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.

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.

[Metabolically active volumes automatic delineation methodologies in PET imaging: review and perspectives]. / Méthodologies de définition automatique des volumes métaboliquement actifs en TEP : évaluation et perspectives.

PET imaging is now considered a gold standard tool in clinical oncology, especially for diagnosis purposes. More recent applications such as therapy follow-up or tumor targeting in radiotherapy require a fast, accurate and robust metabolically active tumor volumes delineation on emission images, which cannot be obtained through manual contouring. This clinical need has sprung a large number of methodological developments regarding automatic methods to define tumor volumes on PET images. This paper reviews most of the methodologies that have been recently proposed and discusses their framework and methodological and/or clinical validation. Perspectives regarding the future work to be done are also suggested.

Incorporation of wavelet-based denoising in iterative deconvolution for partial volume correction in whole-body PET imaging.

PURPOSE: Partial volume effects (PVEs) are consequences of the limited resolution of emission tomography. The aim of the present study was to compare two new voxel-wise PVE correction algorithms based on deconvolution and wavelet-based denoising. MATERIALS AND METHODS: Deconvolution was performed using the Lucy-Richardson and the Van-Cittert algorithms. Both of these methods were tested using simulated and real FDG PET images. Wavelet-based denoising was incorporated into the process in order to eliminate the noise observed in classical deconvolution methods. RESULTS: Both deconvolution approaches led to significant intensity recovery, but the Van-Cittert algorithm provided images of inferior qualitative appearance. Furthermore, this method added massive levels of noise, even with the associated use of wavelet-denoising. On the other hand, the Lucy-Richardson algorithm combined with the same denoising process gave the best compromise between intensity recovery, noise attenuation and qualitative aspect of the images. CONCLUSION: The appropriate combination of deconvolution and wavelet-based denoising is an efficient method for reducing PVEs in emission tomography.

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.

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.

Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET.

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.

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 multiresolution image based approach for correction of partial volume effects in emission tomography.

Partial volume effects (PVEs) are consequences of the limited spatial resolution in emission tomography. They lead to a loss of signal in tissues of size similar to the point spread function and induce activity spillover between regions. Although PVE can be corrected for by using algorithms that provide the correct radioactivity concentration in a series of regions of interest (ROIs), so far little attention has been given to the possibility of creating improved images as a result of PVE correction. Potential advantages of PVE-corrected images include the ability to accurately delineate functional volumes as well as improving tumour-to-background ratio, resulting in an associated improvement in the analysis of response to therapy studies and diagnostic examinations, respectively. The objective of our study was therefore to develop a methodology for PVE correction not only to enable the accurate recuperation of activity concentrations, but also to generate PVE-corrected images. In the multiresolution analysis that we define here, details of a high-resolution image H (MRI or CT) are extracted, transformed and integrated in a low-resolution image L (PET or SPECT). A discrete wavelet transform of both H and L images is performed by using the "à trous" algorithm, which allows the spatial frequencies (details, edges, textures) to be obtained easily at a level of resolution common to H and L. A model is then inferred to build the lacking details of L from the high-frequency details in H. The process was successfully tested on synthetic and simulated data, proving the ability to obtain accurately corrected images. Quantitative PVE correction was found to be comparable with a method considered as a reference but limited to ROI analyses. Visual improvement and quantitative correction were also obtained in two examples of clinical images, the first using a combined PET/CT scanner with a lymphoma patient and the second using a FDG brain PET and corresponding T1-weighted MRI in an epileptic patient.

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 system's 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 system's 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.

Technology related parameters affecting quantification in positron emission tomography imaging.

Some of the issues associated with positron emission tomography (PET) technology which still pose challenges for the recovery of quantitative images are discussed. Through these issues reference to what is today considered as the 'gold standard' in quantitative PET imaging is also presented. A brief comparison of 2-D and 3-D PET is given, together with a short discussion of combined PET/CT imaging devices.

Functional imaging of malignant paragangliomas and carcinoid tumours.

Complete staging is mandatory for the management and therapy of neuroendocrine tumours. Various radiotracers are available but the best imaging strategy has yet to be defined. In this study we retrospectively compared 123I-MIBG, 111In-[D-Phe1]-DTPA-octreotide and 18F-FDG (PET) imaging in 15 patients with metastatic neuroendocrine tumours (11 carcinoid tumours, 4 paragangliomas). Planar images were acquired 1, 4, 24 and 48 h following the injection of 111In-[D-Phe1]-DTPA-octreotide and 123I-MIBG. Whole-body PET scans were performed 45 min after injection of 18F-FDG. 111In-[D-Phe1]-DTPA-octreotide was positive in 11/15 patients and identified 44 lesions, 18F-FDG PET was positive in 11/15 patients and identified 107 lesions and 123I-MIBG was positive in 8/15 patients and identified 67 lesions. No single scintigraphic technique identified all metastatic sites. In one patient all studies were negative. 18F-FDG PET identified more abnormal sites than the other two modalities. Combination of all three imaging modalities with X-ray CT helps to provide a more comprehensive map of the disease.

Post-synchronized scintigraphic data to estimate antral motility.

We evaluated an improved dynamic antral scintigraphy (DAS) technique, without any frequency filtering or computation of an autocorrelation function. This DAS was performed in 15 consecutive patients and 10 healthy volunteers. Antral frequency was first estimated and was given as an input parameter to compute phase and amplitude values in each antral pixel. Motility indices were calculated by multiplying the frequency by a normalized amplitude in the whole antrum. In addition, a gastric emptying (GE) test was performed. Only 10 patients had a delayed GE when using a cut-off value of the mean of half emptying time (T +2SD) obtained in controls. Antral frequencies were significantly increased but motility indices were significantly lower in patients than in controls. These results in patients were accounted for by a retention of food in the antrum. Therefore, amplitude normalization by the antral mean count activity of each set of data was essential for discriminating between patients and controls and normalized indices appeared early predictors of hypomotility in patients with normal GE. This improved DAS technique should be a useful tool to assess antral dysmotility noninvasively, and may be of physiological and clinical interest.

Three-dimensional attenuation map reconstruction using geometrical models and free-form deformations.

We address the issue of using deformable models to reconstruct an unknown attenuation map of the torso from a set of transmission scans. We assume the three-dimensional (3-D) distribution of attenuation coefficients to be piecewise uniform. We represent the unknown distribution by a set of closed surfaces defining regions having the same attenuating properties. The methods of reconstruction published so far tend to directly deform the surfaces, the parameters being the surface elements. Rather than deforming the surfaces, we explore the possibility of deforming the space in which the geometrical primitives are contained. We focus on the use of free-form deformations (FFD's) to describe the continuous transformation of space used to match a set of transmission measurements. We illustrate this approach by reconstructing realistically simulated transmission scans of the torso with various noise levels and compare the results to standard reconstruction methods.

Gastric emptying of solids: estimates of lag phase and constant emptying times.

There is no consensus regarding the best way to estimate the lag phase time (Tlag) and the constant emptying time (TRE) of the gastric emptying of solids. Furthermore, biphasic gastric emptying is usually described by the modified power exponential function of either Elashoff or Siegel. In an attempt to test the validity of the power exponential functions and to identify relevant parameters of biphasic gastric emptying, we followed an approach which consists of describing the power exponential function by two straight lines. The first line is horizontal and represents Tlag. The second line is tangential to the constant emptying [tangent at the maximum slope (MS) or at the half-emptying value]. Scintigraphic data of 132 patients and 15 controls were fitted by both power exponential functions. Each corresponding half-emptying time, Tlag and TRE estimated from the Elashof and Siegel power exponential functions were strongly correlated (0.93 < r < 1, P < 0.0001). The Bland and Altman statistical method demonstrated good agreement (<5% outliers). The half-emptying tangent method sometimes gave negative Tlag and should be abandoned. Tlag(MS) and TRE(MS) did not correlate and therefore were independent parameters. We conclude that the Elashoff and Siegel functions are equivalent and that the maximum slope tangent method allows a reliable description of the two independent phases of gastric emptying.

Post-synchronization of dynamic images of periodically moving organs.

The motion of periodically moving organs can be studied by acquisition of dynamic image series. When time is short, it is necessary either to find a sync signal to sum synchronous images in real time or to acquire a regular time series and to synchronize a posteriori. Dynamic acquisitions were performed (gastric and lung studies). The activity in each pixel of the moving organ can be expressed as h(t)=a0 + a1 cos(omegao0t - phi). The time-activity curve u(t) over a region of interest (ROI) of the considered organ was computed. When the ROI is well chosen, the power spectrum of u(t) exhibits a sharp peak near the characteristic frequency of the periodic motion. The DC component, amplitude and phase in each pixel can be then estimated by minimizing the following function: J=sigma[h(t) - g(t)]2, where g(t) is a noisy measurement of h(t). It is then easy to reconstruct an a posteriori gated time series by computing h(t) for various times over a single period. This approach was successful in characterizing lung and gastric motions. Dynamic series were acquired as for gastric emptying studies. The characteristic frequency of antral motility was easily and unambiguously estimated and DC, amplitude and phase images were computed. Dynamic pulmonary functional imaging was performed with 81Krm. The characteristic frequency was also easily estimated from the time-activity curve power spectrum using a ROI drawn over the lower part of the lungs. The DC, amplitude and phase images were then computed from the dynamic series and the characteristic frequency. In conclusion, a posteriori gating of dynamic series of periodically moving organs can be achieved in a simple fashion. This approach overcomes the difficulty of direct analysis of time-activity curves and provides amplitude and phase images.

Use of left ventricular pacing in heart failure: evaluation by gated blood pool imaging.

BACKGROUND: Left ventricular (LV) pacing has been suggested to complement other forms of therapy in patients with heart failure. METHODS AND RESULTS: We investigated 17 patients (15 men, 2 women, aged 68 +/- 6 years, 10 ischemic and 7 primary dilated cardiomyopathy) with heart failure (13 were in New York Heart Association class IV and 4 in class III). One month after LV pacer implantation, 12 patients reported clinical improvement (mean class 3.7 before pacing vs 2.6 with LV pacing; P = .001). We report the results of 3 equilibrium-gated blood pool studies performed in each patient, 1 before pacing and 2 after pacer implantation (1 with pacing on, and 1 after turning off the pacer). LV pacing did not modify LV ejection fraction. Phase analysis demonstrated a significant decrease of the interventricular phase shift (delta(pi)) with LV pacing (no pacing, delta(pi) = 8.99 degrees +/- 19.05 degrees; delta7n= -0.97 degrees +/- 27.85 degrees with LV pacing). Clinical improvement was observed in patients with an initial positive delta(pi) that decreased with pacing and/or an initial LV phase standard deviation >50 degrees that decreased with pacing. CONCLUSION: LV pacing induces interventricular and intraventricular synchronization. A decrease of the interventricular phase shift seems to be the most important predictor of functional recovery for paced patients with heart failure.