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.

PET respiratory motion correction: quo vadis?

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

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.

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.

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.

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.

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.

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.

Local respiratory motion correction for PET/CT imaging: Application to lung cancer

PURPOSE

Despite multiple methodologies already proposed to correct respiratory motion in the whole PET imaging field of view (FOV), such approaches have not found wide acceptance in clinical routine. An alternative can be the local respiratory motion correction (LRMC) of data corresponding to a given volume of interest (VOI: organ or tumor). Advantages of LRMC include the use of a simple motion model, faster execution times, and organ specific motion correction. The purpose of this study was to evaluate the performance of LMRC using various motion models for oncology (lung lesion) applications.

METHODS

Both simulated (NURBS based 4D cardiac-torso phantom) and clinical studies (six patients) were used in the evaluation of the proposed LRMC approach. PET data were acquired in list-mode and synchronized with respiration. The implemented approach consists first in defining a VOI on the reconstructed motion average image. Gated PET images of the VOI are subsequently reconstructed using only lines of response passing through the selected VOI and are used in combination with a center of gravity or an affine/elastic registration algorithm to derive the transformation maps corresponding to the respiration effects. Those are finally integrated in the reconstruction process to produce a motion free image over the lesion regions.

RESULTS

Although the center of gravity or affine algorithm achieved similar performance for individual lesion motion correction, the elastic model, applied either locally or to the whole FOV, led to an overall superior performance. The spatial tumor location was altered by 89% and 81% for the elastic model applied locally or to the whole FOV, respectively (compared to 44% and 39% for the center of gravity and affine models, respectively). This resulted in similar associated overall tumor volume changes of 84% and 80%, respectively (compared to 75% and 71% for the center of gravity and affine models, respectively). The application of the nonrigid deformation model in LRMC led to over an order of magnitude gain in computational efficiency of the correction relative to the application of the deformable model to the whole FOV.

CONCLUSIONS

The results of this study support the use of LMRC as a flexible and efficient correction approach for respiratory motion effects for single lesions in the thoracic area.

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.

Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications

Simultaneous PET and MR imaging is a promising new technique allowing the fusion of functional (PET) and anatomic/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames that lead to low signal-to-noise ratio considering the associated low statistics. More advanced correction approaches, previously developed for PET/CT imaging, consist of either registering all the reconstructed gated frames to the reference frame or incorporating motion parameters into the iterative reconstruction process to produce a single motion-compensated PET image. The goal of this work was to compare these two—previously implemented in PET/CT—correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4-dimensional PET/MR acquisitions. Two different correction approaches were evaluated comparing the incorporation of elastic transformations extracted from 4-dimensional MR imaging datasets during PET list-mode image reconstruction to a postreconstruction image-based approach.

METHODS

Eleven patient datasets acquired on a PET/MR system were used. T1-weighted 4D MR images were registered to the end-expiration image using a nonrigid B-spline registration algorithm to derive deformation matrices accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space [RS] method). The corrected images were compared with those produced by applying the deformation matrices in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion-averaged images.

RESULTS

Both correction techniques led to significant improvement in accounting for respiratory motion artifacts when compared with uncorrected motion-averaged images. These improvements included signal-to-noise ratio (mean increase of 28.0% and 24.2% for the RS and IS methods, respectively), lesion size (reduction of 60.4% and 47.9%, respectively), lesion contrast (increase of 70.1% and 57.2%, respectively), and lesion position (changes of 60.9% and 46.7%, respectively).

CONCLUSION

Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D PET RS approach.

Influence of partial volume correction in staging of head and neck squamous cell carcinoma using PET/CT

AIM

PET/CT is widely used for the detection of lymph node involvement in head and neck squamous cell carcinoma (HNSCC). However, PET qualitative and quantitative capabilities are hindered by partial volume effects (PVE). Therefore, a retrospective study on 32 patients (57 lymph nodes) was carried out to evaluate the potential improvement of PVE correction (PVEC) in FDG PET/CT imaging for the diagnosis of HNSCC. Histopathological analysis of lymph nodes following neck dissection was used as the gold standard.

METHODS

A previously proposed deconvolution based PVEC approach was used to derive improved quantitative accuracy PET images, while the anatomical lymph node volumes were determined on the CT images. Different parameters including SUVmax and SUVmean were derived from both original and PVEC PET images for each patient.

RESULTS

Histopathology confirmed that SUVmax and SUVmean after PVEC allows a statistically significant differentiation of malignant and benign lymph nodes (P<0.05). The sensitivity of SUVmax and SUVmean was 64% and 57% respectively with or without PVEC. PVEC increased specificity from 71% to 76% for SUVmax and 57% to 66% for SUVmean. Corresponding accuracy increased from 66% to 71% for SUVmax and from 59% to 66% for SUVmean. However, the most accurate differentiation between benign and malignant nodes was obtained while using the magnitude of SUVmax increase after PVEC with a corresponding sensitivity, specificity and accuracy of 77%, 82% and 80% respectively.

CONCLUSION

Our work shows that the use of partial volume effects correction allows a more accurate nodal staging using FDG PET imaging in HNSCC.

Influence of partial volume correction in staging of head and neck squamous cell carcinoma using PET/CT

Aim: PET/CT is widely used for the detection of lymph node involvement in head and neck squamous cell carcinoma (HNSCC). However, PET qualitative and quantitative capabilities are hindered by partial volume effects (PVE). Therefore, a retrospective study on 32 patients (57 lymph nodes) was carried out to evaluate the potential improvement of PVE correction (PVEC) in FDG PET/CT imaging for the diagnosis of HNSCC. Histopathological analysis of lymph nodes following neck dissection was used as the gold standard. Methods: A previously proposed deconvolution based PVEC approach was used to derive improved quantitative accuracy PET images, while the anatomical lymph node volumes were determined on the CT images. Different parameters including SUVmax and SUVmean were derived from both original and PVEC PET images for each patient. Results: Histopathology confirmed that SUVmax and SUVmean after PVEC allows a statistically significant differentiation of malignant and benign lymph nodes (p<0.05). The sensitivity of SUVmax and SUVmean was 64% and 57% respectively with or without PVEC. PVEC increased specificity from 71% to 76% for SUVmax and 57% to 66% for SUVmean. Corresponding accuracy increased from 66% to 71% for SUVmax and from 59% to 66% for SUVmean. However, the most accurate differentiation between benign and malignant nodes was obtained while using the magnitude of SUVmax increase after PVEC with a corresponding sensitivity, specificity and accuracy of 77%, 82% and 80% respectively. Conclusion: Our work shows that the use of partial volume effects correction allows a more accurate nodal staging using FDG PET imaging in HNSCC.

Comparison of different methods of incorporating respiratory motion for lung cancer tumor volume delineation on PET images: a simulation study

The interest of PET complementary information for the delineation of the target volume in radiotherapy of lung cancer is increasing. However, respiratory motion requires the determination of a functional internal target volume (ITV) on PET images for which several strategies have been proposed. The purpose of this study was the comparison of these strategies for taking into account respiratory motion and deriving the ITV: (1) adding fixed margins to the volume defined on a single binned image, (2) segmenting a motion averaged image and (3) considering the union of volumes delineated on binned frames. For this third strategy, binned frames were either non-corrected for motion, or corrected using two different methods: elastic registration or super resolution. The strategies' performances were assessed on realistic simulated datasets combining the NCAT phantom with a PET Philips GEMINI scanner model in GATE, and containing various configurations of tumor to background contrast, with both regular and irregular respiratory motion (with a range of motion amplitudes). The obtained ITVs' sensitivity (SE) and positive predictive value (PVE) with respect to the known true ITV were significantly higher (from 0.8 to 0.95) than all other techniques when using binned frames corrected for motion, independently of motion regularity, amplitude, or tumor to background contrast. Although the absolute difference was small and not always significant, images corrected using super resolution led to systematically better results than using elastic registration. The worst results were obtained when using the motion averaged image for SE (around 0.5-0.6) and using the margins added to a single frame for PPV (0.6-0.7), respectively. The best strategy to account for breathing motion for tumor ITV delineation in radiotherapy planning is to rely on the use of the union of volumes delineated on super resolution-corrected binned images.

Patient specific respiratory motion modeling using a 3D patient's external surface

PURPOSE

Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal.

METHODS

Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient.

RESULTS

Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only.

CONCLUSIONS

The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.

Technical note: Correlation of respiratory motion between external patient surface and internal anatomical landmarks

PURPOSE

Current respiratory motion monitoring devices used for motion synchronization in medical imaging and radiotherapy provide either 1D respiratory signal over a specific region or 3D information based on few external or internal markers. On the other hand, newer technology may offer the potential to monitor the entire patient external surface in real time. The main objective of this study was to assess the motion correlation between such an external patient surface and internal anatomical landmarks motion.

METHODS

Four dimensional computed tomography (4D CT) volumes for ten patients were used in this study. Anatomical landmarks were manually selected in the thoracic region across the 4D CT datasets by two experts. The landmarks included normal structures as well as the tumor location. In addition, a distance map representing the entire external patient surface, which corresponds to surfaces acquired by a time of flight (ToF) camera or similar devices, was created by segmenting the skin of all 4D CT volumes using a thresholding algorithm. Finally, the correlation between the internal landmarks and external surface motion was evaluated for different regions (placement and size) throughout a patient's surface.

RESULTS

Significant variability was observed in the motion of the different parts of the external patient surface. The larger motion magnitude was consistently measured in the central regions of the abdominal and the thoracic areas for the different patient datasets considered. The highest correlation coefficients were observed between the motion of these external surface areas and internal landmarks such as the diaphragm and mediastinum structures as well as the tumor location landmarks (0.8 +/- 0.18 and 0.72 +/- 0.12 for the abdominal and the thoracic regions, respectively). Worse correlation was observed when one considered landmarks not significantly influenced by respiratory motion such as the apex and the sternum.

CONCLUSIONS

There were large differences in the motion correlation observed considering different regions of interest placed over a patients' external surface and internal anatomical landmarks. The positioning of current devices used for respiratory motion synchronization may reduce such correlation by averaging the motion over correlated and poorly correlated external regions. The potential of capturing in real-time the motion of the complete external patient surface as well as choosing the area of the surface that correlates best with the internal motion should allow reducing such variability and associated errors in both respiratory motion synchronization and subsequent motion modeling processes.