Partial Volume Correction Algorithm Research Articles

Impact of Aggregation Methods for Texture Features on Their Robustness Performance: Application to Nasopharyngeal 18F-FDG PET/CT.

This study aims to investigate the impact of aggregation methods used for the generation of texture features on their robustness of nasopharyngeal carcinoma (NPC) based on 18F-FDG PET/CT images. 128 NPC patients were enrolled and 95 texture features were extracted for each patient including six feature families under different aggregation methods. For GLCM and GLRLM features, six aggregation methods were considered. For GLSZM, GLDZM, NGTDM and NGLDM features, three aggregation methods were considered. The robustness of the features affected by aggregation methods was assessed by the pair-wise intra-class correlation coefficient (ICC). Furthermore, the effects of discretization and partial volume correction (PVC) on the percent of ICC categories of all texture features were evaluated by overall ICC instead of the pair-wise ICC. There were 12 features with excellent pair-wise ICCs varying aggregation methods, namely joint average, sum average, autocorrelation, long run emphasis, high grey level run emphasis, short run high grey level emphasis, long run high grey level emphasis, run length variance, SZM high grey level emphasis, DZM high grey level emphasis, high grey level count emphasis and dependence count percentage. For GLCM and GLRLM features, 19/25 and 14/16 features showed excellent pair-wise ICCs varying aggregation methods (averaged and merged) on the same dimensional features (2D, 2.5D or 3D). Different discretization levels and partial volume corrections lead to consistent robustness of textural features affected by aggregation methods. Different dimensional features with the same aggregation methods showed worse robustness compared with the same dimensional features with different aggregation methods. Different discretization levels and PVC algorithms had a negligible effect on the percent of ICC categories of all texture features.

Arterial spin labeling signal in the CSF: Implications for partial volume correction and blood-CSF barrier characterization.

For better quantification of perfusion with arterial spin labeling (ASL), partial volume correction (PVC) is used to disentangle the signals from gray matter (GM) and white matter within any voxel. Based on physiological considerations, PVC algorithms typically assume zero signal in the cerebrospinal fluid (CSF). Recent measurements, however, have shown that CSF-ASL signal can exceed 10% of GM signal, even when using recommended ASL labeling parameters. CSF signal is expected to particularly affect PVC results in the choroid plexus. This study aims to measure the impact of CSF signal on PVC perfusion measurements, and to investigate the potential use of PVC to retrieve pure CSF-ASL signal for blood-CSF barrier characterization. In vivo imaging included six pCASL sequences with variable label duration and post-labeling delay (PLD), and an eight-echo 3D-GRASE readout. A dataset was simulated to estimate the effect of CSF-PVC with known ground-truth parameters. Differences between the results of CSF-PVC and non-CSF-PVC were estimated for regions of interest (ROIs) based on GM probability, and a separate ROI isolating the choroid plexus. In vivo, the suitability of PVC-CSF signal as an estimate of pure CSF was investigated by comparing its time course with the long-TE CSF signal. Results from both simulation and in vivo data indicated that including the CSF signal in PVC improves quantification of GM CBF by approximately 10%. In simulated data, this improvement was greater for multi-PLD (model fitting) quantification than for single PLD (~1-5% difference). In the choroid plexus, the difference between CSF-PVC and non-CSF-PVC was much larger, averaging around 30%. Long-TE (pure) CSF signal could not be estimated from PVC CSF signal as it followed a different time course, indicating the presence of residual macrovascular signal in the PVC. The inclusion of CSF adds value to PVC for more accurate measurements of GM perfusion, and especially for quantification of perfusion in the choroid plexus and study of the glymphatic system.

Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

BackgroundNovel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome.MethodsWe aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer’s disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR.ResultsUncorrected SUVRs were underestimated against true SUVRs (− 15.7 and − 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions.ConclusionsWe demonstrated error propagation by numerical simulation of THK-PET imaging.Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.

Segmentation of prostate zones using probabilistic atlas-based method with diffusion-weighted MR images

Background and objectiveAccurate segmentation of prostate and its zones constitute an essential preprocessing step for computer-aided diagnosis and detection system for prostate cancer (PCa) using diffusion-weighted imaging (DWI). However, low signal-to-noise ratio and high variability of prostate anatomic structures are challenging for its segmentation using DWI. We propose a semi-automated framework that segments the prostate gland and its zones simultaneously using DWI. MethodsIn this paper, the Chan-Vese active contour model along with morphological opening operation was used for segmentation of prostate gland. Then segmentation of prostate zones into peripheral zone (PZ) and transition zone (TZ) was carried out using in-house developed probabilistic atlas with partial volume (PV) correction algorithm. The study cohort included MRI dataset of 18 patients (n = 18) as our dataset and methodology were also independently evaluated using 15 MRI scans (n = 15) of QIN-PROSTATE-Repeatability dataset. The atlas for zones of prostate gland was constructed using dataset of twelve patients of our patient cohort. Three-fold cross-validation was performed with 10 repetitions, thus total 30 instances of training and testing were performed on our dataset followed by independent testing on the QIN-PROSTATE-Repeatability dataset. Dice similarity coefficient (DSC), Jaccard coefficient (JC), and accuracy were used for quantitative assessment of the segmentation results with respect to boundaries delineated manually by an expert radiologist. A paired t-test was performed to evaluate the improvement in zonal segmentation performance with the proposed PV correction algorithm. ResultsFor our dataset, the proposed segmentation methodology produced improved segmentation with DSC of 90.76 ± 3.68%, JC of 83.00 ± 5.78%, and accuracy of 99.42 ± 0.36% for the prostate gland, DSC of 77.73 ± 2.76%, JC of 64.46 ± 3.43%, and accuracy of 82.47 ± 2.22% for the PZ, and DSC of 86.05 ± 1.50%, JC of 75.80 ± 2.10%, and accuracy of 91.67 ± 1.56% for the TZ. The segmentation performance for QIN-PROSTATE-Repeatability dataset was, DSC of 85.50 ± 4.43%, JC of 75.00 ± 6.34%, and accuracy of 81.52 ± 5.55% for prostate gland, DSC of 74.40 ± 1.79%, JC of 59.53 ± 8.70%, and accuracy of 80.91 ± 5.16% for PZ, and DSC of 85.80 ± 5.55%, JC of 74.87 ± 7.90%, and accuracy of 90.59 ± 3.74% for TZ. With the implementation of the PV correction algorithm, statistically significant (p<0.05) improvements were observed in all the metrics (DSC, JC, and accuracy) for both prostate zones, PZ and TZ segmentation. ConclusionsThe proposed segmentation methodology is stable, accurate, and easy to implement for segmentation of prostate gland and its zones (PZ and TZ). The atlas-based segmentation framework with PV correction algorithm can be incorporated into a computer-aided diagnostic system for PCa localization and treatment planning.

Reproducibility of CMRO2 determination using dynamic 17O MRI

To assess the reproducibility of 17 O MRI-based determination of the cerebral metabolic rate of oxygen consumption (CMRO2 ) in healthy volunteers. To assess the influence of image acquisition and reconstruction parameters on dynamic quantification of functional parameters such as CMRO2 . Dynamic 17 O MRI data were simulated and used to investigate influences of temporal resolution (Δt) and partial volume correction (PVC) on the determination of CMRO2 . Three healthy volunteers were examined in two separate examinations. In vivo 17 O MRI measurements were conducted with a nominal spatial resolution of (7.5 mm)3 using a density-adapted radial sequence with golden angle acquisition scheme. In each measurement, 4.0 ± 0.1 L of 70%-enriched 17 O gas were administered using a rebreathing system. Data were corrected with a PVC algorithm, and CMRO2 was determined in gray matter (GM) and white matter (WM) compartments using a three-phase metabolic model (baseline, 17 O inhalation, decay phase). Comparison with the ground truth of simulations revealed improved CMRO2 determination after application of PVC and with Δt ≤ 2:00 min. Evaluation of in vivo data yields to CMRO2,GM = 2.31 ± 0.1 μmol/g/min and to CMRO2,WM = 0.69 ± 0.04 μmol/g/min with coefficients of variation (CoV) of 0.3-5.5% and 4.3-5.0% for intra-volunteer and inter-volunteer data, respectively. This in vivo 17 O inhalation study demonstrated that the proposed experimental setup enables reproducible determination of CMRO2 in healthy volunteers. Magn Reson Med 79:2923-2934, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Improved T*₂ determination in 23Na, 35Cl, and 17O MRI using iterative partial volume correction based on 1H MRI segmentation.

Functional parameters can be measured with the help of quantitative non-proton MRI where exact relaxometry parameters are needed. Investigation of [Formula: see text] is often biased by strong partial volume (PV) effects. Hence, in this work a PV correction algorithm approach was evaluated that uses iteratively adapted [Formula: see text]-values and high-resolution structural 1H data to determine transverse relaxation in non-proton MRI more accurately. Simulations, a phantom study and in vivo 23Na, 17O and 35Cl MRI measurements of five healthy volunteers were performed to evaluate the algorithm. [Formula: see text] values of grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) were obtained. Data were acquired at B 0 =7T with nominal spatial resolutions of (4-7mm)3 using a density-adapted radial sequence. The resulting transverse relaxation times were used for quantification of 17O data. The conducted simulations and phantom study verified the correction performance of the algorithm. For in vivo measured [Formula: see text] values, the correction of PV effects leads to an increase in CSF and to a decrease in GM/WM (23Na MRI: long/short GM, WM [Formula: see text]: 36.4±3.1/5.4±0.2, 23.3±2.6/3.5±0.1ms; 35Cl MRI: 8.9±1.4/1.0±0.4, 5.9±0.3/0.4±0.1ms; 17O MRI: 2.5±0.1, 2.8±0.1ms). Iteratively corrected in vivo [Formula: see text] values of the 17O study resulted in improved water content quantification. The proposed iterative algorithm for PV correction leads to more accurate [Formula: see text] values and, thus, can improve accuracy in quantitative non-proton MRI.

Noise suppressed partial volume correction for cardiac SPECT/CT.

Partial volume correction (PVC) methods typically improve quantification at the expense of increased image noise and reduced reproducibility. In this study, the authors developed a novel voxel-based PVC method that incorporates anatomical knowledge to improve quantification while suppressing noise for cardiac SPECT/CT imaging. In the proposed method, the SPECT images were first reconstructed using anatomical-based maximum a posteriori (AMAP) with Bowsher's prior to penalize noise while preserving boundaries. A sequential voxel-by-voxel PVC approach (Yang's method) was then applied on the AMAP reconstruction using a template response. This template response was obtained by forward projecting a template derived from a contrast-enhanced CT image, and then reconstructed using AMAP to model the partial volume effects (PVEs) introduced by both the system resolution and the smoothing applied during reconstruction. To evaluate the proposed noise suppressed PVC (NS-PVC), the authors first simulated two types of cardiac SPECT studies: a (99m)Tc-tetrofosmin myocardial perfusion scan and a (99m)Tc-labeled red blood cell (RBC) scan on a dedicated cardiac multiple pinhole SPECT/CT at both high and low count levels. The authors then applied the proposed method on a canine equilibrium blood pool study following injection with (99m)Tc-RBCs at different count levels by rebinning the list-mode data into shorter acquisitions. The proposed method was compared to MLEM reconstruction without PVC, two conventional PVC methods, including Yang's method and multitarget correction (MTC) applied on the MLEM reconstruction, and AMAP reconstruction without PVC. The results showed that the Yang's method improved quantification, however, yielded increased noise and reduced reproducibility in the regions with higher activity. MTC corrected for PVE on high count data with amplified noise, although yielded the worst performance among all the methods tested on low-count data. AMAP effectively suppressed noise and reduced the spill-in effect in the low activity regions. However it was unable to reduce the spill-out effect in high activity regions. NS-PVC yielded superior performance in terms of both quantitative assessment and visual image quality while improving reproducibility. The results suggest that NS-PVC may be a promising PVC algorithm for application in low-dose protocols, and in gated and dynamic cardiac studies with low counts.

Partial volume correction for in vivo 23Na-MRI data of the human brain

The concentration of sodium is a functional cell parameter and absolute quantification can be interesting for diagnostical purposes. The accuracy of sodium magnetic resonance imaging (23Na-MRI) is strongly biased by partial volume effects (PVEs). Hence our purpose was to establish a partial volume correction (PVC) method for 23Na-MRI.The existing geometric transfer matrix (GTM) correction method was transferred from positron emission tomography (PET) to 23Na-MRI and tested in a phantom study. Different parameters, as well as accuracy of registration and segmentation were evaluated prior to first in vivo measurements. In vivo sodium data-sets of the human brain were obtained at B0=7T with a nominal spatial resolution of (3mm)3 using a density adapted radial pulse sequence. A volunteer study with four healthy subjects was performed to measure partial volume (PV) corrected tissue sodium concentration (TSC) which was verified by means of an intrinsic correction control.In the phantom study the PVC algorithm yielded a good correction performance and reduced the discrepancy between the measured sodium concentration value and the expected value in the smallest compartments of the phantom by 11% to a mean PVE induced discrepancy of 5.7% after correction. The corrected in vivo data showed a reduction of PVE bias for the investigated compartments for all volunteers, resulting in a mean reduction of discrepancy between two separate CSF compartments from 36% to 7.6%. The absolute TSC for two separate CSF compartments (sulci, lateral ventricles), gray and white brain matter after correction were 129±8mmol/L, 138±4mmol/L, 48±1mmol/L and 43±3mmol/L, respectively.The applied PVC algorithm reduces the PV-bias in quantitative 23Na-MRI. Accurate, high-resolution anatomical data is required to enable appropriate PVC. The algorithm and segmentation approach is robust and leads to reproducible results.

A simple algorithm for subregional striatal uptake analysis with partial volume correction in dopaminergic PET imaging

In positron emission tomography (PET) of the dopaminergic system, quantitative measurements of nigrostriatal dopamine function are useful for differential diagnosis. A subregional analysis of striatal uptake enables the diagnostic performance to be more powerful. However, the partial volume effect (PVE) induces an underestimation of the true radioactivity concentration in small structures. This work proposes a simple algorithm for subregional analysis of striatal uptake with partial volume correction (PVC) in dopaminergic PET imaging. The PVC algorithm analyzes the separate striatal subregions and takes into account the PVE based on the recovery coefficient (RC). The RC is defined as the ratio of the PVE-uncorrected to PVE-corrected radioactivity concentration, and is derived from a combination of the traditional volume of interest (VOI) analysis and the large VOI technique. The clinical studies, comprising 11 patients with Parkinson's disease (PD) and 6 healthy subjects, were used to assess the impact of PVC on the quantitative measurements. Simulations on a numerical phantom that mimicked realistic healthy and neurodegenerative situations were used to evaluate the performance of the proposed PVC algorithm. In both the clinical and the simulation studies, the striatal-to-occipital ratio (SOR) values for the entire striatum and its subregions were calculated with and without PVC. In the clinical studies, the SOR values in each structure (caudate, anterior putamen, posterior putamen, putamen, and striatum) were significantly higher by using PVC in contrast to those without. Among the PD patients, the SOR values in each structure and quantitative disease severity ratings were shown to be significantly related only when PVC was used. For the simulation studies, the average absolute percentage error of the SOR estimates before and after PVC were 22.74% and 1.54% in the healthy situation, respectively; those in the neurodegenerative situation were 20.69% and 2.51%, respectively. We successfully implemented a simple algorithm for subregional analysis of striatal uptake with PVC in dopaminergic PET imaging. The PVC algorithm provides an accurate measure of the SOR in the entire striatum and its subregions, and improves the correlation between the SOR values and the clinical disease severity of PD patients.

Influence of the partial volume correction method on 18F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM

Kinetic parameters estimated from dynamic 18F-fluorodeoxyglucose (18F-FDG) PET acquisitions have been used frequently to assess brain function in humans. Neglecting partial volume correction (PVC) for a dynamic series has been shown to produce significant bias in model estimates. Accurate PVC requires a space-variant model describing the reconstructed image spatial point spread function (PSF) that accounts for resolution limitations, including non-uniformities across the field of view due to the parallax effect. For ordered subsets expectation maximization (OSEM), image resolution convergence is local and influenced significantly by the number of iterations, the count density, and background-to-target ratio. As both count density and background-to-target values for a brain structure can change during a dynamic scan, the local image resolution may also concurrently vary. When PVC is applied post-reconstruction the kinetic parameter estimates may be biased when neglecting the frame-dependent resolution. We explored the influence of the PVC method and implementation on kinetic parameters estimated by fitting 18F-FDG dynamic data acquired on a dedicated brain PET scanner and reconstructed with and without PSF modelling in the OSEM algorithm. The performance of several PVC algorithms was quantified with a phantom experiment, an anthropomorphic Monte Carlo simulation, and a patient scan. Using the last frame reconstructed image only for regional spread function (RSF) generation, as opposed to computing RSFs for each frame independently, and applying perturbation geometric transfer matrix PVC with PSF based OSEM produced the lowest magnitude bias kinetic parameter estimates in most instances, although at the cost of increased noise compared to the PVC methods utilizing conventional OSEM. Use of the last frame RSFs for PVC with no PSF modelling in the OSEM algorithm produced the lowest bias in cerebral metabolic rate of glucose estimates, although by less than 5% in most cases compared to the other PVC methods. The results indicate that the PVC implementation and choice of PSF modelling in the reconstruction can significantly impact model parameters.

A three-dimensional model-based partial volume correction strategy for gated cardiac mouse PET imaging

Quantification in cardiac mouse positron emission tomography (PET) imaging is limited by the imaging spatial resolution. Spillover of left ventricle (LV) myocardial activity into adjacent organs results in partial volume (PV) losses leading to underestimation of myocardial activity. A PV correction method was developed to restore accuracy of the activity distribution for FDG mouse imaging. The PV correction model was based on convolving an LV image estimate with a 3D point spread function. The LV model was described regionally by a five-parameter profile including myocardial, background and blood activities which were separated into three compartments by the endocardial radius and myocardium wall thickness. The PV correction was tested with digital simulations and a physical 3D mouse LV phantom. In vivo cardiac FDG mouse PET imaging was also performed. Following imaging, the mice were sacrificed and the tracer biodistribution in the LV and liver tissue was measured using a gamma-counter. The PV correction algorithm improved recovery from 50% to within 5% of the truth for the simulated and measured phantom data and image uniformity by 5–13%. The PV correction algorithm improved the mean myocardial LV recovery from 0.56 (0.54) to 1.13 (1.10) without (with) scatter and attenuation corrections. The mean image uniformity was improved from 26% (26%) to 17% (16%) without (with) scatter and attenuation corrections applied. Scatter and attenuation corrections were not observed to significantly impact PV-corrected myocardial recovery or image uniformity. Image-based PV correction algorithm can increase the accuracy of PET image activity and improve the uniformity of the activity distribution in normal mice. The algorithm may be applied using different tracers, in transgenic models that affect myocardial uptake, or in different species provided there is sufficient image quality and similar contrast between the myocardium and surrounding structures.

A method for model-free partial volume correction in oncological PET

BackgroundAs is well known, limited spatial resolution leads to partial volume effects (PVE) and consequently to limited signal recovery. Determination of the mean activity concentration of a target structure is thus compromised even at target sizes much larger than the reconstructed spatial resolution. This leads to serious size-dependent underestimates of true signal intensity in hot spot imaging. For quantitative PET in general and in the context of therapy assessment in particular it is, therefore, mandatory to perform an adequate partial volume correction (PVC). The goal of our work was to develop and to validate a model-free PVC algorithm for hot spot imaging.MethodsThe algorithm proceeds in two automated steps. Step 1: estimation of the actual object boundary with a threshold based method and determination of the total activity A measured within the enclosed volume V. Step 2: determination of the activity fraction B, which is measured outside the object due to the partial volume effect (spill-out). The PVE corrected mean value is then given by Cmean = (A+B)/V. For validation simulated tumours were used which were derived from real patient data (liver metastases of a colorectal carcinoma and head and neck cancer, respectively). The simulated tumours have characteristics (regarding tumour shape, contrast, noise, etc.) which are very similar to those of the underlying patient data, but the boundaries and tracer accumulation are exactly known. The PVE corrected mean values of 37 simulated tumours were determined and compared with the true mean values.ResultsFor the investigated simulated data the proposed approach yields PVE corrected mean values which agree very well with the true values (mean deviation (± s.d.): (−0.8±2.5)%).ConclusionsThe described method enables accurate quantitative partial volume correction in oncological hot spot imaging.

In vivo Validation of Reconstruction-Based Resolution Recovery for Human Brain Studies

The aim of this study was to validate in vivo the accuracy of a reconstruction-based partial volume correction (PVC), which takes into account the point spread function of the imaging system. The NEMA NU2 Image Quality phantom and five healthy volunteers (using [(11)C]flumazenil) were scanned on both HR+ and high-resolution research tomograph (HRRT) scanners. HR+ data were reconstructed using normalization and attenuation-weighted ordered subsets expectation maximization (NAW-OSEM) and a PVC algorithm (PVC-NAW-OSEM). HRRT data were reconstructed using 3D ordinary Poisson OSEM (OP-OSEM) and a PVC algorithm (PVC-OP-OSEM). For clinical studies, parametric volume of distribution (V(T)) images were generated. For phantom data, good recovery was found for both OP-OSEM (0.84 to 0.97) and PVC-OP-OSEM (0.91 to 0.98) HRRT reconstructions. In addition, for the HR+, good recovery was found for PVC-NAW-OSEM (0.84 to 0.94), corresponding well with OP-OSEM. Finally, for clinical data, good correspondence was found between PVC-NAW-OSEM and OP-OSEM-derived V(T) values (slope: 1.02+/-0.08). This study showed that HR+ image resolution using PVC-NAW-OSEM was comparable to that of the HRRT scanner. As the HRRT has a higher intrinsic resolution, this agreement validates reconstruction-based PVC as a means of improving the spatial resolution of the HR+ scanner and thereby improving the quantitative accuracy of positron emission tomography.

Relationship Between Non-small Cell Lung Cancer FDG Uptake at PET, Tumor Histology, and Ki-67 Proliferation Index

We compared primary non-small cell lung cancer (NSCLC) F-fluorodeoxyglucose (FDG) uptake at positron emission tomography (PET) to tumor histologic features and Ki-67 proliferation index. This large, prospectively-recruited patient cohort has previously been analyzed based on differences in FDG uptake across stage groups; the current analysis adds further dimensions to this characterization. One hundred seventy-eight patients with potentially-resectable NSCLC were scanned with FDG PET before therapy. A partial volume correction algorithm was used to correct FDG uptake values for their dependence on tumor size. Primary tumor resection specimens, core biopsies, and biopsies of metastatic lymph nodes were used to assess each tumor's NSCLC histologic subtype, degree of differentiation, and Ki-67 proliferation index. Bronchioalveolar carcinomas were found to have lower FDG uptake at PET and lower Ki-67 scores than any other histologic subtype. Non-bronchioalveolar adenocarcinomas had lower FDG uptake and Ki-67 scores than squamous cell carcinomas or large cell undifferentiated carcinomas. Better differentiated NSCLCs had lower FDG uptake and Ki-67 scores than more poorly differentiated NSCLCs. There was a significant positive correlation between FDG uptake and Ki-67 scores. Partial volume correction increased the strength of this correlation, while also diminishing the strong positive correlation between FDG uptake and tumor size. There are significant differences in NSCLC FDG uptake across histologic subtypes and differentiation groups. These differences parallel nearly identical differences in Ki-67 scores, implying that differences in NSCLC tumor cell proliferation may give rise to commensurate differences in tumor glucose metabolism.

Bootstrapped DEPICT for error estimation in PET functional imaging

Basis pursuit denoising is a new approach for data-driven estimation of parametric images from dynamic positron emission tomography (PET) data. At present, this kinetic modeling technique does not allow for the estimation of the errors on the parameters. These estimates are useful when performing subsequent statistical analysis, such as, inference across a group of subjects or when applying partial volume correction algorithms. The difficulty with calculating the error estimates is a consequence of using an overcomplete dictionary of kinetic basis functions. In this paper, a bootstrap approach for the estimation of parameter errors from dynamic PET data is presented. This paper shows that the bootstrap can be used successfully to compute parameter errors on a region of interest or parametric image basis. Validation studies evaluate the methods performance on simulated and measured PET data ([ 11C]Diprenorphine—opiate receptor and [ 11C]Raclopride—dopamine D 2 receptor). The method is presented in the context of PET neuroreceptor binding studies, however, it has general applicability to a wide range of PET/SPET radiotracers in neurology, oncology and cardiology.

Measuring the arterial input function with gradient echo sequences.

The measurement of the arterial input function by use of gradient echo sequences was investigated by in vitro and in vivo experiments. First, calibration curves representing the influence of the concentration of Gd-DTPA on both the phase and the amplitude of the MR signal were measured in human blood by means of a slow-infusion experiment. The results showed a linear increase in the phase velocity and a quadratic increase in DeltaR(*) (2) as a function of the Gd-DTPA concentration. Next, the resultant calibration curves were incorporated in a partial volume correction algorithm for the arterial input function determination. The algorithm was tested in a phantom experiment and was found to substantially improve the accuracy of the concentration measurement. Finally, the reproducibility of the arterial input function measurement was estimated in 16 patients by considering the input function of the left and the right sides as replicate measurements. This in vivo study showed that the reproducibility of the arterial input function determination using gradient echo sequences is improved by employing a partial volume correction algorithm based on the calibration curve for the contrast agent used.

Positron emission tomography partial volume correction: estimation and algorithms.

Partial volume effects in positron emission tomography (PET) lead to quantitative under- and over-estimations of the regional concentrations of radioactivity in reconstructed images and corresponding errors in derived functional or parametric images. The limited resolution of PET leads to "tissue-fraction" effects, reflecting underlying tissue heterogeneity, and "spillover" effects between regions. Addressing the former problem in general requires supplementary data, for example, coregistered high-resolution magnetic resonance images, whereas the latter effect can be corrected for with PET data alone if the point-spread function of the tomograph has been characterized. Analysis of otherwise homogeneous region-of-interest data ideally requires a combination of tissue classification and correction for the point-spread function. The formulation of appropriate algorithms for partial volume correction (PVC) is dependent on both the distribution of the signal and the distribution of the underlying noise. A mathematical framework has therefore been developed to accommodate both of these factors and to facilitate the development of new PVC algorithms based on the description of the problem. Several methodologies and algorithms have been proposed and implemented in the literature in order to address these problems. These methods do not, however, explicitly consider the noise model while differing in their underlying assumptions. The general theory for estimation of regional concentrations, associated error estimation, and inhomogeneity tests are presented in a weighted least squares framework. The analysis has been validated using both simulated and real PET data sets. The relations between the current algorithms and those published previously are formulated and compared. The incorporation of tensors into the formulation of the problem has led to the construction of computationally rapid algorithms taking into account both tissue-fraction and spillover effects. The suitability of their application to dynamic and static images is discussed.

MR-based correction of brain PET measurements for heterogeneous gray matter radioactivity distribution.

Partial volume and mixed tissue sampling errors can cause significant inaccuracy in quantitative positron emission tomographic (PET) measurements. We previously described a method of correcting PET data for the effects of partial volume averaging on gray matter (GM) quantitation; however, this method may incompletely correct GM structures when local tissue concentrations are highly heterogeneous. We have extended this three-compartment algorithm to include a fourth compartment: a GM volume of interest (VOI) that can be delineated on magnetic resonance (MR) imaging. Computer simulations of PET images created from human MR data demonstrated errors of up to 120% in assigned activity values in small brain structures in uncorrected data. Four-compartment correction achieved full recovery of a wide range of coded activity in GM VOIs such as the amygdala, caudate, and thalamus. Further validation was performed in an agarose brain phantom in actual PET acquisitions. Implementation of this partial volume correction approach in [18F]fluorodeoxyglucose and [11C]-carfentanil PET data acquired in a healthy elderly human subject was also performed. This newly developed MR-based partial volume correction algorithm permits the accurate determination of the true radioactivity concentration in specific structures that can be defined by MR by accounting for the influence of heterogeneity of GM radioactivity.