Structural-functional lung imaging using a combined CT-EIT and a Discrete Cosine Transformation reconstruction method.
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ABSTRACT:
SUBMITTER: Schullcke B
PROVIDER: S-EPMC4867600 | biostudies-other | 2016
REPOSITORIES: biostudies-other
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Project description:BackgroundGallium-68 lung perfusion PET/CT is an emerging imaging modality for the assessment of regional lung function, especially to optimise radiotherapy (RT) planning. A key step of lung functional avoidance RT is the delineation of lung functional volumes (LFVs) to be integrated into radiation plans. However, there is currently no consistent and reproducible delineation method for LFVs. The aim of this study was to develop and evaluate an automated delineation threshold method based on total lung function for LFVs delineation with Gallium-68 MAA lung PET/CT imaging.Material and methodPatients prospectively enrolled in the PEGASUS trial-a pilot study assessing the feasibility of lung functional avoidance using perfusion PET/CT imaging for lung stereotactic body radiotherapy (SBRT) of primary or secondary lesion-were analysed. Patients underwent lung perfusion MAA-68Ga PET/CT imaging and pulmonary function tests (PFTs) as part of pre-treatment evaluation. LFVs were delineated using two methods: the commonly used relative to the maximal pixel value threshold method (pmax threshold method, X%pmax volumes) and a new approach based on a relative to whole lung function threshold method (WLF threshold method, FVX% volumes) using a dedicated iterative algorithm. For both methods, LFVs were expressed in terms of % of the anatomical lung volume (AV) and of % of the total lung activity. Functional volumes were compared for patients with normal PFTs and pre-existing airway disease.Results60 patients were analysed. Among the 48 patients who had PFTs, 31 (65%) had pre-existing lung disease. The pmax and WLF threshold methods clearly provided different functional volumes with a wide range of relative lung function for a given pmax volume, and conversely, a wide range of corresponding pmax values for a given WLF volume. The WLF threshold method provided more reliable and consistent volumes with much lower dispersion of LFVs as compared to the pmax method, especially in patients with normal PFTs.ConclusionsWe developed a relative to whole lung function threshold segmentation method to delineate lung functional volumes on perfusion PET/CT imaging. The automated algorithm allows for reproducible contouring. This new approach, relatively unaffected by the presence of hot spots, provides reliable and consistent functional volumes, and is clinically meaningful for clinicians.
| S-EPMC10093378 | biostudies-literature
Project description:Micro-computed tomography (CT) enables three-dimensional (3D) imaging of complex soft tissue structures, but current protocols used to achieve this goal preclude cellular and molecular phenotyping of the tissue. Here we describe a radiolucent cryostage that permits micro-CT imaging of unfixed frozen human lung samples at an isotropic voxel size of (11 µm)3 under conditions where the sample is maintained frozen at -30°C during imaging. The cryostage was tested for thermal stability to maintain samples frozen up to 8 h. This report describes the methods used to choose the materials required for cryostage construction and demonstrates that whole genome mRNA integrity and expression are not compromised by exposure to micro-CT radiation and that the tissue can be used for immunohistochemistry. The new cryostage provides a novel method enabling integration of 3D tissue structure with cellular and molecular analysis to facilitate the identification of molecular determinants of disease.New & noteworthyThe described micro-CT cryostage provides a novel way to study the three-dimensional lung structure preserved without the effects of fixatives while enabling subsequent studies of the cellular matrix composition and gene expression. This approach will, for the first time, enable researchers to study structural changes of lung tissues that occur with disease and correlate them with changes in gene or protein signatures.
| S-EPMC6442661 | biostudies-literature
Project description:PurposeThree-dimensional UTE MRI has shown the ability to provide simultaneous structural and functional lung imaging, but it is limited by respiratory motion and relatively low lung parenchyma SNR. The purpose of this paper is to improve this imaging by using a respiratory phase-resolved reconstruction approach, named motion-compensated low-rank reconstruction (MoCoLoR), which directly incorporates motion compensation into a low-rank constrained reconstruction model for highly efficient use of the acquired data.Theory and methodsThe MoCoLoR reconstruction is formulated as an optimization problem that includes a low-rank constraint using estimated motion fields to reduce the rank, optimizing over both the motion fields and reconstructed images. The proposed reconstruction along with XD and motion state-weighted motion-compensation (MostMoCo) methods were applied to 18 lung MRI scans of pediatric and young adult patients. The data sets were acquired under free-breathing and without sedation with 3D radial UTE sequences in approximately 5 min. After reconstruction, they went through ventilation analyses. Performance across reconstruction regularization and motion-state parameters were also investigated.ResultsThe in vivo experiments results showed that MoCoLoR made efficient use of the data, provided higher apparent SNR compared with state-of-the-art XD reconstruction and MostMoCo reconstructions, and yielded high-quality respiratory phase-resolved images for ventilation mapping. The method was effective across the range of patients scanned.ConclusionThe motion-compensated low-rank regularized reconstruction approach makes efficient use of acquired data and can improve simultaneous structural and functional lung imaging with 3D-UTE MRI. It enables the scanning of pediatric patients under free-breathing and without sedation.
| S-EPMC10501714 | biostudies-literature
Project description:Synaesthesia is a heritable condition in which particular stimuli generate specific and consistent sensory percepts or associations in another modality or processing stream. Functional neuroimaging studies have identified potential correlates of these experiences, including, in some but not all cases, the hyperactivation of visuotemporal areas and of parietal areas thought to be involved in perceptual binding. Structural studies have identified a similarly variable spectrum of differences between synaesthetes and controls. However, it remains unclear the extent to which these neural correlates reflect the synaesthetic experience itself or additional phenotypes associated with the condition. Here, we acquired both structural and functional neuroimaging data comparing thirteen grapheme-color synaesthetes with eleven non-synaesthetes. Using voxel-based morphometry and diffusion tensor imaging, we identify a number of clusters of increased volume of gray matter, of white matter or of increased fractional anisotropy in synaesthetes vs. controls. To assess the possible involvement of these areas in the synaesthetic experience, we used nine areas of increased gray matter volume as regions of interest in an fMRI experiment that characterized the contrast in response to stimuli which induced synaesthesia (i.e., letters) vs. those which did not (non-meaningful symbols). Four of these areas showed sensitivity to this contrast in synaesthetes but not controls. Unexpectedly, in two of them, in left lateral occipital cortex and in postcentral gyrus, the letter stimuli produced a strong negative BOLD signal in synaesthetes. An additional whole-brain fMRI analysis identified 14 areas, three of which were driven mainly by a negative BOLD response to letters in synaesthetes. Our findings suggest that cortical deactivations may be involved in the conscious experience of internally generated synaesthetic percepts.
| S-EPMC3812565 | biostudies-literature
Project description:PURPOSE:To apply a previously designed framework for predicting radiation pneumonitis by using pretreatment lung function heterogeneity metrics, anatomic dosimetry, and functional lung dosimetry derived from 2 imaging modalities within the same cohort. METHODS AND MATERIALS:Treatment planning computed tomography (CT) scans were co-registered with pretreatment [99mTc] macro-aggregated albumin perfusion single-photon positron emission tomography (SPECT)/CT scans and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT scans of 28 patients who underwent definitive thoracic radiation. Clinical radiation pneumonitis was defined as grade ?2 (Common Terminology Criteria for Adverse Events, v. 4). Anatomic dosimetric parameters (mean lung dose [MLD], volume receiving ?20 Gy [V20]) were collected from treatment planning scans. Baseline functional lung heterogeneity parameters and functional lung dose-volume parameters were calculated from pretreatment SPECT/CT and FDG PET/CT scans. Functional heterogeneity parameters calculated over the tumor-subtracted lung included skewness, kurtosis, and coefficient of variation from perfusion SPECT and FDG PET and the global lung parenchymal glycolysis and mean standardized uptake value from FDG PET. Functional dose-volume parameters calculated in regions of highly functional lung, defined on perfusion (p) or SUV (s) images, included mean lung dose (pMLD, sMLD) and V20 (pV20, sV20). Fraction of integral lung function receiving ?20 Gy (pF20, sF20) was also calculated. Equivalent doses in 2 Gy per fraction (EQD2) were calculated to account for differences in treatment regimens and dose fractionation (EQD2Lung). RESULTS:Two anatomic dosimetric parameters (MLD, V20) and 4 functional dosimetric parameters (pMLD, pV20, pF20, sF20) were significant predictors of grade ?2 pneumonitis (area under the curve >0.84; P < .05). Dose-independent functional lung heterogeneity metrics were not associated with pneumonitis incidence. At thresholds of 100% sensitivity and 65% to 91% specificity, corresponding to maximum prediction accuracy for pneumonitis, these parameters had the following cutoff values: MLD = 13.6 Gy EQD2Lung, V20 = 25%, pMLD = 13.2 Gy EQD2Lung, pV20 = 15%, pF20 = 17%, and sF20 = 25%. Significant parameters MLD, V20, pF20, and sF20 were not cross-correlated to significant parameters pMLD and pV20, indicating that they may offer independently predictive information (Spearman ? < 0.7). CONCLUSIONS:We reported differences in anatomic and functional lung dosimetry between patients with and without pneumonitis in this limited patient cohort. Adding selected independent functional lung parameters may risk stratify patients for pneumonitis. Validation studies are ongoing in a prospective functional lung avoidance trial at our institution.
| S-EPMC6202143 | biostudies-literature
Project description:ObjectiveTo investigate the association between CT imaging features and survival outcomes in patients with primary invasive mucinous adenocarcinoma (IMA).Materials and methodsPreoperative CT image findings were consecutively evaluated in 317 patients with resected IMA from January 2011 to December 2015. The association between CT features and long-term survival were assessed by univariate analysis. The independent prognostic factors were identified by the multivariate Cox regression analyses. The survival comparison of IMA patients was investigated using the Kaplan-Meier method and propensity scores. Furthermore, the prognostic impact of CT features was assessed based on different imaging subtypes, and the results were adjusted using the Bonferroni method.ResultsThe median follow-up time was 52.8 months; the 5-year disease-free survival (DFS) and overall survival rates of resected IMAs were 68.5% and 77.6%, respectively. The univariate analyses of all IMA patients demonstrated that 15 CT imaging features, in addition to the clinicopathologic characteristics, significantly correlated with the recurrence or death of IMA patients. The multivariable analysis revealed that five of them, including imaging subtype (p = 0.002), spiculation (p < 0.001), tumor density (p = 0.008), air bronchogram (p < 0.001), emphysema (p < 0.001), and location (p = 0.029) were independent prognostic factors. The subgroup analysis demonstrated that pneumonic-type IMA had a significantly worse prognosis than solitary-type IMA. Moreover, for solitary-type IMAs, the most independent CT imaging biomarkers were air bronchogram and emphysema with an adjusted p value less than 0.05; for pneumonic-type IMA, the tumors with mixed consolidation and ground-glass opacity were associated with a longer DFS (adjusted p = 0.012).ConclusionCT imaging features characteristic of IMA may provide prognostic information and individual risk assessment in addition to the recognized clinical predictors.
| S-EPMC8005341 | biostudies-literature
Project description:Statistical iterative reconstruction (SIR) for X-ray computed tomography (CT) under the penalized weighted least-squares criteria can yield significant gains over conventional analytical reconstruction from the noisy measurement. However, due to the nonlinear expression of the objective function, most exiting algorithms related to the SIR unavoidably suffer from heavy computation load and slow convergence rate, especially when an edge-preserving or sparsity-based penalty or regularization is incorporated. In this work, to address abovementioned issues of the general algorithms related to the SIR, we propose an adaptive nonmonotone alternating direction algorithm in the framework of augmented Lagrangian multiplier method, which is termed as "ALM-ANAD". The algorithm effectively combines an alternating direction technique with an adaptive nonmonotone line search to minimize the augmented Lagrangian function at each iteration. To evaluate the present ALM-ANAD algorithm, both qualitative and quantitative studies were conducted by using digital and physical phantoms. Experimental results show that the present ALM-ANAD algorithm can achieve noticeable gains over the classical nonlinear conjugate gradient algorithm and state-of-the-art split Bregman algorithm in terms of noise reduction, contrast-to-noise ratio, convergence rate, and universal quality index metrics.
| S-EPMC4619856 | biostudies-literature
Project description:PURPOSE:To facilitate investigations into the impacts of acquisition and reconstruction parameters on quantitative imaging, radiomics and CAD using CT imaging, we previously released an open-source implementation of a conventional weighted filtered backprojection reconstruction called FreeCT_wFBP. Our purpose was to extend that work by providing an open-source implementation of a model-based iterative reconstruction method using coordinate descent optimization, called FreeCT_ICD. METHODS:Model-based iterative reconstruction offers the potential for substantial radiation dose reduction, but can impose substantial computational processing and storage requirements. FreeCT_ICD is an open-source implementation of a model-based iterative reconstruction method that provides a reasonable tradeoff between these requirements. This was accomplished by adapting a previously proposed method that allows the system matrix to be stored with a reasonable memory requirement. The method amounts to describing the attenuation coefficient using rotating slices that follow the helical geometry. In the initially proposed version, the rotating slices are themselves described using blobs. We have replaced this description by a unique model that relies on trilinear interpolation together with the principles of Joseph's method. This model offers an improvement in memory requirement while still allowing highly accurate reconstruction for conventional CT geometries. The system matrix is stored column-wise and combined with an iterative coordinate descent (ICD) optimization. The result is FreeCT_ICD, which is a reconstruction program developed on the Linux platform using C++ libraries and released under the open-source GNU GPL v2.0 license. The software is capable of reconstructing raw projection data of helical CT scans. In this work, the software has been described and evaluated by reconstructing datasets exported from a clinical scanner which consisted of an ACR accreditation phantom dataset and a clinical pediatric thoracic scan. RESULTS:For the ACR phantom, image quality was comparable to clinical reconstructions as well as reconstructions using open-source FreeCT_wFBP software. The pediatric thoracic scan also yielded acceptable results. In addition, we did not observe any deleterious impact in image quality associated with the utilization of rotating slices. These evaluations also demonstrated reasonable tradeoffs in storage requirements and computational demands. CONCLUSION:FreeCT_ICD is an open-source implementation of a model-based iterative reconstruction method that extends the capabilities of previously released open-source reconstruction software and provides the ability to perform vendor-independent reconstructions of clinically acquired raw projection data. This implementation represents a reasonable tradeoff between storage and computational requirements and has demonstrated acceptable image quality in both simulated and clinical image datasets.
| S-EPMC6274626 | biostudies-literature
Project description:ObjectivesTo evaluate image quality and reconstruction times of a commercial deep learning reconstruction algorithm (DLR) compared to hybrid-iterative reconstruction (Hybrid-IR) and model-based iterative reconstruction (MBIR) algorithms for cerebral non-contrast CT (NCCT).MethodsCerebral NCCT acquisitions of 50 consecutive patients were reconstructed using DLR, Hybrid-IR and MBIR with a clinical CT system. Image quality, in terms of six subjective characteristics (noise, sharpness, grey-white matter differentiation, artefacts, natural appearance and overall image quality), was scored by five observers. As objective metrics of image quality, the noise magnitude and signal-difference-to-noise ratio (SDNR) of the grey and white matter were calculated. Mean values for the image quality characteristics scored by the observers were estimated using a general linear model to account for multiple readers. The estimated means for the reconstruction methods were pairwise compared. Calculated measures were compared using paired t tests.ResultsFor all image quality characteristics, DLR images were scored significantly higher than MBIR images. Compared to Hybrid-IR, perceived noise and grey-white matter differentiation were better with DLR, while no difference was detected for other image quality characteristics. Noise magnitude was lower for DLR compared to Hybrid-IR and MBIR (5.6, 6.4 and 6.2, respectively) and SDNR higher (2.4, 1.9 and 2.0, respectively). Reconstruction times were 27 s, 44 s and 176 s for Hybrid-IR, DLR and MBIR respectively.ConclusionsWith a slight increase in reconstruction time, DLR results in lower noise and improved tissue differentiation compared to Hybrid-IR. Image quality of MBIR is significantly lower compared to DLR with much longer reconstruction times.Key points• Deep learning reconstruction of cerebral non-contrast CT results in lower noise and improved tissue differentiation compared to hybrid-iterative reconstruction. • Deep learning reconstruction of cerebral non-contrast CT results in better image quality in all aspects evaluated compared to model-based iterative reconstruction. • Deep learning reconstruction only needs a slight increase in reconstruction time compared to hybrid-iterative reconstruction, while model-based iterative reconstruction requires considerably longer processing time.
| S-EPMC8270865 | biostudies-literature
Project description:A Compton camera is a device for imaging a radio-source distribution without using a mechanical collimator. Ordered-subset expectation-maximization (OS-EM) is widely used to reconstruct Compton images. However, the OS-EM algorithm tends to over-concentrate and amplify noise in the reconstructed image. It is, thus, necessary to optimize the number of iterations to develop high-quality images, but this has not yet been achieved. In this paper, we apply a median filter to an OS-EM algorithm and introduce a median root prior expectation-maximization (MRP-EM) algorithm to overcome this problem. In MRP-EM, the median filter is used to update the image in each iteration. We evaluated the quality of images reconstructed by our proposed method and compared them with those reconstructed by conventional algorithms using mathematical phantoms. The spatial resolution was estimated using the images of two point sources. Reproducibility was evaluated on an ellipsoidal phantom by calculating the residual sum of squares, zero-mean normalized cross-correlation, and mutual information. In addition, we evaluated the semi-quantitative performance and uniformity on the ellipsoidal phantom. MRP-EM reduces the generated noise and is robust with respect to the number of iterations. An evaluation of the reconstructed image quality using some statistical indices shows that our proposed method delivers better results than conventional techniques.
| S-EPMC7059936 | biostudies-literature