C. Cheze Le Rest

List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations

Respiratory motion in emission tomography leads to reduced image quality. Developed correction methodology has been concentrating on the use of respiratory synchronized acquisitions leading to gated frames. Such frames, however, are of low signal-to-noise ratio as a result of containing reduced statistics. In this work, we describe the implementation of an elastic transformation within a list-mode-based reconstruction for the correction of respiratory motion over the thorax, allowing the use of all data available throughout a respiratory motion average acquisition. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List-mode-data-based PET-simulated frames were subsequently produced by combining the NCAT datasets with Monte Carlo simulation. A non-rigid registration algorithm based on B-spline basis functions was employed to derive transformation parameters accounting for the respiratory motion using the NCAT dynamic CT images. The displacement matrices derived were subsequently applied during the image reconstruction of the original emission list mode data. Two different implementations for the incorporation of the elastic transformations within the one-pass list mode EM (OPL-EM) algorithm were developed and evaluated. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lung fields, the elastic-transformation-based correction leads to a more uniform improvement across the lungs for different lesion sizes and locations.

Validation of a Monte Carlo simulation of the Philips Allegro/GEMINI PET systems using GATE.

A newly developed simulation toolkit, GATE (Geant4 Application for Tomographic Emission), was used to develop a Monte Carlo simulation of a fully three-dimensional (3D) clinical PET scanner. The Philips Allegro/GEMINI PET systems were simulated in order to (a) allow a detailed study of the parameters affecting the systems performance under various imaging conditions, (b) study the optimization and quantitative accuracy of emission acquisition protocols for dynamic and static imaging, and (c) further validate the potential of GATE for the simulation of clinical PET systems. A model of the detection system and its geometry was developed. The accuracy of the developed detection model was tested through the comparison of simulated and measured results obtained with the Allegro/GEMINI systems for a number of NEMA NU2-2001 performance protocols including spatial resolution, sensitivity and scatter fraction. In addition, an approximate model of the systems dead time at the level of detected single events and coincidences was developed in an attempt to simulate the count rate related performance characteristics of the scanner. The developed dead-time model was assessed under different imaging conditions using the count rate loss and noise equivalent count rates performance protocols of standard and modified NEMA NU2-2001 (whole body imaging conditions) and NEMA NU2-1994 (brain imaging conditions) comparing simulated with experimental measurements obtained with the Allegro/GEMINI PET systems. Finally, a reconstructed image quality protocol was used to assess the overall performance of the developed model. An agreement of <3% was obtained in scatter fraction, with a difference between 4% and 10% in the true and random coincidence count rates respectively, throughout a range of activity concentrations and under various imaging conditions, resulting in <8% differences between simulated and measured noise equivalent count rates performance. Finally, the image quality validation study revealed a good agreement in signal-to-noise ratio and contrast recovery coefficients for a number of different volume spheres and two different (clinical level based) tumour-to-background ratios. In conclusion, these results support the accurate modelling of the Philips Allegro/GEMINI PET systems using GATE in combination with a dead-time model for the signal flow description, which leads to an agreement of <10% in coincidence count rates under different imaging conditions and clinically relevant activity concentration levels.

Respiratory motion correction for PET oncology applications using affine transformation of list mode data

Respiratory motion is a source of artefacts and reduced image quality in PET. Proposed methodology for correction of respiratory effects involves the use of gated frames, which are however of low signal-to-noise ratio. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. We have implemented an affine transformation of list mode data for the correction of respiratory motion over the thorax. The study was performed using datasets of the NCAT phantom at different points throughout the respiratory cycle. List mode data based PET simulated frames were produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an affine registration and were subsequently applied on the original list mode data. The corrected and uncorrected list mode datasets were subsequently reconstructed using the one-pass list mode EM (OPL-EM) algorithm. Comparison of corrected and uncorrected respiratory motion average frames suggests that an affine transformation in the list mode data prior to reconstruction can produce significant improvements in accounting for respiratory motion artefacts in the lungs and heart. However, the application of a common set of transformation parameters across the imaging field of view does not significantly correct the respiratory effects on organs such as the stomach, liver or spleen.

A posteriori respiratory motion gating of dynamic PET images

The presence of patient physiological motion during imaging may cause significant artifacts in image quality. Proposed correction methodologies involve the use of gated acquisitions through simultaneous recording of an external signal. The purpose of our work is to determine the feasibility of post-acquisition synchronization of dynamically acquired PET images in the absence of any external signal. The principle of the technique is based on the assumption that although the amplitude of the motion may vary from pixel to pixel inside the same organ, the frequency of the periodic motion is the same. Under such conditions, the prerequisite for a posteriori gating is the ability to accurately estimate that frequency. We performed simulation studies using the NCAT phantom and a Monte Carlo simulation of the GE Advance PET system (3D mode of operation). A number of NCAT emission and the corresponding transmission frames were generated throughout a respiratory cycle. Time frames of 0.15, 0.45 and 0.62 seconds were simulated with variable count statistics (namely 30k, 70k and 120k of total simulated coincidences). Time activity curves were obtained, for each of the dynamic series formed, using different ROIs and Fourier transform was performed in order to estimate the frequency of the simulated motion. We were able to determine the frequency of motion (within 2% of the simulated frequency) for all three frame time durations evaluated. Using the estimated frequency we were able to calculate on a pixel by pixel basis the amplitude and the phase of the motion, allowing us to reconstruct an a posteriori gated time series.

Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation.

Denoising of Positron Emission Tomography (PET) images is a challenging task due to the inherent low signal-to-noise ratio (SNR) of the acquired data. A pre-processing denoising step may facilitate and improve the results of further steps such as segmentation, quantification or textural features characterization. Different recent denoising techniques have been introduced and most state-of-the-art methods are based on filtering in the wavelet domain. However, the wavelet transform suffers from some limitations due to its non-optimal processing of edge discontinuities. More recently, a new multi scale geometric approach has been proposed, namely the curvelet transform. It extends the wavelet transform to account for directional properties in the image. In order to address the issue of resolution loss associated with standard denoising, we considered a strategy combining the complementary wavelet and curvelet transforms. We compared different figures of merit (e.g. SNR increase, noise decrease in homogeneous regions, resolution loss, and intensity bias) on simulated and clinical datasets with the proposed combined approach and the wavelet-only and curvelet-only filtering techniques. The three methods led to an increase of the SNR. Regarding the quantitative accuracy however, the wavelet and curvelet only denoising approaches led to larger biases in the intensity and the contrast than the proposed combined algorithm. This approach could become an alternative solution to filters currently used after image reconstruction in clinical systems such as the Gaussian filter.

A patient specific respiratory model based on 4D CT data and a time of flight camera (TOF)

Respiratory motion is an important factor leading to errors and uncertainties in radiation therapy (RT). Solutions presented to date include modeling tumor and surrounding tissues motion. Having such a model is a key point to deliver, under breathing induced motion, less dose to the normal healthy tissues and higher dose to the tumor. Many continuous motion models have been developed based on 4D CT data. All these models are reconstructed using, for instance, an external respiratory signal (RPM or respiratory belt) or the diaphragm position. Possible limitations of these models are cases where the correlation between the respiratory motion and the corresponding surrogate is less reliable. In this paper, we describe an approach based on the creation of a continuous patient specific model that takes into account respiratory signal irregularities and uses, as surrogate, a surface map acquired using a time of flight camera. This model has been validated on three patients. Our results show that using the time of flight camera surface maps for the model reconstruction and application leads to higher accuracy compared to the use of a 1D respiratory signal.

Incorporation of elastic transformations in list-mode based reconstruction for respiratory motion correction in PET

Respiratory motion in emission tomography leads to reduced image quality. Proposed correction methodology has been concentrating on the use of respiratory synchronised acquisitions leading to gated frames. Such frames however are of low signal to noise ratio as a result of containing reduced statistics. Therefore a method accounting for respiratory motion effects without affecting the statistical quality of the reconstructed images is necessary. In this work we describe the implementation of an elastic transformation within a list-mode based reconstruction for the correction of respiratory motion over the thorax. The developed algorithm was evaluated using datasets of the NCAT phantom generated at different points throughout the respiratory cycle. List mode data based PET simulated frames were subsequently produced by combining the NCAT datasets with a Monte Carlo simulation. Transformation parameters accounting for respiratory motion were estimated according to an elastic registration of the NCAT dynamic CT images and were subsequently applied during the image reconstruction of the original emission list mode data. The One-pass list mode EM (OPL-EM) algorithm was modified to integrate the elastic transformation. The corrected images were compared with those produced using an affine transformation of list mode data prior to reconstruction, as well as with uncorrected respiratory motion average images. Results demonstrate that although both correction techniques considered lead to significant improvements in accounting for respiratory motion artefacts in the lungs and heart, the elastic transformation based correction leads to a more uniform improvement across the lung field for different lesion sizes.

Towards a generic respiratory motion model for 4D CT imaging of the thorax

One of the most important parameters reducing the sensitivity and specificity in the thoracic and abdominal areas is respiratory motion and associated deformations which represent today an important challenge in medical imaging. The negative impact of respiratory motion is not only evident in the field of cancer diagnostic imaging but also in therapy applications, particularly in the field of radiation therapy. Modelling of the respiratory motion is therefore very important for the efficacy of both diagnostic and therapeutic imaging applications. All respiratory motion models presented to date are patient specific and require a 4D CT acquisition for every patient implying an associated increased dose which cannot be easily justified for all patients. The objective of this study is the description of a framework for the creation of a global generic model from 4D CT images of different patients. This global model is based on principal component analysis (PCA) and can be adapted to a given patient anatomy needing only one static CT image in combination with respiratory synchronised images of the patient surface. In this work 4D CT images of three patients were used to create the model which was tested by subsequently adapting it on the static CT images of two different patients. Our comparative results between the model derived and the corresponding acquired 4D CT images demonstrate the efficiency of the developed generic motion model.

A method for synchronizing an external respiratory signal with a list‐mode PET acquisition

A method is proposed to synchronize positron emission tomography (PET) list-mode data with an externally recorded respiratory signal in the absence of a master clock. When the respiratory signal reaches a user-defined threshold, a trigger mark is stored in the list-mode file. After the acquisition, synchronization is achieved when the stored trigger marks are superimposed on the respiratory curve to form a horizontal line over time at the user-defined threshold. Synchronization was possible and unequivocal for ten out of ten clinical studies. The list-mode acquisition actually started approximately 40 and 4 s after acquisition initiation at the user interface of the Philips Gemini and the GE DLS PET-CT systems, respectively.

Rôle de la TEP/TDM au 18FDG dans la prise en charge des suspicions de récidive de cancer différencié de la thyroïde

Introduction L’objectif principal de notre etude etait d’evaluer l’impact de la TEP/TDM au 18FDG dans la prise en charge des patients suspects de recidive de cancer differencie de la thyroide (CDT). Les objectifs secondaires etaient d’evaluer les performances diagnostiques de l’examen et de determiner un seuil de Tg minimum a partir duquel les performances de l’examen seront optimales. Methode Cette etude retrospective, sur 60xa0patients ayant beneficie au total de 81xa0examens TEP/TDM au FDG pour suspicion de recidive de CDT a ete realisee au CHU de Poitiers (juillet 2010xa0–xa0decembre 2013). Ont ete inclus les examens realises chez des patients presentant une elevation des marqueurs (thyroglobuline [Tg] ou anticorps anti-thyroglobuline [AAT]) et/ou des anomalies detectees a l’echographie cervicale, dans un contexte de scintigraphie a l’iode 131xa0negative. Les resultats de la TEP ont ete confrontes aux donnees histologiques ou a defaut aux donnees de suivi clinique et paraclinique des patients (autres modalites d’imagerie et evolution des taux seriques de Tg/AAT). Resultats Vingt-six examens etaient positifs dont 20xa0vrais positifs (VPxa0: 13xa0cas de recidive locoregionale et 7xa0cas de recidive a distance) et 6xa0faux-positifs (FPxa0: 4xa0cas a l’etage cervical et 2xa0cas a l’etage thoracique). Huit examens ont ete classes comme faux negatifs. Les sensibilite, specificite, valeurs predictives positive et negative de la TEP/TDM etaient, respectivement, de 71,4xa0%, 88,7xa0%, 76,9xa0% et 85,5xa0%. La valeur moyenne du SUVmax des foyers hypermetaboliques des VP etait significativement plus elevee comparativement aux FP (pxa0=xa00,02). Il a ete observe que plus le taux de Tg etait eleve, plus la proportion d’examens positifs augmentait (pxa0=xa00,0003). Des valeurs seuil de Tg non stimulee de 0,5xa0ng/mL et de Tg stimulee de 2,7xa0ng/mL (disponibles pour 56xa0examens) ont ete retrouvees comme optimales a l’aide de courbes ROC. Un changement de l’attitude therapeutique a ete induit par la TEP dans 15xa0% des cas. Conclusion Cette etude confirme les bonnes performances de la TEP/TDM au 18FDG dans la detection et la localisation precise de recidives locoregionales et des metastases a distance de cancer differencie de la thyroide chez des patients presentant une elevation du taux de Tg avec une scintigraphie a l’iode 131xa0negative. Elle a un impact modere mais significatif sur la prise en charge des patients et peut etre recommandee en routine clinique.