Serious game for radiotherapy training.

BACKGROUND: Cancer patients are often treated with radiation, therefore increasing their exposure to high energy emissions. In such cases, medical errors may be threatening or fatal, inducing the need to innovate new methods for maximum reduction of irreversible events. Training is an efficient and methodical tool to subject professionals to the real world and heavily educate them on how to perform with minimal errors. An evolving technique for this is Serious Gaming that can fulfill this purpose, especially with the rise of COVID-19 and the shift to the online world, by realistic and visual simulations built to present engaging scenarios. This paper presents the first Serious Game for Lung Cancer Radiotherapy training that embodies Biomedical Engineering principles and clinical experience to create a realistic and precise platform for coherent training. METHODS: To develop the game, thorough 3D modeling, animation, and gaming fundamentals were utilized to represent the whole clinical process of treatment, along with the scores and progress of every player. The model's goal is to output coherency and organization for students' ease of use and progress tracking, and to provide a beneficial educational experience supplementary to the users' training. It aims to also expand their knowledge and use of skills in critical cases where they must perform crucial decision-making and procedures on patients of different cases. RESULTS: At the end of this research, one of the accomplished goals consists of building a realistic model of the different equipment and tools accompanied with the radiotherapy process received by the patient on Maya 2018, including the true beam table, gantry, X-ray tube, CT Scanner, and so on. The serious game itself was then implemented on Unity Scenes with the built models to create a gamified authentic environment that incorporates the 5 main series of steps; Screening, Contouring, External Beam Planning, Plan Evaluation, Treatment, to simulate the practical workflow of an actual Oncology treatment delivery for lung cancer patients. CONCLUSION: This serious game provides an educational and empirical space for training and practice that can be used by students, trainees, and professionals to expand their knowledge and skills in the aim of reducing potential errors.

National Diagnostic Reference Levels for Nuclear Medicine in Qatar.

Nuclear medicine (NM) started in Qatar in the mid-1980s with a 1-head γ-camera in Hamad General Hospital. However, Qatar is expanding, and now Hamad Medical Corp. has 2 NM departments and 1 PET/CT Center for Diagnosis and Research, with several hybrid SPECT/CT and PET/CT cameras. Furthermore, 2 new NM departments will be established in Qatar in the coming 3 y. Therefore, there is a need to optimize radiation protection in NM imaging and establish diagnostic reference levels (DRLs) for the first time in Qatar. This need is not only for the NM part of the examination but also for the CT part, especially in hybrid SPECT/CT and PET/CT. Methods: Data for adult patients were collected from the 3 SPECT/CT machines in the 2 NM facilities and from the 2 PET/CT machines in the PET/CT center. The 75th percentile values (also known as the third quartile) were considered preliminary DRLs and were consistent with the most commonly administered activities. The results for various general NM protocols were described, especially 99mTc-based radiopharmaceuticals and PET/CT protocols including mainly oncologic applications. Results: The first DRLs for NM imaging in Qatar adults were established. The values agreed with other published DRLs, as was the case, for example, for PET oncology using 18F-FDG, with DRLs of 258, 230, 370, 400, and 461-710 MBq for Qatar, Kuwait, Korea, the United Kingdom, and the United States, respectively. Similarly, for cardiac stress or rest myocardial perfusion imaging using 99mTc-methoxyisobutylisonitrile, the DRLs were 926, 976, 1,110, 800, and 945-1,402 MBq for Qatar, Kuwait, Korea, the United Kingdom, and the United States, respectively. Conclusion: The optimization of administered activity that this study will enable for NM procedures in Qatar will be of great value, especially for new departments that adhere to these DRLs.

An update on computational anthropomorphic anatomical models.

The prevalent availability of high-performance computing coupled with validated computerized simulation platforms as open-source packages have motivated progress in the development of realistic anthropomorphic computational models of the human anatomy. The main application of these advanced tools focused on imaging physics and computational internal/external radiation dosimetry research. This paper provides an updated review of state-of-the-art developments and recent advances in the design of sophisticated computational models of the human anatomy with a particular focus on their use in radiation dosimetry calculations. The consolidation of flexible and realistic computational models with biological data and accurate radiation transport modeling tools enables the capability to produce dosimetric data reflecting actual setup in clinical setting. These simulation methodologies and results are helpful resources for the medical physics and medical imaging communities and are expected to impact the fields of medical imaging and dosimetry calculations profoundly.

Assessing Bone Marrow Activity with [18F]FLT PET in Patients with Essential Thrombocythemia and Prefibrotic Myelofibrosis: A Proof of Concept.

Objectives: This study aims to assess the value of FLT-PET as a non-invasive tool to differentiate between patients with ET and Pre-PMF. This study is a pilot study to have a proof of concept only. Methods: This is a prospective, interventional study where a total of 12 patients were included. Each patient underwent FLT PET imaging as well as bone marrow examination (gold standard). In addition, semi-quantitative (SUVmax and SUVmean) measurements of FLT uptake in the liver, spleen, and Lspine, SUVmean, as well as the Total Lesion Glycolysis (TLG) of the Lspine were performed. Results from the two patient cohorts were compared using = Kruskal-Wallis statistical test. A P-value of <.05 is considered to be statistically significant. Results: The differences in FLT SUVmax and SUVmean measurements in the three organs (liver, spleen, and LSpine) between the ET and Pre-PMF patients were not statistically significant (P > .05). In contrast, TLG measurements in the LSpine were statistically different (P = .013), and therefore, compared to gold standard bone marrow results, TLG can separate ET and Pre-PMF patients. Conclusion: This study is a proof of concept about the potential to discriminate between ET and pre-PMF patients in a non-invasive way. TLG of the LSpine in FLT PET images is a potential quantitative parameter to distinguish between ET and pre-PMF patients.

Technical note: Surface imaging for real-time patient positioning in external radiation therapy.

PURPOSE: In the last few years, there has been a growing interest in surface imaging for patient positioning in external radiation therapy. The aim of this study is to evaluate the accuracy of daily patient positioning using the Azure Kinect surface imaging. METHODS: A total of 50 fractions in 10 patients including lung, pelvic, and head and neck tumors were analyzed in real time. A rigid registration algorithm, based on the iterative closest point (ICP) approach, is employed to estimate the patient position in 6 degrees of freedom (DOF). This position is compared to the reference values obtained by the radiograph imaging. The mean setup error and its standard deviation were calculated for all measured fractions. RESULTS: The positioning error showed 1.1 ± 1.1 mm in lateral, 1.8 ± 2.1 mm in longitudinal, and 0.8 ± 1.1 mm in vertical, and 0.3°± 0.4° in yaw, 0.2°± 0.2° in pitch, and 0.2°± 0.2° in roll directions. The larger setup error occurred in pelvic regions. CONCLUSION: We have evaluated in a radiotherapy set-up considering different patient anatomical locations, a depth measurement based surface imaging solution for patient positioning considering the 6 DOF couch motion. We showed that the proposed solution allows an accurate patient positioning without the need for patient markings or the use of additional radiation dose.

Surface imaging for real-time patient respiratory function assessment in intensive care.

PURPOSE: Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients. METHODS: The proposed system uses a Time-of-Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital. RESULTS: The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients. CONCLUSION: This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients.

A study of 18F-FLT positron emission tomography/computed tomography imaging in cases of prefibrotic/early primary myelofibrosis and essential thrombocythemia.

The objectives of this research project are to study in patients with primary myelofibrosis (PMF) and Essential Thrombocythemia (ET); (1) the uptake patterns of FLT-PET (FLT-PET) and its value in diagnosing, staging, and treatment response monitoring of malignant hematopoiesis, (2) compare imaging findings from FLT-PET with bone marrow biopsy (standard of care), and (3) associate FLT-PET uptake patterns with genetic makeup such as JAK2 (Janus kinase 2), CALR (Calreticulin), MPL (myeloproliferative leukemia protein), Triple negative disease, and allele burden.This trial is registered in ClinicalTrials.gov with number NCT03116542. Protocol version: Mar 2017.

Thoracic Stent-Graft Migration: The Role of the Geometric Modifications of the Stent-Graft at 3 years.

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.

A 4D global respiratory motion model of the thorax based on CT images: A proof of concept.

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.

Development of a Dedicated Rebinner with Rigid Motion Correction for the mMR PET/MR Scanner, and Validation in a Large Cohort of 11C-PIB Scans.

Head motion occurring during brain PET studies leads to image blurring and to bias in measured local quantities. The objective of this work was to implement a correction method for PET data acquired with the mMR synchronous PET/MR scanner. Methods: A list-mode-based motion-correction approach has been designed. The developed rebinner chronologically reads the recorded events from the Siemens list-mode file, applies the estimated geometric transformations, and frames the detected counts into sinograms. The rigid-body motion parameters were estimated from an initial dynamic reconstruction of the PET data. We then optimized the correction for 11C-Pittsburgh compound B (11C-PIB) scans using simulated and actual data with well-controlled motion. Results: An efficient list-mode-based motion correction approach has been implemented, fully optimized, and validated using simulated and actual PET data. The average spatial resolution loss induced by inaccuracies in motion parameter estimates and by the rebinning process was estimated to correspond to a 1-mm increase in full width at half maximum with motion parameters estimated directly from the PET data with a temporal frequency of 20 s. The results show that the rebinner can be safely applied to the 11C-PIB scans, allowing almost complete removal of motion-induced artifacts. The application of the correction method to a large cohort of 11C-PIB scans led to the following observations: first, that more than 21% of the scans were affected by motion greater than 10 mm (39% for subjects with Mini-Mental State Examination scores below 20), and second, that the correction led to quantitative changes in Alzheimer-specific cortical regions of up to 30%. Conclusion: The rebinner allows accurate motion correction at a cost of minimal resolution reduction. Application of the correction to a large cohort of 11C-PIB scans confirmed the necessity of systematically correcting for motion to obtain quantitative results.

Technical Note: Kinect V2 surface filtering during gantry motion for radiotherapy applications.

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.

Geometric accuracy of the MR imaging techniques in the presence of motion.

Magnetic Resonance Imaging (MRI) is increasingly being used for improving tumor delineation and tumor tracking in the presence of respiratory motion. The purpose of this work is to design and build an MR compatible motion platform and to use it for evaluating the geometric accuracy of MR imaging techniques during respiratory motion. The motion platform presented in this work is composed of a mobile base made up of a flat plate and four wheels. The mobile base is attached from one end and through a rigid rod to a synchrony motion table by Accuray® placed at the end of the MRI table and from the other end to an elastic rod. The geometric accuracy was measured by placing a control point-based phantom on top of the mobile base. In-house software module was used to automatically assess the geometric distortion. The blurring artifact was also assessed by measuring the Full Width Half Maximum (FWHM) of each control point. Our results were assessed for 50, 100, and 150 mm radial distances, with a mean geometric distortion during the superior-inferior motion of 0.27, 0.41, and 0.55 mm, respectively. Adding the anterior-posterior motion, the mean geometric distortions increased to 0.4, 0.6, and 0.8 mm. Blurring was observed during motion causing an increase in the FWHM of ≈30%. The platform presented in this work provides a valuable tool for the assessment of the geometric accuracy and blurring artifact for MR during motion. Although the main objective was to test the spatial accuracy of an MR system during motion, the modular aspect of the presented platform enables the use of any commercially available phantom for a full quality control of the MR system during motion.

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.

Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method.

PURPOSE: Sphericity has been proposed as a parameter for characterizing PET tumour volumes, with complementary prognostic value with respect to SUV and volume in both head and neck cancer and lung cancer. The objective of the present study was to investigate its dependency on tumour delineation and the resulting impact on its prognostic value. METHODS: Five segmentation methods were considered: two thresholds (40% and 50% of SUVmax), ant colony optimization, fuzzy locally adaptive Bayesian (FLAB), and gradient-aided region-based active contour. The accuracy of each method in extracting sphericity was evaluated using a dataset of 176 simulated, phantom and clinical PET images of tumours with associated ground truth. The prognostic value of sphericity and its complementary value with respect to volume for each segmentation method was evaluated in a cohort of 87 patients with stage II/III lung cancer. RESULTS: Volume and associated sphericity values were dependent on the segmentation method. The correlation between segmentation accuracy and sphericity error was moderate (|ρ| from 0.24 to 0.57). The accuracy in measuring sphericity was not dependent on volume (|ρ| < 0.4). In the patients with lung cancer, sphericity had prognostic value, although lower than that of volume, except for that derived using FLAB for which when combined with volume showed a small improvement over volume alone (hazard ratio 2.67, compared with 2.5). Substantial differences in patient prognosis stratification were observed depending on the segmentation method used. CONCLUSION: Tumour functional sphericity was found to be dependent on the segmentation method, although the accuracy in retrieving the true sphericity was not dependent on tumour volume. In addition, even accurate segmentation can lead to an inaccurate sphericity value, and vice versa. Sphericity had similar or lower prognostic value than volume alone in the patients with lung cancer, except when determined using the FLAB method for which there was a small improvement in stratification when the parameters were combined.

MR-based respiratory and cardiac motion correction for PET imaging.

PURPOSE: To develop a motion correction for Positron-Emission-Tomography (PET) using simultaneously acquired magnetic-resonance (MR) images within 90 s. METHODS: A 90 s MR acquisition allows the generation of a cardiac and respiratory motion model of the body trunk. Thereafter, further diagnostic MR sequences can be recorded during the PET examination without any limitation. To provide full PET scan time coverage, a sensor fusion approach maps external motion signals (respiratory belt, ECG-derived respiration signal) to a complete surrogate signal on which the retrospective data binning is performed. A joint Compressed Sensing reconstruction and motion estimation of the subsampled data provides motion-resolved MR images (respiratory + cardiac). A 1-POINT DIXON method is applied to these MR images to derive a motion-resolved attenuation map. The motion model and the attenuation map are fed to the Customizable and Advanced Software for Tomographic Reconstruction (CASToR) PET reconstruction system in which the motion correction is incorporated. All reconstruction steps are performed online on the scanner via Gadgetron to provide a clinically feasible setup for improved general applicability. The method was evaluated on 36 patients with suspected liver or lung metastasis in terms of lesion quantification (SUVmax, SNR, contrast), delineation (FWHM, slope steepness) and diagnostic confidence level (3-point Likert-scale). RESULTS: A motion correction could be conducted for all patients, however, only in 30 patients moving lesions could be observed. For the examined 134 malignant lesions, an average improvement in lesion quantification of 22%, delineation of 64% and diagnostic confidence level of 23% was achieved. CONCLUSION: The proposed method provides a clinically feasible setup for respiratory and cardiac motion correction of PET data by simultaneous short-term MRI. The acquisition sequence and all reconstruction steps are publicly available to foster multi-center studies and various motion correction scenarios.

Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation.

PURPOSE: The aim of this paper is to define the requirements and describe the design and implementation of a standard benchmark tool for evaluation and validation of PET-auto-segmentation (PET-AS) algorithms. This work follows the recommendations of Task Group 211 (TG211) appointed by the American Association of Physicists in Medicine (AAPM). METHODS: The recommendations published in the AAPM TG211 report were used to derive a set of required features and to guide the design and structure of a benchmarking software tool. These items included the selection of appropriate representative data and reference contours obtained from established approaches and the description of available metrics. The benchmark was designed in a way that it could be extendable by inclusion of bespoke segmentation methods, while maintaining its main purpose of being a standard testing platform for newly developed PET-AS methods. An example of implementation of the proposed framework, named PETASset, was built. In this work, a selection of PET-AS methods representing common approaches to PET image segmentation was evaluated within PETASset for the purpose of testing and demonstrating the capabilities of the software as a benchmark platform. RESULTS: A selection of clinical, physical, and simulated phantom data, including "best estimates" reference contours from macroscopic specimens, simulation template, and CT scans was built into the PETASset application database. Specific metrics such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity (S), were included to allow the user to compare the results of any given PET-AS algorithm to the reference contours. In addition, a tool to generate structured reports on the evaluation of the performance of PET-AS algorithms against the reference contours was built. The variation of the metric agreement values with the reference contours across the PET-AS methods evaluated for demonstration were between 0.51 and 0.83, 0.44 and 0.86, and 0.61 and 1.00 for DSC, PPV, and the S metric, respectively. Examples of agreement limits were provided to show how the software could be used to evaluate a new algorithm against the existing state-of-the art. CONCLUSIONS: PETASset provides a platform that allows standardizing the evaluation and comparison of different PET-AS methods on a wide range of PET datasets. The developed platform will be available to users willing to evaluate their PET-AS methods and contribute with more evaluation datasets.

4-Dimensional MRI and Attenuation Map Generation in PET/MRI with 4-Dimensional PET-Derived Deformation Matrices: Study of Feasibility for Lung Cancer Applications.

Respiratory motion may reduce accuracy in the fusion of functional and anatomic images from combined PET/MRI systems. Methodologies for the correction of respiratory motion in PET acquisitions with such systems are mostly based on the use of respiration-synchronized MRI acquisitions to derive motion fields. Existing approaches based on tagging acquisitions may introduce artifacts in MR images, whereas motion model approaches require the acquisition of training datasets. The objective of this work was to investigate the possibility of generating 4-dimensional (4D) MR images and associated attenuation maps (AMs) from the combination of a single static MR image and motion fields obtained from simultaneously acquired 4D non-attenuation-corrected (NAC) PET images. Methods: Four-dimensional PET/MRI datasets were acquired for 11 patients on a simultaneous PET/MRI system. The 4D PET datasets were retrospectively binned into 4 motion amplitude frames corresponding to the simultaneously acquired T1-weighted 4D MR images. A T1-weighted 3-dimensional MRI sequence with Dixon-based fat and water separation was also acquired at the end of expiration for PET attenuation correction purposes. All reconstructed 4D NAC PET images were then elastically registered to the single end-of-expiration NAC PET image. The derived motion fields were subsequently applied to the end-of-expiration frame of the acquired 4D MRI volume and the AM derived from the Dixon MR image to generate respiration-synchronized MR images and corresponding AMs. Results: The accuracy of the proposed method was assessed by comparing the generated and acquired images according to metrics such as overall correlation coefficients and differences in distances of anatomic landmarks on the generated and acquired MRI datasets. High correlation coefficients (mean ± SD: 0.93 ± 0.03) and small differences (2.69 ± 0.5 mm) were obtained. Moreover, small tissue classification differences (2.23% ± 0.68%) between generated and 4D MRI-extracted AMs were observed. Conclusion: Our results confirm the feasibility of using 4D NAC PET images for accurate PET attenuation correction and respiratory motion correction in PET/MRI, without the need for patient-specific 4D MRI acquisitions.

Motion correction using anatomical information in PET/CT and PET/MR hybrid imaging.

Respiratory and cardiac motion causes qualitative and quantitative inaccuracies in whole body multi-modality imaging such as positron emission tomography coupled with computed tomography (PET/CT) and positron emission tomography/magnetic resonance imaging (PET/MRI). Solutions presented to date include motion synchronized PET and corresponding anatomical acquisitions (four dimensional [4D] PET/CT, 4D PET/MR), frequently referred to as the gating approach. This method is based on the acquisition of an external surrogate using an external device (pressure belt, optical monitoring system, spirometer etc.), subsequently used to bin PET and CT or MR anatomical data into a number of gates. A first limitation of this method is the low signal to noise ratio (SNR) of the resulting motion synchronized PET frames, given that every reconstructed frame contains only part of the count statistics available throughout a motion average PET acquisition. Another limitation is that the complex motion of internal organs cannot be fully estimated, characterized and modelled using a mono-dimensional motion signal. In order to resolve such issues, many advanced techniques have been proposed which include three consecutive major steps. These are based on firstly acquiring an external or internal motion surrogate, estimating or modelling the internal motion using anatomical information extracted from 4D anatomical images (CT and/or MR) and finally correcting for motion either in the PET raw data space, the image space or incorporate it within the PET image reconstruction which is the most optimal based motion correction method in PET/CT and in PET/MR imaging. Current research efforts are concentrating on combining the last two steps within a joint motion estimation/motion correction approach, the exploitation of MRI specific motion characterization sequences and the combination of both respiratory and cardiac motion corrections. The goal of this review is to present and discuss the different steps of all these motion correction methods in PET/CT and PET/MR imaging for whole body applications.

Kinect2 - respiratory movement detection study.

Radiotherapy is one of the main cancer treatments. It consists in irradiating tumor cells to destroy them while sparing healthy tissue. The treatment is planned based on Computed Tomography (CT) and is delivered over fractions during several days. One of the main challenges is replacing patient in the same position every day to irradiate the tumor volume while sparing healthy tissues. Many patient positioning techniques are available. They are both invasive and not accurate performed using tattooed marker on the patient's skin aligned with a laser system calibrated in the treatment room or irradiating using X-ray. Currently systems such as Vision RT use two Time of Flight cameras. Time of Flight cameras have the advantage of having a very fast acquisition rate allows the real time monitoring of patient movement and patient repositioning. The purpose of this work is to test the Microsoft Kinect2 camera for potential use for patient positioning and respiration trigging. This type of Time of Flight camera is non-invasive and costless which facilitate its transfer to clinical practice.