Non-Local Means Denoising of Dynamic PET Images

Objective

Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM).

Theory

NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch.

Methods

To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches – Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches.

Results

The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.     

Bayesian penalized-likelihood reconstruction algorithm suppresses edge artifacts in PET reconstruction based on point-spread-function

PurposeThe Bayesian penalized-likelihood reconstruction algorithm (BPL), Q.Clear, uses relative difference penalty as a regularization function to control image noise and the degree of edge-preservation in PET images. The present study aimed to determine the effects of suppression on edge artifacts due to point-spread-function (PSF) correction using a Q.Clear.MethodsSpheres of a cylindrical phantom contained a background of 5.3 kBq/mL of [¹⁸F]FDG and sphere-to-background ratios (SBR) of 16, 8, 4 and 2. The background also contained water and spheres containing 21.2 kBq/mL of [¹⁸F]FDG as non-background. All data were acquired using a Discovery PET/CT 710 and were reconstructed using three-dimensional ordered-subset expectation maximization with time-of-flight (TOF) and PSF correction (3D-OSEM), and Q.Clear with TOF (BPL). We investigated β-values of 200–800 using BPL. The PET images were analyzed using visual assessment and profile curves, edge variability and contrast recovery coefficients were measured.ResultsThe 38- and 27-mm spheres were surrounded by higher radioactivity concentration when reconstructed with 3D-OSEM as opposed to BPL, which suppressed edge artifacts. Images of 10-mm spheres had sharper overshoot at high SBR and non-background when reconstructed with BPL. Although contrast recovery coefficients of 10-mm spheres in BPL decreased as a function of increasing β, higher penalty parameter decreased the overshoot.ConclusionsBPL is a feasible method for the suppression of edge artifacts of PSF correction, although this depends on SBR and sphere size. Overshoot associated with BPL caused overestimation in small spheres at high SBR. Higher penalty parameter in BPL can suppress overshoot more effectively.     

PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods

PurposeThis work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques.MethodsPETSTEP was implemented within Matlab as open source software. It allows generating three-dimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images.ResultsPETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods.ConclusionsPETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.     

Semiautomatic algorithm for lymph node analysis corrected for partial volume effects in combined positron emission tomography-computed tomography

Positron emission tomography-computed tomography (PET-CT) is superior compared to stand-alone PET in evaluation of malignancies. Few studies have employed high-resolution structural information to correct PET. We designed a semiautomatic algorithm using CT and PET to obtain a partial volume corrected (PVC) standardized uptake value (SUV) and a combined morphologic and functional parameter (multimodal SUV) for lymph node assessment. Lesions were segmented by a semiautomatic algorithm in CT images. Lesion volume was used for PVC and for calculating the multimodal SUV. The method was applied to 47 lymph nodes (30 patients) characterized as suspicious in 18F-fluorodeoxyglucose-PET-CT. In phantoms, PVC improved significantly the measured uptake of the lesion. In patients, 36 lymph nodes could be segmented without problems; in 11 lesions, a manual interaction was necessary. SUVs before PVC (mean 1.29) increased significantly (p < .0005) after PVC (mean 2.8). If SUV 2.5 was used as a threshold value to distinguish between benign and malignant lesions, 11 of the 47 lesions changed from benign to malignant after the PVC. The mean multimodal SUV was 0.39 mL for the benign lesions and 4.47 mL for the malignant lesions. In this work we presented a method for quantitative analysis of lymph nodes in PET-CT. PVC leads to significant differences in SUV.     

Evaluation of Non-Local Means Based Denoising Filters for Diffusion Kurtosis Imaging Using a New Phantom

Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW) image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM). Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE), Bias and standard deviation (Std). The result of the comparison based on the phantom was consistent with those based on real datasets.     

Évaluation de l’exactitude de latéralisation du foyer épileptogène par la tomographie par émission de positons au 18f-fluorodésoxyglucose dans le bilan préopératoire de l’épilepsie temporale : analyse visuelle simple et par index d’asymétrie inter-hémisphérique par soustraction d’images,analyse quantitative par statistical parametric mapping

IntroductionInter-ictal 18F-2-fluoro-deoxy-D-glucose-positron emission tomography (FDG-PET) plays a key role for the preoperative evaluation of patients with pharmacoresistant temporal lobe epilepsy. PET images are usually analyzed visually, a way that is reported to provide a high diagnostic value but that remains subjective, depending on the expertise and experience of the observer. By contrast, the voxel-based quantitative analyses, such as statistical parametric mapping (SPM), are objective and therefore, observer independent methods of analyses. In this study, the accuracy of the analyses of brain FDG-PET images to lateralize the temporal lobe epileptogenic zone was compared between: (1) a conventional visual method, (2) a quantitative SPM analysis, and (3) a visual analysis of inter-hemispheric asymmetry (IHA) obtained after images substraction.Materials and methodsFDG-PET scans of 31 patients presenting a severe temporal epilepsy and whom the temporal foci had been accurately lateralized (successful subsequent surgical treatment) were retrospectively analysed by (1) a consensual visual analysis from two experienced observers; (2) SPM analysis with voxel-wise comparisons of FDG-PET images of patients with those of age-matched healthy controls, using various statistical threshold (P) and cluster (k) values; and (3) visual assessment by the two same observers of images obtained for assessing the IHA. For this purpose, a flipped image was initially obtained by reversing in the left-right direction the FDG-PET images, which had been previously spacially normalized with the SPM template. Then, flipped and non-flipped images were substracted.ResultsThe temporal hypometabolic area was accurately identified: (1) by the conventional visual analysis in 87 % of patients and with a satisfactory interobserver reproducibility (interobserver Cohen's coefficient = 0.79); (2) by SPM analysis, in 90 % of patients (when using optimal thresholds of 0.01 for P value and of 50 voxels (400 mm³) for k value); and (3) with the visual analysis of IHA in 97 % of patients with an excellent interobserver reproductibility (interobserver Cohen's coefficient = 1).ConclusionIn patients presenting severe temporal epilepsy, visual assessment of FDG-PET images from IHA seems more accurate for lateralizing the epileptogenic temporal areas when compared with either conventional visual or quantitative SPM analyses. Moreover, this method is very easy to use in clinical practice, contrary to the quantitative method using SPM     

Utility of fast non-local means (FNLM) filter for detection of pulmonary nodules in chest CT for pediatric patient

PurposeThis study was aimed to evaluate the utility based on imaging quality of the fast non-local means (FNLM) filter in diagnosing lung nodules in pediatric chest computed tomography (CT).MethodsWe retrospectively reviewed the chest CT reconstructed with both filtered back projection (FBP) and iterative reconstruction (IR) in pediatric patients with metastatic lung nodules. After applying FNLM filter with six h values (0.0001, 0.001, 0.01, 0.1, 1, and 10) to the FBP images, eight sets of images including FBP, IR, and FNLM were analyzed. The image quality of the lung nodules was evaluated objectively for coefficient of variation (COV), contrast to noise ratio (CNR), and point spread function (PSF), and subjectively for noise, sharpness, artifacts, and diagnostic acceptability.ResultsThe COV was lowest in IR images and decreased according to increasing h values and highest with FBP images (P < 0.001). The CNR was highest with IR images, increased according to increasing h values and lowest with FBP images (P < 0.001). The PSF was lower only in FNLM filter with h value of 0.0001 or 0.001 than in IR images (P < 0.001). In subjective analysis, only images of FNLM filter with h value of 0.0001 or 0.001 rarely showed unacceptable quality and had comparable results with IR images. There were less artifacts in FNLM images with h value of 0.0001 compared with IR images (p < 0.001).ConclusionFNLM filter with h values of 0.0001 allows comparable image quality with less artifacts compared with IR in diagnosing metastatic lung nodules in pediatric chest CT.     

Non-rigid registration of serial dedicated breast CT,longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method

Rationale and objectivesDedicated breast CT and PET/CT scanners provide detailed 3D anatomical and functional imaging data sets and are currently being investigated for applications in breast cancer management such as diagnosis, monitoring response to therapy and radiation therapy planning. Our objective was to evaluate the performance of the diffeomorphic demons (DD) non-rigid image registration method to spatially align 3D serial (pre- and post-contrast) dedicated breast computed tomography (CT), and longitudinally-acquired dedicated 3D breast CT and positron emission tomography (PET)/CT images.MethodsThe algorithmic parameters of the DD method were optimized for the alignment of dedicated breast CT images using training data and fixed. The performance of the method for image alignment was quantitatively evaluated using three separate data sets; (1) serial breast CT pre- and post-contrast images of 20 women, (2) breast CT images of 20 women acquired before and after repositioning the subject on the scanner, and (3) dedicated breast PET/CT images of 7 women undergoing neo-adjuvant chemotherapy acquired pre-treatment and after 1 cycle of therapy.ResultsThe DD registration method outperformed no registration (p < 0.001) and conventional affine registration (p ≤ 0.002) for serial and longitudinal breast CT and PET/CT image alignment. In spite of the large size of the imaging data, the computational cost of the DD method was found to be reasonable (3–5 min).ConclusionsCo-registration of dedicated breast CT and PET/CT images can be performed rapidly and reliably using the DD method. This is the first study evaluating the DD registration method for the alignment of dedicated breast CT and PET/CT images.     

Accuracy of target delineation by positron emission tomography-based auto-segmentation methods after deformable image registration: A phantom study

PurposeDiagnostic positron emission tomography and computed tomography (PET/CT) images can be fused to the planning CT images by a deformable image registration (DIR). The aim of this study was to evaluate the standardized uptake value (SUV) and target delineation on deformed PET images.MethodsWe used a cylindrical phantom and removable inserts of four spheres (16–38 mm in diameter) and three ellipsoids with a volume equal to the 38-mm-diameter sphere (S38) in each. S38 was filled with 18F-fluorodeoxyglucose activity, and then PET/CT images were acquired. The contours of S38 were generated using original PET images by PET auto-segmentation (PET-AS) methods of (1) SUV2.5, (2) 40% of maximum SUV (SUV40%max), and (3) gradient-based (GB), and were deformed to the other inserts by DIR. We compared the volumes and the SUV_max with the generated contours using the deformed PET images.ResultsThe SUV_max was slightly decreased by DIR; the mean absolute difference was −0.10 ± 0.04. For SUV2.5 and SUV40%max, the differences in S38 volumes between the original and deformed PET images were less than 5%, regardless of deformation type. For the GB, the contoured volumes obtained from deformed PET images were larger than those of the original PET images for the deformation type of ellipsoids. When the S38 was deformed to the 16-mm-diameter sphere, the maximum volume difference was −22.8%.ConclusionsAlthough SUV fluctuations by DIR were negligible, the target delineation on deformed PET images by the GB should be carefully considered owing to the distortion of intensity profiles.     

Quantization accuracy of short-duration respiratory-gated PET/CT acquisitions

PurposePET/CT acquisitions are affected by physiological motion, which lowers the quantization accuracy. Respiratory-gated PET/CT methods require a long acquisition time, which may not be compatible with the clinical schedule. The objective of the present study was to assess the quantization accuracy of short-duration, respiratory-gated PET acquisitions and processing with the “CT-based” methodology developed in our laboratory.MethodsQuantization accuracy was first assessed in a phantom study. A standard (“Ungated”) PET/CT acquisition was followed by a 10-minute list-mode acquisition with simultaneous respiratory signal recording and a short breath-hold CT scan (BH-CT). These acquisitions were repeated 10 times. For the CT-based images, we reconstructed (i) 10 full-duration (FD-CT-based) volumes that took account of all events recorded in the position defined by BH-CT and (ii) 10 short-duration (SD-CT-based) volumes based on only 30 seconds of selected events. Using these volumes, we performed a bias–variance analysis to assess the effects of respiration-motion reduction and the counting statistics on the quantization accuracy. We also applied Ungated, FD- and SD-CT-based methods to 16 patients (21 pulmonary lesions) and measured the maximum standardized uptake (SUVmax) values.ResultsThe bias values were 71%, 40% and 44% for Ungated, FD- and SD-CT-based images, respectively. In the clinical study, there was a statistically significant difference in SUVmax between Ungated images and both the CT-Based images (p < 0.02) but not between the FD-CT-Based and SD-CT-Based images (p = 0.42).ConclusionOur findings demonstrated that the additional acquisition time required by the CT-based method can be reduced without altering quantitative accuracy.     

Centiloid scale analysis for 18F-THK5351 PET imaging in Alzheimer’s disease

PurposeA standardized method for quantification is required for analyzing PET data, but such standards have not been established for tau PET imaging. The Centiloid scale has recently been proposed as a standard method for quantifying amyloid deposition on PET imaging. Therefore, the present study aimed to apply the Centiloid scale to ¹⁸F-THK5351 PET imaging in Alzheimer’s disease (AD).MethodsWe acquired ¹⁸F-THK5351 PET, ¹¹C-PiB PET, and MR images from 47 cognitively normal (CN) individuals and 28 patients with AD with mild to moderate dementia. PET images were spatially normalized to Montreal Neurological Institute space. The PET signals were then normalized using the signal in the reference volume of interest (VOI). Target VOI for specific ¹⁸F-THK5351 retention in AD was extracted by voxel-wise comparison of PET images between the 47 CN individuals and 16 AD patients with moderate dementia. Scale anchor points were defined by the CN individuals as 0-anchor points and by that of the average of the typical AD patients as 100-anchor points.ResultsSpecific retention of ¹⁸F-THK5351 was predominant in the angular gyrus, inferior temporal cortex, and parieto-occipital regions in patients with AD. Standardized uptake value ratio (SUVR) of 1.227 and 1.797 were defined as 0- and 100-anchor points, respectively. ¹⁸F-THK5351 PET data could be expressed using the Centiloid scale, with the SUVR of the ¹⁸F-THK5351 PET images converted to Centiloid using our VOI, the standard Centiloid reference VOI, and the following equation: Centiloid = 169.0 × SUVR–204.6.ConclusionCentiloid methods can be applied to tau PET imaging using ¹⁸F-THK5351.     

Faisabilité et intérêt d’un nouvel algorithme de reconstruction des images TEP-18FDG (AW OSEM DR) pour la délimitation des volumes cibles en radiothérapie. À propos d’un cas de néoplasie ORL

ObjectiveWe compared two reconstruction methods for 18fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) images with “attenuation weighted ordered subset expectation maximization” using either the manufacturer-provided (AW-OSEM) or a “Detector response” (AW-OSEM DR) tomographic operator. We looked at the feasibility of using the latter reconstruction for radiotherapy target volumes definition in cancers of the superior aero-digestive tract (VADS). In this preliminary study, we first assessed the spatial resolution of images obtained with AW-OSEM and AW-OSEM DR on a Biograph™ 6, and secondly target volumes of radiotherapy “Gross Tumor Volume” (GTV), “Clinical Target Volume” (CTV) and “Planning Target Volume” (PTV) obtained with each of these reconstruction methods.Material and methodsThe spatial resolution was measured on a test object containing 4 radioactive point sources. Furthermore, radiotherapy target volumes have been defined with the software Eclipse™ on injected scanner (CT IV) and PET/CT (PET AW-OSEM and PET AW-OSEM DR) images.ResultsSpatial resolution was improved with AW-OSEM DR algorithm reconstruction compared to images obtained with AW-OSEM reconstruction (from 7.5 mm down to 5.4 mm for the highest reduction). GTV from AW-OSEM DR reconstruction with 42 and 50% of the “Standard uptake value maximum” (SUVmax) semi-automatic threshold (1.2 and 0.7 cm³ respectively) were lower than those obtained with AW-OSEM (3.6 and 2.2 cm³ respectively). They were also lower than GTV defined with CT IV (5.5 cm³). It was the same for CTV and PTV.ConclusionThis study showed that AW-OSEM DR reconstruction method allows less impaired spatial resolution than AW-OSEM. In the case of radiotherapy target volumes delineation, AW-OSEM DR may decrease the GTV, CTV and PTV and therefore the risk of side effects associated with organs at risk.     

Few-view CT reconstruction via a novel non-local means algorithm

PurposeNon-local means (NLM) based reconstruction method is a promising algorithm for few-view computed tomography (CT) reconstruction, but often suffers from over-smoothed image edges. To address this problem, an adaptive NLM reconstruction method based on rotational invariance (ART-RIANLM) is proposed.MethodsThe method consists of four steps: 1) Initializing parameters; 2) ART reconstruction using raw data; 3) Positivity constraint of the reconstructed image; 4) Image updating by RIANLM filtering. In RIANLM, two kinds of rotational invariance measures which are average gradient (AG) and region homogeneity (RH) are proposed to calculate the distance between two patches and a novel NLM filter is developed to avoid over-smoothed image. Moreover, the parameter h in RIANLM which controls the decay of the weights is adaptive to avoid over-smoothness, while it is constant in NLM during the whole reconstruction process. The proposed method is validated on two digital phantoms and real projection data.ResultsIn our experiments, the searching neighborhood size is set as 15 × 15 and the similarity window is set as 3 × 3. For the simulated case of Shepp-Logan phantom, ART-RIANLM produces higher SNR (36.23 dB > 24.00 dB) and lower MAE (0.0006 < 0.0024) reconstructed images than ART-NLM. The visual inspection demonstrated that the proposed method could suppress artifacts or noises more effectively and recover image edges better. The result of real data case is also consistent with the simulation result.ConclusionsA RIANLM based reconstruction method for few-view CT is presented. Compared to the traditional ART-NLM method, SNR and MAE from ART-RIANLM increases 51% and decreases 75%, respectively.     

Resolution and noise performance of sparse view X-ray CT reconstruction via Lp-norm regularization

ObjectivesAdaptive steepest descent projection onto convex sets (ASD-POCS) algorithms with Lp-norm (0 < p ≤ 1) regularization have shown great promise in sparse-view X-ray CT reconstruction. However, the difference in p value selection can lead to varying algorithm performance of noise and resolution. Therefore, it is imperative to develop a reliable method to evaluate the resolution and noise properties of the ASD-POCS algorithms under different Lp-norm priors.MethodsA comparative performance evaluation of ASD-POCS algorithms under different Lp-norm (0 < p ≤ 2) priors were performed in terms of modulation transfer function (MTF), noise power spectrum (NPS) and noise equivalent quanta (NEQ). Simulation data sets from the EGSnrc/BEAMnrc Monte Carlo system and an actual mouse data set were used for algorithms comparison.ResultsA considerable MTF improvement can be achieved with the decrement of p. L1 regularization based algorithm obtains the best noise performance, and shows superiority in NEQ evaluation. The advantage of L1-norm prior is also confirmed by the reconstructions from the actual mouse data set through contrast to noise ratio (CNR) comparison.ConclusionAlthough the ASD-POCS algorithms using small Lp-norm (p ≤ 0.5) priors yield a higher MTF than do the high Lp-norms, the best noise-resolution performance is achieved when p is between 0.8 and 1. The results are expected to be a reference to the choice of p in Lp-norm (0 < p ≤ 2) regularization.     

Portal imaging: Performance improvement in noise reduction by means of wavelet processing

This paper discusses the suitability, in terms of noise reduction, of various methods which can be applied to an image type often used in radiation therapy: the portal image. Among these methods, the analysis focuses on those operating in the wavelet domain. Wavelet-based methods tested on natural images – such as the thresholding of the wavelet coefficients, the minimization of the Stein unbiased risk estimator on a linear expansion of thresholds (SURE-LET), and the Bayes least-squares method using as a prior a Gaussian scale mixture (BLS-GSM method) – are compared with other methods that operate on the image domain – an adaptive Wiener filter and a nonlocal mean filter (NLM). For the assessment of the performance, the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), the Pearson correlation coefficient, and the Spearman rank correlation (ρ) coefficient are used. The performance of the wavelet filters and the NLM method are similar, but wavelet filters outperform the Wiener filter in terms of portal image denoising. It is shown how BLS-GSM and NLM filters produce the smoothest image, while keeping soft-tissue and bone contrast. As for the computational cost, filters using a decimated wavelet transform (decimated thresholding and SURE-LET) turn out to be the most efficient, with calculation times around 1 s.     

Brain PET imaging optimization with time of flight and point spread function modelling

PurposeTo assess the influence of reconstruction algorithms and parameters on the PET image quality of brain phantoms in order to optimize reconstruction for clinical PET brain studies in a new generation PET/CT.MethodsThe 3D Hoffman phantom that simulates ¹⁸F-fluorodeoxyglucose (FDG) distribution was imaged in a Siemens Biograph mCT TrueV PET/CT with Time of Flight (TOF) and Point Spread Function (PSF) modelling. Contrast-to-Noise Ratio (CNR), contrast and noise were studied for different reconstruction models: OSEM, OSEM + TOF, OSEM + PSF and OSEM + PSF + TOF.The 2D multi-compartment Hoffman phantom was filled to simulate 4 different tracers' spatial distribution: FDG, ¹¹C-flumazenil (FMZ), ¹¹C-Methionine (MET) and 6-¹⁸F-fluoro-l-dopa (FDOPA). The best algorithm for each tracer was selected by visual inspection. The maximization of CNR determined the optimal parameters for each reconstruction.ResultsIn the 3D Hoffman phantom, both noise and contrast increased with increasing number of iterations and decreased with increasing FWHM. OSEM + PSF + TOF reconstruction was generally superior to other reconstruction models. Visual analysis of the 2D Hoffman brain phantom suggested that OSEM + PSF + TOF is the optimum algorithm for tracers with focal uptake, such as MET or FDOPA, and OSEM + TOF for tracers with diffuse cortical uptake (i.e. FDG and FMZ). Optimization of CNR demonstrated that OSEM + TOF reconstruction must be performed with 2 iterations and a filter FWHM of 3 mm, and OSEM + PSF + TOF reconstruction with 4 iterations and 1 mm FWHM filter.ConclusionsOptimization of reconstruction algorithm and parameters has been performed to take particular advantage of the last generation PET scanner, recommending specific settings for different brain PET radiotracers.     

An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study

PurposeTo study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed.Materials and methodsThe RSEMD method was tested on in-silico, Catphan^®500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver.ResultsIn all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms.ConclusionsIn this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.     

Intérêt de la TEP dans le bilan d’extension et l’évaluation de la maladie résiduelle des séminomes testiculaires : étude rétrospective

AimsTo evaluate the value of PET/CT comparatively to CT in staging and restaging after chemotherapy of testicular seminoma, to assess quantitative methods and prognostic value of PET in post-chemotherapy residual masses.MethodsThirty-two patients and a maximum of 65 targeted lesions visualized on PET-CT and CT performed for staging and therapeutic response assessment were analysed and compared. Each lesion was quantified according to miscellaneous SUV normalized methods. Optimal threshold of SUV for prediction of residual disease was obtained (ROC method). The prognostic value of PET/CT at the completion of treatment was determined with progression free survival study (Kaplan-Meier method).ResultsPET/CT exhibited higher accuracy than CT in the initial staging and assessment of therapeutic response, respectively 98% versus 83.3% and 95.1% versus 75.6%. Quantification, whichever method, was not more efficient than visual reading for prediction of residual disease. Progression-free survival was higher with negative than with positive PET/CT (P = 0.0033).ConclusionOur work demonstrates that PET/CT exhibits better accuracy than CT in both staging and restaging at the end of treatment. Quantification methods do not improve accuracy of PET/CT for prediction of viable residual disease. The prognostic value of PET/CT appears very promising and needs to be confirmed by large prospective studies. PET/CT appears to be a relevant method of prognostic stratification of the risk of relapse in seminoma.     

Image quality for radiotherapy CT simulators with different scanner bore size

PurposeWe compare image quality parameters derived from phantom images taken on three commercially available radiotherapy CT simulators. To make an unbiased evaluation, we assured images were obtained with the same surface dose measured using XR-QA2 model GafChromic™ film placed at the imaging phantom surface for all three CT-simulators.MethodsRadiotherapy CT simulators GE LS 16, Philips Brilliance Big Bore, and Toshiba Aquilion LB were compared in terms of spatial resolution, low contrast detectability, image uniformity, and contrast to noise ratio using CATPHAN-504 phantom, scanned with Head and Pelvis protocols. Dose was measured at phantom surface, with CT scans repeated until doses on all scanners were within 2%.ResultsIn terms of spatial resolution, the GE simulator appears slightly better, while Philips CT images are superior in terms of SNR for both scanning protocols. The CNR results show that Philips CT images appear to be better, except for high Z material, while Toshiba appears to fit in between the two simulators.ConclusionsWhile the image quality parameters for three RT CT simulators show comparable results, the scanner bore size is of vital importance in various radiotherapy applications. Since the image quality is a function of a large number of confounding parameters, any loss in image quality due to scanner bore size could be compensated by the appropriate choice of scanning parameters, including the exposure and by balancing between the additional imaging dose to the patient and high image quality required in highly conformal RT techniques.     

3D TOF-PET image reconstruction using total variation regularization

In this paper we introduce a semi-analytic algorithm for 3-dimensional image reconstruction for positron emission tomography (PET). The method consists of the back-projection of the acquired data into the most likely image voxel according to time-of-flight (TOF) information, followed by the filtering step in the image space using an iterative optimization algorithm with a total variation (TV) regularization. TV regularization in image space is more computationally efficient than usual iterative optimization methods for PET reconstruction with full system matrix that use TV regularization. The efficiency comes from the one-time TOF back-projection step that might also be described as a reformatting of the acquired data. An important aspect of our work concerns the evaluation of the filter operator of the linear transform mapping an original radioactive tracer distribution into the TOF back-projected image. We obtain concise, closed-form analytical formula for the filter operator. The proposed method is validated with the Monte Carlo simulations of the NEMA IEC phantom using a one-layer, 50 cm-long cylindrical device called Jagiellonian PET scanner. The results show a better image quality compared with the reference TOF maximum likelihood expectation maximization algorithm.