sample of drug-naïve, first episode psychosis patients, suggesting a reduced number or an altered function of immune cells in brain at early stage schizophrenia.

19.
Med Phys ; 45(4): 1400-1407, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29419891

RESUMO

PURPOSE: In radiotherapy, the Kinect V2 camera, has recently received a lot of attention concerning many clinical applications including patient positioning, respiratory motion tracking, and collision detection during the radiotherapy delivery phase. However, issues associated with such applications are related to some materials and surfaces reflections generating an offset in depth measurements especially during gantry motion. This phenomenon appears in particular when the collimator surface is observed by the camera; resulting in erroneous depth measurements, not only in Kinect surfaces itself, but also as a large peak when extracting a 1D respiratory signal from these data. METHODS: In this paper, we proposed filtering techniques to reduce the noise effect in the Kinect-based 1D respiratory signal, using a trend removal filter, and in associated 2D surfaces, using a temporal median filter. Filtering process was validated using a phantom, in order to simulate a patient undergoing radiotherapy treatment while having the ground truth. RESULTS: Our results indicate a better correlation between the reference respiratory signal and its corresponding filtered signal (Correlation coefficient of 0.76) than that of the nonfiltered signal (Correlation coefficient of 0.13). Furthermore, surface filtering results show a decrease in the mean square distance error (85%) between the reference and the measured point clouds. CONCLUSION: This work shows a significant noise compensation and surface restitution after surface filtering and therefore a potential use of the Kinect V2 camera for different radiotherapy-based applications, such as respiratory tracking and collision detection.


Assuntos
Movimento (Física) , Radioterapia/instrumentação , Artefatos , Humanos , Respiração , Processamento de Sinais Assistido por Computador
20.
Phys Med Biol ; 63(4): 045012, 2018 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-29339575

RESUMO

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a temporal regularization in the reconstruction process of the PET dynamic series led to substantial quantitative improvements and motion artifact reduction. Future work will include the integration of a linear FDG kinetic model, in order to directly reconstruct parametric images.

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