Arterial input function estimation compensating for inflow and partial voluming in dynamic contrast-enhanced MRI.

Both inflow and the partial volume effect (PVE) are sources of error when measuring the arterial input function (AIF) in dynamic contrast-enhanced (DCE) MRI. This is relevant, as errors in the AIF can propagate into pharmacokinetic parameter estimations from the DCE data. A method was introduced for flow correction by estimating and compensating the number of the perceived pulse of spins during inflow. We hypothesized that the PVE has an impact on concentration-time curves similar to inflow. Therefore, we aimed to study the efficiency of this method to compensate for both effects simultaneously. We first simulated an AIF with different levels of inflow and PVE contamination. The peak, full width at half-maximum (FWHM), and area under curve (AUC) of the reconstructed AIFs were compared with the true (simulated) AIF. In clinical data, the PVE was included in AIFs artificially by averaging the signal in voxels surrounding a manually selected point in an artery. Subsequently, the artificial partial volume AIFs were corrected and compared with the AIF from the selected point. Additionally, corrected AIFs from the internal carotid artery (ICA), the middle cerebral artery (MCA), and the venous output function (VOF) estimated from the superior sagittal sinus (SSS) were compared. As such, we aimed to investigate the effectiveness of the correction method with different levels of inflow and PVE in clinical data. The simulation data demonstrated that the corrected AIFs had only marginal bias in peak value, FWHM, and AUC. Also, the algorithm yielded highly correlated reconstructed curves over increasingly larger neighbourhoods surrounding selected arterial points in clinical data. Furthermore, AIFs measured from the ICA and MCA produced similar peak height and FWHM, whereas a significantly larger peak and lower FWHM was found compared with the VOF. Our findings indicate that the proposed method has high potential to compensate for PVE and inflow simultaneously. The corrected AIFs could thereby provide a stable input source for DCE analysis.

A CNN-based denoising method trained with images acquired with electron density phantoms for thin-sliced coronary artery calcium scans.

PURPOSE: This work proposed a convolutional neural network (CNN)-based method trained with images acquired with electron density phantoms to reduce quantum noise for coronary artery calcium (CAC) scans reconstructed with slice thickness less than 3 mm. METHODS: A DenseNet model was used to estimate quantum noise for CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5 mm. Training data was acquired using electron density phantoms in three different sizes. The label images of the CNN model were real noise maps, while the input images of the CNN model were pseudo noise maps. Image denoising was conducted by subtracting the CNN output images from thin-sliced CAC scans. The efficacy of the proposed method was verified through both phantom study and patient study. RESULTS: By means of phantom study, the proposed method was proven effective in reducing quantum noise in CAC scans reconstructed with 1.5-mm slice thickness without causing significant texture change or variation in HU values. With regard to patient study, calcifications were more clear on the denoised CAC scans reconstructed with slice thickness of 0.5, 1.0 and 1.5 mm than on 3-mm slice images, while over-smooth changes were not observed in the denoised CAC scans reconstructed with 1.5-mm slice thickness. CONCLUSION: Our results demonstrated that the electron density phantoms can be used to generate training data for the proposed CNN-based denoising method to reduce quantum noise for CAC scans reconstructed with 1.5-mm slice thickness. Because anthropomorphic phantom is not a necessity, our method could make image denoising more practical in routine clinical practice.

A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information.

PURPOSE: Partial volume effect (PVE) is a consequence of the limited spatial resolution of PET scanners. PVE can cause the intensity values of a particular voxel to be underestimated or overestimated due to the effect of surrounding tracer uptake. We propose a novel partial volume correction (PVC) technique to overcome the adverse effects of PVE on PET images. METHODS: Two hundred and twelve clinical brain PET scans, including 50 18F-Fluorodeoxyglucose (18F-FDG), 50 18F-Flortaucipir, 36 18F-Flutemetamol, and 76 18F-FluoroDOPA, and their corresponding T1-weighted MR images were enrolled in this study. The Iterative Yang technique was used for PVC as a reference or surrogate of the ground truth for evaluation. A cycle-consistent adversarial network (CycleGAN) was trained to directly map non-PVC PET images to PVC PET images. Quantitative analysis using various metrics, including structural similarity index (SSIM), root mean squared error (RMSE), and peak signal-to-noise ratio (PSNR), was performed. Furthermore, voxel-wise and region-wise-based correlations of activity concentration between the predicted and reference images were evaluated through joint histogram and Bland and Altman analysis. In addition, radiomic analysis was performed by calculating 20 radiomic features within 83 brain regions. Finally, a voxel-wise two-sample t-test was used to compare the predicted PVC PET images with reference PVC images for each radiotracer. RESULTS: The Bland and Altman analysis showed the largest and smallest variance for 18F-FDG (95% CI: - 0.29, + 0.33 SUV, mean = 0.02 SUV) and 18F-Flutemetamol (95% CI: - 0.26, + 0.24 SUV, mean = - 0.01 SUV), respectively. The PSNR was lowest (29.64 ± 1.13 dB) for 18F-FDG and highest (36.01 ± 3.26 dB) for 18F-Flutemetamol. The smallest and largest SSIM were achieved for 18F-FDG (0.93 ± 0.01) and 18F-Flutemetamol (0.97 ± 0.01), respectively. The average relative error for the kurtosis radiomic feature was 3.32%, 9.39%, 4.17%, and 4.55%, while it was 4.74%, 8.80%, 7.27%, and 6.81% for NGLDM_contrast feature for 18F-Flutemetamol, 18F-FluoroDOPA, 18F-FDG, and 18F-Flortaucipir, respectively. CONCLUSION: An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required.

[Fiber direction estimation using constrained spherical deconvolution based on multi-model response function].

Constrained spherical deconvolution can quantify white matter fiber orientation distribution information from diffusion magnetic resonance imaging data. But this method is only applicable to single shell diffusion magnetic resonance imaging data and will provide wrong fiber orientation information in white matter tissue which contains isotropic diffusion signals. To solve these problems, this paper proposes a constrained spherical deconvolution method based on multi-model response function. Multi-shell data can improve the stability of fiber orientation, and multi-model response function can attenuate isotropic diffusion signals in white matter, providing more accurate fiber orientation information. Synthetic data and real brain data from public database were used to verify the effectiveness of this algorithm. The results demonstrate that the proposed algorithm can attenuate isotropic diffusion signals in white matter and overcome the influence of partial volume effect on fiber direction estimation, thus estimate fiber direction more accurately. The reconstructed fiber direction distribution is stable, the false peaks are less, and the recognition ability of cross fiber is stronger, which lays a foundation for the further research of fiber bundle tracking technology.

Partial volume correction in arterial spin labeling perfusion MRI: A method to disentangle anatomy from physiology or an analysis step too far?

The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent. In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption. We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, 'uncorrected', images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods.

Analysis of right ventricular mass from magnetic resonance imaging data: a simple post-processing algorithm for correction of partial-volume effects.

Magnetic resonance imaging (MRI) of the right ventricle (RV) offers important diagnostic information, but the accuracy of this information is hampered by the complex geometry of the RV. Here, we propose a novel postprocessing algorithm that corrects for partial-volume effects in the analysis of standard MRI cine images of RV mass (RVm) and evaluate the method in clinical and preclinical data. Self-corrected RVm measurement was compared with conventionally measured RVm in 16 patients who showed different clinical indications for cardiac MRI and in 17 Wistar rats with different degrees of pulmonary congestion. The rats were studied under isoflurane anaesthesia. To evaluate the reliability of the proposed method, the measured end-systolic and end-diastolic RVm were compared. Accuracy was evaluated by comparing preclinical RVm to ex vivo RV weight (RVw). We found that use of the self-correcting algorithm improved reliability compared with conventional segmentation. For clinical data, the limits of agreement (LOAs) were -1.8 ± 8.6g (self-correcting) vs. 5.8 ± 7.8g (conventional), and coefficients of variation (CoVs) were 7.0% (self-correcting) vs. 14.3% (conventional). For preclinical data, LOAs were 21 ± 46 mg (self-correcting) vs. 64 ± 89 mg (conventional), and CoVs were 9.0% (self-correcting) and 17.4% (conventional). Self-corrected RVm also showed better correspondence with the ex vivo RVw: LOAs were -5 ± 80 mg (self-correcting) vs. 94 ± 116 mg (conventional) in end-diastole and -26 ± 74 mg (self-correcting) vs. 31 ± 98 mg (conventional) in end-systole. The new self-correcting algorithm improves the reliability and accuracy of RVm measurements in both clinical and preclinical MRI. It is simple and easy to implement and does not require any additional MRI data.NEW & NOTEWORTHY Magnetic resonance imaging (MRI) of the right ventricle (RV) offers important diagnostic information, but the accuracy of this information is hampered by the complex geometry of the RV. In particular, the crescent shape of the RV renders it particularly vulnerable to partial-volume effects. We present a new, simple, self-correcting algorithm that can be applied to correct partial-volume effects in MRI-based RV mass estimation. The self-correcting algorithm offers improved reliability and accuracy compared with the conventional approach.

Graph theory analysis of the dopamine D2 receptor network in Parkinson's disease patients with cognitive decline.

Cognitive decline in Parkinson's disease (PD) is a common sequela of the disorder that has a large impact on patient well-being. Its physiological etiology, however, remains elusive. Our study used graph theory analysis to investigate the large-scale topological patterns of the extrastriatal dopamine D2 receptor network. We used positron emission tomography with [11 C]FLB-457 to measure the binding potential of cortical dopamine D2 receptors in two networks: the meso-cortical dopamine network and the meso-limbic dopamine network. We also investigated the application of partial volume effect correction (PVEC) in conjunction with graph theory analysis. Three groups were investigated in this study divided according to their cognitive status as measured by the Montreal Cognitive Assessment score, with a score ≤25 considered cognitively impaired: (a) healthy controls (n = 13, 11 female), (b) cognitively unimpaired PD patients (PD-CU, n = 13, 5 female), and (c) PD patients with mild cognitive impairment (PD-MCI, n = 17, 4 female). In the meso-cortical network, we observed increased small-worldness, normalized clustering, and local efficiency in the PD-CU group compared to the PD-MCI group, as well as a hub shift in the PD-MCI group. Compensatory reorganization of the meso-cortical dopamine D2 receptor network may be responsible for some of the cognitive preservation observed in PD-CU. These results were found without PVEC applied and PVEC proved detrimental to the graph theory analysis. Overall, our findings demonstrate how graph theory analysis can be used to detect subtle changes in the brain that would otherwise be missed by regional comparisons of receptor density.

Cortical parameters predict bone strength at the tibial diaphysis, but are underestimated by HR-pQCT and µCT compared to histomorphometry.

Cortical bone and its microstructure are crucial for bone strength, especially at the long bone diaphysis. However, it is still not well-defined how imaging procedures can be used as predictive tools for mechanical bone properties. This study evaluated the capability of several high-resolution imaging techniques to capture cortical bone morphology and assessed the correlation with the bone's mechanical properties. The microstructural properties (cortical thickness [Ct.Th], porosity [Ct.Po], area [Ct.Ar]) of 11 female tibial diaphysis (40-90 years) were evaluated by dual-energy X-ray absorptiometry (DXA), high-resolution peripheral-quantitative-computed-tomography (HR-pQCT), micro-CT (µCT) and histomorphometry. Stiffness and maximal torque to failure were determined by mechanical testing. T-Scores determined by DXA ranged from 0.6 to -5.6 and a lower T-Score was associated with a decrease in Ct.Th (p ≤ 0.001) while the Ct.Po (p ≤ 0.007) increased, and this relationship was independent of the imaging method. With decreasing T-Score, histology showed an increase in Ct.Po from the endosteal to the periosteal side (p = 0.001) and an exponential increase in the ratio of osteons at rest to those after remodelling. However, compared to histomorphometry, HR-pQCT and µCT underestimated Ct.Po and Ct.Th. A lower T-Score was also associated with significantly reduced stiffness (p = 0.031) and maximal torque (p = 0.006). Improving the accuracy of Ct.Po and Ct.Th did not improve prediction of the mechanical properties, which was most closely related to geometry (Ct.Ar). The ex-vivo evaluation of mechanical properties correlated with all imaging modalities, with Ct.Th and Ct.Po highly correlated with the T-Score of the tibial diaphysis. Cortical microstructural changes were underestimated with the lower resolution of HR-pQCT and µCT compared to the histological 'gold standard'. The increased accuracy did not result in an improved prediction for local bone strength in this study, which however might be related to the limited number of specimens and thus needs to be evaluated in a larger collective.

Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting.

PURPOSE: To develop an efficient algorithm for multi-component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties. METHODS: Different tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non-negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi-component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi-component analysis of MRF data. A new algorithm combining joint sparsity and non-negativity constraints is proposed and compared to state-of-the-art multi-component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers. RESULTS: Simulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi-component MRF publications. CONCLUSIONS: The proposed algorithm is faster than previously proposed methods for multi-component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel-wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi-component MRF.

Dedicated diffusion phantoms for the investigation of free water elimination and mapping: insights into the influence of T2 relaxation properties.

Conventional diffusion-weighted (DW) MRI suffers from free water contamination due to the finite voxel size. The most common case of free water contamination occurs with cerebrospinal fluid (CSF) in voxels located at the CSF-tissue interface, such as at the ventricles in the human brain. Another case refers to intra-tissue free water as in vasogenic oedema. In order to avoid the bias in diffusion metrics, several multi-compartment methods have been introduced, which explicitly model the presence of a free water compartment. However, fitting multi-compartment models in DW MRI represents a well known ill conditioned problem. Although during the last decade great effort has been devoted to mitigating this estimation problem, the research field remains active. The aim of this work is to introduce the design, characterise the NMR properties and demonstrate the use of two dedicated anisotropic diffusion fibre phantoms, useful for the study of free water elimination (FWE) and mapping models. In particular, we investigate the recently proposed FWE diffusion tensor imaging approach, which takes explicit account of differences in the transverse relaxation times between the free water and tissue compartments.

Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation.

Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.

Black blood myocardial T2 mapping.

PURPOSE: To develop a black blood heart-rate adaptive T2 -prepared balanced steady-state free-precession (BEATS) sequence for myocardial T2 mapping. METHODS: In BEATS, blood suppression is achieved by using a combination of preexcitation and double inversion recovery pulses. The timing and flip angles of the preexcitation pulse are auto-calculated in each patient based on heart rate. Numerical simulations, phantom studies, and in vivo studies were conducted to evaluate the performance of BEATS. BEATS T2 maps were acquired in 36 patients referred for clinical cardiac MRI and in 1 swine with recent myocardial infarction. Two readers assessed all images acquired in patients to identify the presence of artifacts associated with slow blood flow. RESULTS: Phantom experiments showed that the BEATS sequence provided accurate T2 values over a wide range of simulated heart rates. Black blood myocardial T2 maps were successfully obtained in all subjects. No significant difference was found between the average T2 measurements obtained from the BEATS and conventional bright-blood T2 ; however, there was a decrease in precision using the BEATS sequence. A suppression of the blood pool resulted in sharper definition of the blood-myocardium border and reduced partial voluming effect. The subjective assessment showed that 16% (18 out of 108) of short-axis slices have residual blood artifacts (12 in the apical slice, 4 in the midventricular slice, and 2 in the basal slice). CONCLUSION: The BEATS sequence yields dark blood myocardial T2 maps with better definition of the blood-myocardium border. Further studies are warranted to evaluate diagnostic accuracy of black blood T2 mapping.

Optimization of phase-contrast MRI for the estimation of global cerebral blood flow of mice at 11.7T.

PURPOSE: To optimize phase-contrast (PC) MRI for the measurement of global cerebral blood flow (CBF) in the mouse at 11.7T. METHODS: We determined proper velocity encoding (VENC) for internal carotid arteries (ICAs) and vertebral arteries (VAs). Next, we optimized spatial resolution of the sequence. To shorten scan time without compromising data quality, we further optimized repetition time and developed a reduced field-of-view (FOV) scheme for ICA and VA PC MRI. Whole-brain volume was determined with T2 -weighted image to obtain unit-volume CBF. RESULTS: Peak flow velocities were 13.8 ± 1.7, 14.4 ± 0.6, 6.5 ± 1.7, and 6.7 ± 1.3 cm/s for left ICA, right ICA, left VA, and right VA, respectively. Thus, VENC values of 20 and 10 cm/s were chosen for ICA and VA PC MRI, respectively. An in-plane spatial resolution of 50 × 50 µm2 was found to provide a reasonable trade-off between reducing partial-volume effects and maintaining signal-to-noise ratio. Because of the fact that saturated spins in the imaging slice are rapidly replaced by fresh spins, TR of the sequence can be decreased to as short as 15 ms without reducing signal intensity, thereby substantially lowering scan time. Moreover, reduced FOV along the phase-encoding direction was able to shorten scan time by 33.3% while maintaining measurement accuracy. With these optimizations, it took 96 seconds to evaluate CBF with a test-retest variability of approximately 5% and an inter-rater correlation of >0.95. Global unit-volume CBF was found to be 279.5 ± 11.1 mL of blood/100 ml of tissue/min. CONCLUSION: We have optimized PC MRI for noninvasive quantification of blood flow in mice at 11.7T.

Futility of attempts to detect and quantify beta cells by PET imaging in the pancreas: why it is time to abandon the approach.

In this commentary, we describe the limitations of positron emission tomography (PET) in visualising and characterising beta cell mass in the native pancreas in healthy individuals and those diagnosed with diabetes. Imaging with PET requires a large mass of targeted cells or other structures in the range of approximately 8-10 cm3. Since islets occupy only 1% of the pancreatic volume and are dispersed throughout the organ, it is our view that uptake of PET tracers, including [18F]fluoropropyl-(+)-dihydrotetrabenazine, in islets cannot be successfully detected by current imaging modalities. Therefore, we dispute the feasibility of PET imaging for the detection of loss of beta cells in the native pancreas in individuals with diabetes. However, we believe this novel approach can be successfully employed to visualise beta cell mass in individuals with hyperinsulinism and transplanted islets.

Resolution considerations in imaging of the cortical layers.

The cortical layers are a finger print of brain development, function, connectivity and pathology. Obviously, the formation of the layers and their composition is essential to cognition and behavior. The layers were traditionally measured by histological means but recent studies utilizing MRI suggested that T1 relaxation imaging consist of enough contrast to separate the layers. Indeed extreme resolution, post mortem, studies demonstrated this phenomenon. Yet, one of the limiting factors of using T1 MRI to visualize the layers in neuroimaging research is partial volume effect. This happen when the image resolution is not high enough and two or more layers resides within the same voxel. In this paper we demonstrate that due to the physical small thickness of the layers it is highly unlikely that high resolution imaging could resolve the layers. By contrast, we suggest that low resolution multi T1 mapping conjugate with composition analysis could provide practical means for measuring the T1 layers. We suggest an acquisition platform that is clinically feasible and could quantify measures of the layers. The key feature of the suggested platform is that separation of the layers is better achieved in the T1 relaxation domain rather than in the spatial image domain.

A linear mixed perfusion model for tissue partial volume correction of perfusion estimates in dynamic susceptibility contrast MRI: Impact on absolute quantification, repeatability, and agreement with pseudo-continuous arterial spin labeling.

PURPOSE: The partial volume effect (PVE) is an important source of bias in brain perfusion measurements. The impact of tissue PVEs in perfusion measurements with dynamic susceptibility contrast MRI (DSC-MRI) has not yet been well established. The purpose of this study was to suggest a partial volume correction (PVC) approach for DSC-MRI and to study how PVC affects DSC-MRI perfusion results. METHODS: A linear mixed perfusion model for DSC-MRI was derived and evaluated by way of simulations. Twenty healthy volunteers were scanned twice, including DSC-MRI, arterial spin labeling (ASL), and partial volume measurements. Two different algorithms for PVC were employed and assessed. RESULTS: Simulations showed that the derived model had a tendency to overestimate perfusion values in voxels with high fractions of cerebrospinal fluid. PVC reduced the tissue volume dependence of DSC-MRI perfusion values from 44.4% to 4.2% in gray matter and from 55.3% to 14.2% in white matter. One PVC method significantly improved the voxel-wise repeatability, but PVC did not improve the spatial agreement between DSC-MRI and ASL perfusion maps. CONCLUSION: Significant PVEs were found for DSC-MRI perfusion estimates, and PVC successfully reduced those effects. The findings suggest that PVC might be an important consideration for DSC-MRI applications. Magn Reson Med 77:2203-2214, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Impact of partial-volume correction in oncological PET studies: a systematic review and meta-analysis.

PURPOSE: Positron-emission tomography can be useful in oncology for diagnosis, (re)staging, determining prognosis, and response assessment. However, partial-volume effects hamper accurate quantification of lesions <2-3× the PET system's spatial resolution, and the clinical impact of this is not evident. This systematic review provides an up-to-date overview of studies investigating the impact of partial-volume correction (PVC) in oncological PET studies. METHODS: We searched in PubMed and Embase databases according to the PRISMA statement, including studies from inception till May 9, 2016. Two reviewers independently screened all abstracts and eligible full-text articles and performed quality assessment according to QUADAS-2 and QUIPS criteria. For a set of similar diagnostic studies, we statistically pooled the results using bivariate meta-regression. RESULTS: Thirty-one studies were eligible for inclusion. Overall, study quality was good. For diagnosis and nodal staging, PVC yielded a strong trend of increased sensitivity at expense of specificity. Meta-analysis of six studies investigating diagnosis of pulmonary nodules (679 lesions) showed no significant change in diagnostic accuracy after PVC (p = 0.222). Prognostication was not improved for non-small cell lung cancer and esophageal cancer, whereas it did improve for head and neck cancer. Response assessment was not improved by PVC for (locally advanced) breast cancer or rectal cancer, and it worsened in metastatic colorectal cancer. CONCLUSIONS: The accumulated evidence to date does not support routine application of PVC in standard clinical PET practice. Consensus on the preferred PVC methodology in oncological PET should be reached. Partial-volume-corrected data should be used as adjuncts to, but not yet replacement for, uncorrected data.

Electronic cleansing in computed tomography colonography using AT layer identification with integration of gradient directional second derivative and material fraction model.

BACKGROUND: In computed tomography colonography images, electronic cleansing (EC) is applied to remove opacified residual materials, called fecal-tagging materials (FTM), using positive-contrast tagging agents and laxative to facilitate polyp detection. METHODS: The proposed EC, EC prop , integrates the gradient directional second derivative into material fraction model to preserve submerged soft tissue (ST) under FTM. Three-material fraction model is used to remove FTM and artifacts at air-tagging (AT) layers and T-junctions where air, ST, and FTM material meet simultaneously. Moreover, the proposed AT layer identification is used to distinguish AT layers from air-tissue-tagging (ATT) layers in order to preserve ATT layers during cleansing. The clinical evaluation on 467 3-Dimensional band view images was conducted by the abdominal radiologist using four grading levels of cleansing quality with five causes of low quality EC. The amount of the remaining artifacts at T-junctions was approximated from the results of EC prop . The results from EC prop were compared with the results from syngo.via Client 3.0 Software, EC syngo , and the fast three-material modeling, EC prev , using the preference of the radiologist. Two-tailed paired Wilcoxon signed rank test is used to indicate statistical significance. RESULTS: The average grade on cleansing quality is 2.89 out of 4. The artifacts at T-junctions from 86.94% of the test images can be removed, whereas artifacts at T-junctions from only 13.06% of the test images cannot be removed. For 13.06% of the test images, the results from EC prop are more preferable to the results from EC syngo (p<0.008). For all the test images, the results from EC prop are more preferable to the results from EC prev (p<0.001). Finally, the visual assessment shows that EC prop can preserve ATT layers, submerged polyps and folds while EC prev can preserve only submerged folds but fails to preserve ATT layers. CONCLUSION: From our implementation, EC prop can improve the performance of the existing EC, such that it can preserve ST, especially ATT layers and remove the artifacts at T-junctions which have never been proposed by any other methods before.

Impact of spillover from white matter by partial volume effect on quantification of amyloid deposition with [11C]PiB PET.

High non-specific uptake of [11C]Pittsburgh compound B ([11C]PiB) in white matter and signal spillover from white matter, due to partial volume effects, confound radioactivity measured in positron emission tomography (PET) with [11C]PiB. We aimed to reveal the partial volume effect in absolute values of kinetic parameters for [11C]PiB, in terms of spillover from white matter. Dynamic data acquired in [11C]PiB PET scans with five healthy volunteers and eight patients with Alzheimer's disease were corrected with region-based and voxel-based partial volume corrections. Binding potential (BPND) was estimated using the two-tissue compartment model analysis with a plasma input function. Partial volume corrections significantly decreased cortical BPND values. The degree of decrease in healthy volunteers (-52.7±5.8%) was larger than that in Alzheimer's disease patients (-11.9±4.2%). The simulation demonstrated that white matter spillover signals due to the partial volume effect resulted in an overestimation of cortical BPND, with a greater degree of overestimation for lower BPND values. Thus, an overestimation due to partial volume effects is more severe in healthy volunteers than in Alzheimer's disease patients. Partial volume corrections may be useful for accurately quantifying Aß deposition in cortical regions.

Age-independent myocardial infarct quantification by signal intensity percent infarct mapping in swine.

PURPOSE: To test whether signal intensity percent infarct mapping (SI-PIM) accurately determines the size of myocardial infarct (MI) regardless of infarct age. MATERIALS AND METHODS: Forty-five swine with reperfused MI underwent 1.5T late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) after bolus injection of 0.2 mmol/kg Gd(DTPA) on days 2-62 following MI. Animals were classified into acute, healing, and healed groups by pathology. Infarct volume (IV) and infarct fraction (IF) were determined by two readers, using binary techniques (including 2-5 standard deviations [SD] above the remote, and full-width at half-maximum) and the SI-PIM method. Triphenyl-tetrazolium-chloride staining (TTC) was performed as reference. Bias (percent under/overestimation of IV relative to TTC) of each quantification method was calculated. Bland-Altman analysis was done to test the accuracy of the quantification methods, while intraclass correlation coefficient (ICC) analysis was done to assess intra- and interobserver agreement. RESULTS: Bias of the MRI quantification methods do not depend on the age of the MI. Full-width at half-maximum (FWHM) and SI-PIM gave the best estimate of MI volume determined by the reference TTC (P-values for the FWHM and SI-PIM methods were 0.183, 0.26, 0.95, and 0.073, 0.091, 0.73 in Group 1, Group 2, and Group 3, respectively), while using any of the binary thresholds of 2-4 SDs above the remote myocardium showed significant overestimation. The 5 SD method, however, provided similar IV compared to TTC and was shown to be independent of the size and age of MI. ICC analysis showed excellent inter- and intraobserver agreement between the readers. CONCLUSION: Our results indicate that the SI-PIM method can accurately determine MI volume regardless of the pathological stage of MI. Once tested, it may prove to be useful for the clinic.