Partial Volume Effect Correction Research Articles

Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction

To explore the effect of T2DM on cerebral perfusion, and the relationship between cerebral perfusion changes and cognitive impairment as well as diabetic variables, by using a whole-brain arterial spin-labeling (ASL) MRI technique. This prospective study was approved by the local institutional review board and was performed between November 2012 and October 2013. All subjects provided informed consent. Forty T2DM patients and 41 age-, sex- and education-matched healthy controls were included. Cerebral blood flow (CBF) map was obtained by pulsed ASL perfusion imaging at 3 T MRI. Voxel-wise comparisons on CBF maps with and without partial volume effects (PVEs) correction were performed between groups. Associations between CBF and cognitive functioning, and between CBF and diabetic variables were investigated by using voxel-wise, whole-brain correlation analyses. In T2DM patients, PVEs uncorrected CBF was decreased in the posterior cingulate cortex (PCC), precuneus and bilateral occipital lobe, and increased in the anterior cingulate cortex (corrected P < .05). These changes were largely unchanged after PVEs correction. Correlation analyses revealed that in patients, hypoperfusion in PCC and precuneus regions were related to higher insulin resistance level and deficits in clock-drawing performance, while the occipital hypoperfusion was associated with worse visual-memory performance, regardless of PVEs correction. The cerebral hypoperfusion pattern in T2DM resembles the pattern observed in the early stage of dementia, and increased insulin resistance might be an important risk factor as well as treatment target for such CBF dysregulation.

Correction: Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group.

[This corrects the article DOI: 10.1371/journal.pone.0152082.].

PET/MRI: A luxury or a necessity?

PET/MRI is a new multimodality technique with a promising future in diagnostic imaging. Technical limitations are being overcome. Interference between the two systems (PET and MRI) seems to have been resolved. MRI-based PET attenuation correction can be performed safely. Scan time is acceptable and the study is tolerable, with claustrophobia prevalence similar to that of MRI. Quantification with common parameters, such as standardized uptake value (SUV), shows a fairly good correlation between both systems. However, PET/CT currently provides better results in scan time, scan costs, and patient comfort. Less patient radiation exposure is a big advantage of PET/MRI over PET/CT, which makes it particularly recommended in paediatric and adolescent patients requiring one or more studies. PET/MRI indications are the same as those of PET/CT, given that in cases where MRI is superior to CT, PET/MRI is superior to PET/CT. This superiority is clear in many soft tissue tumours. Moreover, it is common to perform both PET/CT and MRI in neurological diseases, as well as in some tumours, such as breast cancer. A single PET/MRI study replaces both with obvious benefit. MRI also allows other MRI-based PET corrections, such as motion or partial volume effect corrections. The better spatial resolution of MRI allows the transfer of well-defined MRI areas or small volumes of interest to PET image, in order to measure PET biomarkers in these areas. The richness of information of both techniques opens up immense possibilities of synergistic correlation between them.

PET/MRI: A luxury or a necessity?

PET/MRI is a new multimodality technique with a promising future in diagnostic imaging. Technical limitations are being overcome. Interference between the two systems (PET and MRI) seems to have been resolved. MRI-based PET attenuation correction can be performed safely. Scan time is acceptable and the study is tolerable, with claustrophobia prevalence similar to that of MRI. Quantification with common parameters, such as Standardized Uptake Value (SUV), shows a fairly good correlation between both systems. However, PET/CT currently provides better results in scan time, scan costs, and patient comfort. Less patient radiation exposure is a big advantage of PET/MRI over PET/CT, which makes it particularly recommended in paediatric and adolescent patients requiring one or more studies. PET/MRI indications are the same as those of PET/CT, given that in cases where MRI is superior to CT, PET/MRI is superior to PET/CT. This superiority is clear in many soft tissue tumours. Moreover, it is common to perform both PET/CT and MRI in neurological diseases, as well as in some tumours, such as breast cancer. A single PET/MRI study replaces both with obvious benefit. MRI also allows other MRI-based PET corrections, such as motion or partial volume effect corrections. The better spatial resolution of MRI allows the transfer of well-defined MRI areas or small volumes of interest to PET image, in order to measure PET biomarkers in these areas. The richness of information of both techniques opens up immense possibilities of synergistic correlation between them.

Monitoring of Tumor Growth with [(18)F]-FET PET in a Mouse Model of Glioblastoma: SUV Measurements and Volumetric Approaches.

Noninvasive tumor growth monitoring is of particular interest for the evaluation of experimental glioma therapies. This study investigates the potential of positron emission tomography (PET) using O-(2-18F-fluoroethyl)-L-tyrosine ([18F]-FET) to determine tumor growth in a murine glioblastoma (GBM) model—including estimation of the biological tumor volume (BTV), which has hitherto not been investigated in the pre-clinical context. Fifteen GBM-bearing mice (GL261) and six control mice (shams) were investigated during 5 weeks by PET followed by autoradiographic and histological assessments. [18F]-FET PET was quantitated by calculation of maximum and mean standardized uptake values within a universal volume-of-interest (VOI) corrected for healthy background (SUVmax/BG, SUVmean/BG). A partial volume effect correction (PVEC) was applied in comparison to ex vivo autoradiography. BTVs obtained by predefined thresholds for VOI definition (SUV/BG: ≥1.4; ≥1.6; ≥1.8; ≥2.0) were compared to the histologically assessed tumor volume (n = 8). Finally, individual “optimal” thresholds for BTV definition best reflecting the histology were determined. In GBM mice SUVmax/BG and SUVmean/BG clearly increased with time, however at high inter-animal variability. No relevant [18F]-FET uptake was observed in shams. PVEC recovered signal loss of SUVmean/BG assessment in relation to autoradiography. BTV as estimated by predefined thresholds strongly differed from the histology volume. Strikingly, the individual “optimal” thresholds for BTV assessment correlated highly with SUVmax/BG (ρ = 0.97, p < 0.001), allowing SUVmax/BG-based calculation of individual thresholds. The method was verified by a subsequent validation study (n = 15, ρ = 0.88, p < 0.01) leading to extensively higher agreement of BTV estimations when compared to histology in contrast to predefined thresholds. [18F]-FET PET with standard SUV measurements is feasible for glioma imaging in the GBM mouse model. PVEC is beneficial to improve accuracy of [18F]-FET PET SUV quantification. Although SUVmax/BG and SUVmean/BG increase during the disease course, these parameters do not correlate with the respective tumor size. For the first time, we propose a histology-verified method allowing appropriate individual BTV estimation for volumetric in vivo monitoring of tumor growth with [18F]-FET PET and show that standardized thresholds from routine clinical practice seem to be inappropriate for BTV estimation in the GBM mouse model.

The effect of tumour geometry on the quantification accuracy of planar 123I phantom images

IntroductionAccurate activity quantification is applied in radiation dosimetry. Planar images are important for quantification of whole-body images, enabling assessment of biodistribution from radionuclide administrations. We evaluated the effect of tumour geometry on quantification accuracy of 123I planar phantom studies, including various tumour sizes, tumour-liver distances and two tumour-background ratios. Methods and materialsAn in-house manufactured abdominal phantom was equipped with a liver, different size cylindrical tumours, and a rod for tumour-liver distance variation. The geometric mean method with scatter and attenuation corrections was used for image processing. Scatter and attenuation corrections were made using the triple energy window scatter correction technique and a printed transmission sheet source, respectively. Region definitions for tumour activity distribution compensated for the partial volume effect (PVE). Activity measured in the dose calibrator served as reference for determining quantification accuracy. ResultsThe smallest tumour had the largest percentage deviation with an average activity underestimation of 34.6±1.2%. Activity values for the largest tumour were overestimated by 3.1±3.0%. PVE compensation improved quantification accuracy for all tumour sizes yielding accuracies of <12.4%. Scatter contribution to the tumours from the liver had minimal effect on quantification accuracy at tumour-liver distances >3cm. With PVE compensation, increased tumour-background ratio resulted in a percentage increase of up to 26.3%. ConclusionWhen applying relevant corrections for scatter, attenuation and PVE without background activity, quantification accuracy of <13% was obtained. We demonstrated the successful implementation of a practical technique to obtain quantitative information from 123I planar images.

Medical media analytics via ranking and big learning: A multi-modality image-based disease severity prediction study

Medical media analytics receives vast popularity nowadays because of its effectiveness in improving the performance of diverse health-care applications. In this study, the essential disease severity prediction problem in medical media analytics is investigated and a computer-aided diagnosis (CAD) strategy based on ranking and learning techniques is presented to tackle the disease severity prediction task. To be specific, two types of magnetic resonance images (MRI), including T1-weighted images as anatomic MRI and arterial spin labeling (ASL) images as functional MRI, are incorporated as multi-modality images to provide image-based information for dementia disease severity prediction in this study. There are two main steps composed of the whole CAD strategy. First, the problem of partial volume effects (PVE) mainly caused by signal cross-contamination due to pixel heterogeneity and limited spatial resolution of ASL is focused. Conventional regression-based PVE correction methods are discussed and their inherent problems of blurring and brain details loss in correction results, which prevents the actual brain atrophy being revealed, are studied. A pixel-based PVE correction method, which only counts on single pixel information and formulates the PVE correction problem as a constrained optimization problem solved via the split-Bregman algorithm, is presented to solve the problem. Second, ranking and learning techniques are incorporated based on multi-modality images after performing PVE correction for dementia disease severity prediction. Technically, a conventional discrete position-based ranking evaluation measure is approximated and its surrogated continuous form is optimized via gradient ascend for ranking functions learning. A large database composed of multi-modality images acquired from 320 real patients is utilized for experimental evaluation. Extensive experiments and comprehensive statistical analysis are carried out to demonstrate the superiority of the introduced CAD strategy with comparison to several existing ones. Promising results are reported from the statistical perspective.

Impact of MRI-based Segmentation Artifacts on Amyloid- and FDG-PET Quantitation.

Magnet resonance image (MRI)-based segmentations are widely used for clinical brain research, especially in conjunction with positron-emission-tomography (PET). Although artifacts due to segmentation errors arise commonly, the impact of these artifacts on PET quantitation has not yet been investigated systematically. Therefore, the aim of this study was to assess the effect of segmentation errors on [(18)F]-AV45 and [(18)F]-FDG PET quantitation, with and without correction for partial volume effects (PVE). 119 subjects with both [(18)F]-AV45, and [(18)F]-FDG PET as well as T1-weighted MRI at baseline and at two-year follow-up were selected from the ADNI cohort, and their MRI brain images were segmented using PMOD 3.5. MRIs with segmentation artifacts were masked with the corresponding [(18)F]-FDG PET standard-uptake-value (SUV) images to elucidate and quantify the impact of artifacts on PET analyses for six defined volumes-of-interest (VOI). Artifact volumes were calculated for each VOI, together with error-[%] and root-mean-square-errors (RMSE) in uncorrected and PVE corrected SUV results for the two PET tracers. We also assessed the bias in longitudinal PET data. Artifacts occurred most frequently in the parietal cortex VOI. For [(18)F]-AV45 and [(18)F]-FDG PET, the percentage-errors were dependent on artifact volumes. PVEC SUVs were consequently more distorted than were their uncorrected counterparts. In static and longitudinal assessment, a small subgroup of subjects with large artifacts (≥1500 voxels; ≙5.06 cm³) accounted for much of the PET quantitation bias. Large segmentation artifacts need to be detected and resolved as they considerably bias PET quantitation, especially when PVEC is applied to PET data.

Quantitative Amyloid Imaging in Autosomal Dominant Alzheimer's Disease: Results from the DIAN Study Group.

Amyloid imaging plays an important role in the research and diagnosis of dementing disorders. Substantial variation in quantitative methods to measure brain amyloid burden exists in the field. The aim of this work is to investigate the impact of methodological variations to the quantification of amyloid burden using data from the Dominantly Inherited Alzheimer’s Network (DIAN), an autosomal dominant Alzheimer’s disease population. Cross-sectional and longitudinal [11C]-Pittsburgh Compound B (PiB) PET imaging data from the DIAN study were analyzed. Four candidate reference regions were investigated for estimation of brain amyloid burden. A regional spread function based technique was also investigated for the correction of partial volume effects. Cerebellar cortex, brain-stem, and white matter regions all had stable tracer retention during the course of disease. Partial volume correction consistently improves sensitivity to group differences and longitudinal changes over time. White matter referencing improved statistical power in the detecting longitudinal changes in relative tracer retention; however, the reason for this improvement is unclear and requires further investigation. Full dynamic acquisition and kinetic modeling improved statistical power although it may add cost and time. Several technical variations to amyloid burden quantification were examined in this study. Partial volume correction emerged as the strategy that most consistently improved statistical power for the detection of both longitudinal changes and across-group differences. For the autosomal dominant Alzheimer’s disease population with PiB imaging, utilizing brainstem as a reference region with partial volume correction may be optimal for current interventional trials. Further investigation of technical issues in quantitative amyloid imaging in different study populations using different amyloid imaging tracers is warranted.

Fully automated calculation of image-derived input function in simultaneous PET/MRI in a sheep model.

BackgroundObtaining the arterial input function (AIF) from image data in dynamic positron emission tomography (PET) examinations is a non-invasive alternative to arterial blood sampling. In simultaneous PET/magnetic resonance imaging (PET/MRI), high-resolution MRI angiographies can be used to define major arteries for correction of partial-volume effects (PVE) and point spread function (PSF) response in the PET data. The present study describes a fully automated method to obtain the image-derived input function (IDIF) in PET/MRI. Results are compared to those obtained by arterial blood sampling.MethodsTo segment the trunk of the major arteries in the neck, a high-resolution time-of-flight MRI angiography was postprocessed by a vessel-enhancement filter based on the inertia tensor. Together with the measured PSF of the PET subsystem, the arterial mask was used for geometrical deconvolution, yielding the time-resolved activity concentration averaged over a major artery. The method was compared to manual arterial blood sampling at the hind leg of 21 sheep (animal stroke model) during measurement of blood flow with O15-water. Absolute quantification of activity concentration was compared after bolus passage during steady state, i.e., between 2.5- and 5-min post injection. Cerebral blood flow (CBF) values from blood sampling and IDIF were also compared.ResultsThe cross-calibration factor obtained by comparing activity concentrations in blood samples and IDIF during steady state is 0.98 ± 0.10. In all examinations, the IDIF provided a much earlier and sharper bolus peak than in the time course of activity concentration obtained by arterial blood sampling. CBF using the IDIF was 22 % higher than CBF obtained by using the AIF yielded by blood sampling.ConclusionsThe small deviation between arterial blood sampling and IDIF during steady state indicates that correction of PVE and PSF is possible with the method presented. The differences in bolus dynamics and, hence, CBF values can be explained by the different sampling locations (hind leg vs. major neck arteries) with differences in delay/dispersion. It will be the topic of further work to test the method on humans with the perspective of replacing invasive blood sampling by an IDIF using simultaneous PET/MRI.

18F-FDG-PET partial volume effect correction using a modified recovery coefficient approach based on functional volume and local contrast: physical validation and clinical feasibility in oncology

2-deoxy-2-[18F]fluoro-D-glucose 18F-FDG uptake within tumors reflects the glucose consumption of malignant tumors, i.e., the tumor activity. Thus, 18F-FDG uptake measurements enable improved therapeutic monitoring of patients in chemo- or radiotherapy treatment through the detection of changes in tumor uptake via quantitative measurements of the lesion standard uptake values (SUVs) or activity concentrations. A major bias that affects positron emission tomography (PET) image quantitation is the partial volume effect (PVE), which most strongly affects the smallest structures due to the poor spatial resolution of PET. Thus, PVE corrections are important when 18F-FDG-PET images are used as a quantitative tool for monitoring responses to therapy. The aim of this paper was to propose a PVE correction based on a modified recovery coefficient method (termed FARCAS) that considers the functional volumes and local contrasts of lesions that are automatically determined using a semi-automatic iterative segmentation algorithm. The FARCAS method consists of establishing a set of calibration curves based on the mathematical fitting of the RC values as a function of the automatically determined functional lesion volume and local lesion contrast. We set up our method using data from a cylindrical phantom that included spheres of different volumes (range: 0.43 to 97.8 mL) and contrasts (range: 1.7 to 22.9), and we assessed the method using both cylindrical and anthropomorphic phantom data that also included spheres of different volumes and contrasts. FARCAS was also compared with conventional RC methods that only considered the lesion functional volume, either automatically determined (RCVa) or using the ground truth volume (RCVgt). Finally, the clinical feasibility of FARCAS and its evaluation on tumor classification were also assessed on 24 NSCLC lesions. Whatever the phantom considered, for the spheres with contrast <5, FARCAS obtained comparable results to RCVgt and better than RCVa. For the spheres with contrast >5, FARCAS and RCVa were not statistically different, neither for the cylindrical and nor the anthropomorphic phantom. For the cylindrical phantom FARCAS yielded corrections that were not statistically different to those of RCVa for the smallest spheres (V<2 mL), but statistically superior for the larger spheres (V≥2 mL). RCVgt maintained a non-statistically superior accuracy. Regarding the anthropomorphic data, FARCAS was statistically more accurate than RCVa but not RCVgt. As main findings regarding the clinical data, FARCAS modified the classifications of five of 24 NSCLC lesions based on quantitative PERCIST criteria. The PVE correction proposed in this paper allows the accurate quantification of the PVE-corrected SUV, allowing also an automatic definition of the Metabolic Target Volume (MTV). Our results revealed that the PVE correction based on FARCAS is a better approach than conventional RC to significantly reduce the impact of PVE on lesion quantification, thus improving the evaluation of tumor response to treatment based on PET-CT images.

A novel disease severity prediction scheme via big pair-wise ranking and learning techniques using image-based personal clinical data

Disease severity prediction is essential in clinical diagnosis nowadays, as correct understandings of the onset and progression of disease are priceless in treatment planning. In this study, dementia disease, one of the most severe non-communicable diseases worldwide, is focused. A novel dementia disease severity prediction scheme is proposed using new big ranking and learning techniques. To be specific, arterial spin labeling, an emerging functional-magnetic resonance imaging technique, is adopted to provide image-based clinical data. There are two steps composed of the whole scheme. First, a single-pixel based method is presented to correct the partial volume effect in arterial spin labeling images. The advantage of this method is that, problems of blurring and brain detail loss can be well tackled. Second, novel big pair-wise ranking and learning techniques is proposed to realize the dementia disease severity prediction task using arterial spin labeling images after partial volume effects correction. Extensive experiments using a big database composed of images acquired from 350 real demented patients are carried out with several conventional methods being compared. Comprehensive statistical analysis is performed and it suggests that the new scheme is promising in dementia disease severity prediction.

Cerebral Hypoperfusion and Hypometabolism Detected by Arterial Spin Labeling MRI and FDG-PET in Early-Onset Alzheimer's Disease.

Early-onset Alzheimer's disease (EOAD) is frequently associated with atypical clinical presentations and its early detection remains a challenging issue. In this study, we used arterial spin labeling (ASL), a noninvasive perfusion MRI sequence, and [(18)F]-FDG-PET to detect the perfusion and metabolic features in patients with EOAD. All patients were investigated in the French reference center for young-onset dementia and were assessed by MRI, including a pseudo-continuous ASL (pCASL) sequence, and [(18)F]-FDG-PET. Quantitative analyses and intermodality comparison with correlation analysis were made after data processing including correction of partial volume effects, cortical projection, and specific intensity normalization. We prospectively included 37 patients with EOAD with a mean age of 58.3 years. The areas of most severe hypoperfusion detected with ASL were located in the parietal lobes, the precuneus, the right posterior cingulate cortex, and the frontal lobes (P < .05). The areas of lowest glucose metabolism detected by [(18)F]-FDG-PET were identified in the temporoparietal cortex and the precuneus (P < .05). Hypometabolic regions were more extensive than hypoperfused regions on ASL maps whereas ASL highlighted alterations in the frontal lobes without apparent hypometabolism on [(18)F]-FDG-PET maps. ASL and [(18)F]-FDG-PET detected pathological areas of similar distribution mainly located in the inferior parietal lobules and local zones in the temporal cortex in patients with EOAD. Our preliminary study showed that ASL and [(18)F]-FDG-PET may have a complementary role in combination with structural MRI for the assessment of suspected EOAD.

Reproducibility of multiphase pseudo-continuous arterial spin labeling and the effect of post-processing analysis methods

Arterial spin labeling (ASL) is an emerging MRI technique for non-invasive measurement of cerebral blood flow (CBF). Compared to invasive perfusion imaging modalities, ASL suffers from low sensitivity due to poor signal-to-noise ratio (SNR), susceptibility to motion artifacts and low spatial resolution, all of which limit its reliability. In this work, the effects of various state of the art image processing techniques for addressing these ASL limitations are investigated. A processing pipeline consisting of motion correction, ASL motion correction imprecision removal, temporal and spatial filtering, partial volume effect correction, and CBF quantification was developed and assessed. To further improve the SNR for pseudo-continuous ASL (PCASL) by accounting for errors in tagging efficiency, the data from multiphase (MP) acquisitions were analyzed using a novel weighted-averaging scheme. The performances of each step in terms of SNR and reproducibility were evaluated using test–retest ASL data acquired from 12 young healthy subjects. The proposed processing pipeline was shown to improve the within-subject coefficient of variation and regional reproducibility by 17% and 16%, respectively, compared to CBF maps computed following motion correction but without the other processing steps. The CBF measurements of MP-PCASL compared to PCASL had on average 23% and 10% higher SNR and reproducibility, respectively.

Establishment of an open database of realistic simulated data for evaluation of partial volume correction techniques in brain PET/MR.

The Partial Volume (PV) effect in Positron Emission Tomography (PET) imaging leads to loss in quantification accuracy, which manifests in PV effects (small objects occupy partially the sensitive volume of the imaging instrument, resulting in blurred images). Simultaneous acquisition of PET and Magnetic Resonance Imaging (MRI) produces concurrent metabolic and anatomical information. The latter has proved to be very helpful for the correction of PV effects. Currently, there are several techniques used for PV correction. They can be applied directly during the reconstruction process or as a post-processing step after image reconstruction. In order to evaluate the efficacy of the different PV correction techniques in brain- PET, we are constructing a database of simulated data. Here we present the framework and steps involved in constructing this database. Static 18F-FDG epilepsy and 18F-Florbetapir amyloid dementia PET/MR were selected because of their very different characteristics. The methodology followed was based on four main steps: Image pre-processing, Ground Truth (GT) generation, MRI and PET data simulation and reconstruction. All steps used Open Source software and can therefore be repeated at any centre. The framework as well as the database will be freely accessible. Tools used included GIF, FSL, POSSUM, GATE and STIR. The final data obtained after simulation, involving raw or reconstructed PET data together with corresponding MRI datasets, were close to the original patient data. Besides, there is the advantage that data can be compared with the GT. We indicate several parameters that can be improved and optimized.

Quantifying [18F]fluorodeoxyglucose uptake in the arterial wall: the effects of dual time-point imaging and partial volume effect correction

The human arterial wall is smaller than the spatial resolution of current positron emission tomographs. Therefore, partial volume effects should be considered when quantifying arterial wall (18)F-FDG uptake. We evaluated the impact of a novel method for partial volume effect (PVE) correction with contrast-enhanced CT (CECT) assistance on quantification of arterial wall (18)F-FDG uptake at different imaging time-points. Ten subjects were assessed by CECT imaging and dual time-point PET/CT imaging at approximately 60 and 180 min after (18)F-FDG administration. For both time-points, uptake of (18)F-FDG was determined in the aortic wall by calculating the blood pool-corrected maximum standardized uptake value (cSUVMAX) and cSUVMEAN. The PVE-corrected SUVMEAN (pvcSUVMEAN) was also calculated using (18)F-FDG PET/CT and CECT images. Finally, corresponding target-to-background ratios (TBR) were calculated. At 60 min, pvcSUVMEAN was on average 3.1 times greater than cSUVMAX (P < .0001) and 8.5 times greater than cSUVMEAN (P < .0001). At 180 min, pvcSUVMEAN was on average 2.6 times greater than cSUVMAX (P < .0001) and 6.6 times greater than cSUVMEAN (P < .0001). This study demonstrated that CECT-assisted PVE correction significantly influences quantification of arterial wall (18)F-FDG uptake. Therefore, partial volume effects should be considered when quantifying arterial wall (18)F-FDG uptake with PET.

Differences in 3D dose distributions due to calculation method of voxel S-values and the influence of image blurring in SPECT

This study compares 3D dose distributions obtained with voxel S values (VSVs) for soft tissue, calculated by several methods at their current state-of-the-art, varying the degree of image blurring. The methods were: 1) convolution of Dose Point Kernel (DPK) for water, using a scaling factor method; 2) an analytical model (AM), fitting the deposited energy as a function of the source-target distance; 3) a rescaling method (RSM) based on a set of high-resolution VSVs for each isotope; 4) local energy deposition (LED). VSVs calculated by direct Monte Carlo simulations were assumed as reference. Dose distributions were calculated considering spheroidal clusters with various sizes (251, 1237 and 4139 voxels of 3 mm size), uniformly filled with 131I, 177Lu, 188Re or 90Y. The activity distributions were blurred with Gaussian filters of various widths (6, 8 and 12 mm). Moreover, 3D-dosimetry was performed for 10 treatments with 90Y derivatives. Cumulative Dose Volume Histograms (cDVHs) were compared, studying the differences in D95%, D50% or Dmax (ΔD95%, ΔD50% and ΔDmax) and dose profiles.For unblurred spheroidal clusters, ΔD95%, ΔD50% and ΔDmax were mostly within some percents, slightly higher for 177Lu with DPK (8%) and RSM (12%) and considerably higher for LED (ΔD95% up to 59%). Increasing the blurring, differences decreased and also LED yielded very similar results, but D95% and D50% underestimations between 30–60% and 15–50%, respectively (with respect to 3D-dosimetry with unblurred distributions), were evidenced. Also for clinical images (affected by blurring as well), cDVHs differences for most methods were within few percents, except for slightly higher differences with LED, and almost systematic for dose profiles with DPK (−1.2%), AM (−3.0%) and RSM (4.5%), whereas showed an oscillating trend with LED.The major concern for 3D-dosimetry on clinical SPECT images is more strongly represented by image blurring than by differences among the VSVs calculation methods. For volume sizes about 2-fold the spatial resolution, D95% and D50% underestimations up to about 60 and 50% could result, so the usefulness of 3D-dosimetry is highly questionable for small tumors, unless adequate corrections for partial volume effects are adopted.

A novel partial volume effects correction technique integrating deconvolution associated with denoising within an iterative PET image reconstruction.

Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy-Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction. The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy-Richardson deconvolution algorithm to the current estimation of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases. Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery. This study demonstrates the feasibility of incorporating the Lucy-Richardson deconvolution associated with a wavelet-based denoising in the reconstruction process to better correct for PVE. Future work includes further evaluations of the proposed method on clinical datasets and the use of improved PSF models.

The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment - Beyond classical regression.

Selecting a set of relevant markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) has become a challenging task given the wealth of regional pathologic information that can be extracted from multimodal imaging data.Here, we used regularized regression approaches with an elastic net penalty for best subset selection of multiregional information from AV45-PET, FDG-PET and volumetric MRI data to predict conversion from MCI to AD. The study sample consisted of 127 MCI subjects from ADNI-2 who had a clinical follow-up between 6 and 31 months. Additional analyses assessed the effect of partial volume correction on predictive performance of AV45- and FDG-PET data.Predictor variables were highly collinear within and across imaging modalities. Penalized Cox regression yielded more parsimonious prediction models compared to unpenalized Cox regression. Within single modalities, time to conversion was best predicted by increased AV45-PET signal in posterior medial and lateral cortical regions, decreased FDG-PET signal in medial temporal and temporobasal regions, and reduced gray matter volume in medial, basal, and lateral temporal regions. Logistic regression models reached up to 72% cross-validated accuracy for prediction of conversion status, which was comparable to cross-validated accuracy of non-linear support vector machine classification. Regularized regression outperformed unpenalized stepwise regression when number of parameters approached or exceeded the number of training cases. Partial volume correction had a negative effect on the predictive performance of AV45-PET, but slightly improved the predictive value of FDG-PET data.Penalized regression yielded more parsimonious models than unpenalized stepwise regression for the integration of multiregional and multimodal imaging information. The advantage of penalized regression was particularly strong with a high number of collinear predictors.

Partial-volume effect correction in positron emission tomography brain scan image using super-resolution image reconstruction.

The partial-volume effect (PVE) is a consequence of limited (i.e. finite) spatial resolution. PVE can lead to quantitative underestimation of activity concentrations in reconstructed images, which may result in misinterpretation of positron emission tomography (PET) scan images, especially in the brain. The PVE becomes significant when the dimensions of a source region are less than two to three times the full width at half maximum spatial resolution of the imaging system. In the present study, the ability of super-resolution (SR) image reconstruction to compensate for PVE in PET was characterized. The ability of SR image reconstruction technique to recover activity concentrations in small structures was evaluated by comparing images before and after image reconstruction in the NEMA/IEC phantom (Washington, DC), in the Hoffman brain phantom and in four human brain subjects (three normal subjects and one atrophic brain subject) in terms of apparent recovery coefficient (ARC) and percentage yield. Both the ARC and percentage yield are improved after SR implementation in NEMA/IEC phantom and Hoffman brain phantom. When tested in normal subjects, SR implementation can improve the intensity and justify SR efficiency to correct PVE. SR algorithm can be used to effectively correct PVE in PET images. The current research focused on brain PET scanning exclusively; future work will extend to whole-body imaging.