Optimization of volumetric computed tomography for skeletal analysis of model genetic organisms.
Ontology highlight
ABSTRACT:
SUBMITTER: Vasquez SX
PROVIDER: S-EPMC3189451 | biostudies-literature | 2008 May
REPOSITORIES: biostudies-literature
Publications
Optimization of volumetric computed tomography for skeletal analysis of model genetic organisms.
Vasquez Sergio X SX Hansen Mark S MS Bahadur Ali N AN Hockin Matthew F MF Kindlmann Gordon L GL Nevell Lisa L Wu Isabel Q IQ Grunwald David J DJ Weinstein David M DM Jones Greg M GM Johnson Christopher R CR Vandeberg John L JL Capecchi Mario R MR Keller Charles C
Anatomical record (Hoboken, N.J. : 2007) 20080501 5
Forward and reverse genetics now allow researchers to understand embryonic and postnatal gene function in a broad range of species. Although some genetic mutations cause obvious morphological change, other mutations can be more subtle and, without adequate observation and quantification, might be overlooked. For the increasing number of genetic model organisms examined by the growing field of phenomics, standardized but sensitive methods for quantitative analysis need to be incorporated into rou ...[more]
Similar Datasets
Project description:ObjectivesWe compared the computed tomographic (CT) volumetric analysis and anatomical segment counting (ASC) for predicting postoperative forced expiratory volume in 1 s (FEV1) and diffusing capacity for carbon monoxide (DLCO) in patients who had segmentectomy for early-stage lung cancer.MethodsA total of 175 patients who had segmentectomy for lung cancer and had postoperative pulmonary function test were included. CT volumetric analysis was performed by software, which could measure total lung and target segment volume from CT images. ASC and CT volumetric analysis were used to determine predicted postoperative (PPO) values and the concordance and difference of these values were assessed. The relationship between PPO values and actual postoperative values was also investigated.ResultsThe PPO-FEV1 and PPO-DLCO showed high concordance between 2 methods (concordance correlation coefficient = 0.96 for PPO-FEV1 and 0.95 for PPO-DLCO). There was no significant difference between PPO values as determined by 2 methods (P = 0.53 for PPO-FEV1, P = 0.25 for PPO-DLCO) and actual postoperative values [P = 0.77 (ASC versus actual) and P = 0.20 (CT versus actual) for FEV1; P = 0.41 (ASC versus actual) and P = 0.80 (CT versus actual) for DLCO]. We subdivided the patients according to poor pulmonary function test, the number of resected segments and the location of the resected lobe. All subgroup analyses revealed no significant difference between PPO values and actual postoperative values.ConclusionsBoth CT volumetric analysis and ASC showed high predictability for actual postoperative FEV1 and DLCO in segmentectomy.
| S-EPMC9338704 | biostudies-literature
Project description:BackgroundCommonly used computed tomography (CT) staging systems for chronic rhinosinusitis (CRS) focus on the sinuses and do not quantify disease in the olfactory cleft. The goal of the current study was to determine whether precise measurements of olfactory cleft opacification better correlate with olfaction in patients with CRS.MethodsOlfaction was assessed using the 40-item Smell Identification Test (SIT-40) before and after sinus surgery in adult patients. Olfactory cleft opacification was quantified precisely using three-dimensional (3D), computerized volumetric analysis, as well as via semiquantitative Likert scale estimations at predetermined anatomic sites. Sinus opacification was also quantified using the Lund-Mackay staging system.ResultsThe overall cohort (n = 199) included 89 (44.7%) patients with CRS with nasal polyposis (CRSwNP) and 110 (55.3%) with CRS without nasal polyposis (CRSsNP). The olfactory cleft opacified volume correlated with objective olfaction as determined by the SIT-40 (Spearman's rank correlation coefficient [Rs ] = -0.461; p < 0.001). The correlation was significantly stronger in the CRSwNP subgroup (Rs = -0.573; p < 0.001), whereas no appreciable correlation was found in the CRSsNP group (Rs = -0.141; p = 0.141). Correlations between sinus-specific Lund-Mackay CT scoring and SIT-40 scores were weaker in the CRSwNP (Rs = -0.377; p < 0.001) subgroup but stronger in the CRSsNP (Rs = -0.225; p = 0.018) group when compared to olfactory cleft correlations. Greater intraclass correlations (ICCs) were found between quantitative volumetric measures of olfactory cleft opacification (ICC = 0.844; p < 0.001) as compared with semiquantitative Likert grading (ICC = 0.627; p < 0.001).ConclusionQuantitative measures of olfactory cleft opacification correlate with objective olfaction, with the strongest correlations seen in patients with nasal polyps.
| S-EPMC4560663 | biostudies-literature
Project description:BackgroundMuscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans.MethodsAttenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours.ResultsWe trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%.ConclusionsThis work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer.
| S-EPMC8138051 | biostudies-literature
Project description:OBJECTIVES:We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP). MATERIALS AND METHODS:Thirty-three patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of 2 thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence-based guidelines for computed tomography diagnosis of UIP. Two cardiothoracic fellows with 1 year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple-detector computed tomography images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multidirectional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes. RESULTS:We compared the predictions on the basis of regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the 2 fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic. CONCLUSIONS:Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its associated risks.
| S-EPMC4355184 | biostudies-literature
Project description:The radiological assessment of muscle properties-size, mass, density (also termed radiodensity), composition, and adipose tissue infiltration-is fundamental in muscle diseases. More recently, it also became obvious that muscle atrophy, also termed muscle wasting, is caused by or associated with many other diseases or conditions, such as inactivity, malnutrition, chronic obstructive pulmonary disorder, cancer-associated cachexia, diabetes, renal and cardiac failure, and sarcopenia and even potentially with osteoporotic hip fracture. Several techniques have been developed to quantify muscle morphology and function. This review is dedicated to quantitative computed tomography (CT) of skeletal muscle and only includes a brief comparison with magnetic resonance imaging. Strengths and limitations of CT techniques are discussed in detail, including CT scanner calibration, acquisition and reconstruction protocols, and the various quantitative parameters that can be measured with CT, starting from simple volume measures to advanced parameters describing the adipose tissue distribution within muscle. Finally, the use of CT in sarcopenia and cachexia and the relevance of muscle parameters for the assessment of osteoporotic fracture illustrate the application of CT in two emerging areas of medical interest.
| S-EPMC6260391 | biostudies-literature
Project description:Background The aim of this study was to assess the feasibility and safety of dynamic volumetric computed tomography angiography ( DV - CTA ) for endoleaks detected but not classified by conventional CTA in patients after endovascular aortic repair. Methods and Results From January 2016 to October 2017, 24 patients with endoleaks with aneurysm sac enlargement detected but not classified by conventional CTA were randomly assigned to the conventional CTA group and the DV - CTA group for further evaluation. The amount of contrast agent, radiation dosage, and changes in creatinine during the operation were compared between the 2 groups. Reintervention was performed according to the endoleak classification followed by the 6- and 12-month follow-up. The accuracy of classifying endoleaks by DV - CTA was comparable to that by digital subtraction angiography. Additionally, the total amount of contrast agent and the radiation dosage in the DV - CTA group during the operation were diminished by 14.0% ( P=0.007) and 12.1% ( P=0.004), respectively, compared with those in the conventional CTA group. No contrast-induced nephropathy was observed. All endoleaks were treated instantly after identification. No endoleaks were found in any of the patients during follow-up. Conclusions DV - CTA could replace digital subtraction angiography as an alternative method for the classification of endoleaks that cannot be differentiated by conventional CTA . Additionally, the amount of contrast agent and the total radiation dosage were substantially reduced, which improved safety among operators and patients.
| S-EPMC6507202 | biostudies-literature
Project description:OBJECTIVES: To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images. METHODS: We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < -950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods. RESULTS: Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis. CONCLUSIONS: A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed.
| S-EPMC3686391 | biostudies-literature
Project description:Volume measurements of maxillary sinus may be useful to identify diseases affecting paranasal sinuses. However, literature shows a lack of consensus in studies measuring the volume. This may be attributable to different computed tomography data acquisition techniques, segmentation methods, focuses of investigation, among other reasons. Furthermore, methods for volumetrically quantifying the maxillary sinus are commonly manual or semiautomated, which require substantial user expertise and are time-consuming. The purpose of the present study was to develop an automated tool for quantifying the total and air-free volume of the maxillary sinus based on computed tomography images. The quantification tool seeks to standardize maxillary sinus volume measurements, thus allowing better comparisons and determinations of factors that influence maxillary sinus size. The automated tool utilized image processing techniques (watershed, threshold, and morphological operators). The maxillary sinus volume was quantified in 30 patients. To evaluate the accuracy of the automated tool, the results were compared with manual segmentation that was performed by an experienced radiologist using a standard procedure. The mean percent differences between the automated and manual methods were 7.19% ± 5.83% and 6.93% ± 4.29% for total and air-free maxillary sinus volume, respectively. Linear regression and Bland-Altman statistics showed good agreement and low dispersion between both methods. The present automated tool for maxillary sinus volume assessment was rapid, reliable, robust, accurate, and reproducible and may be applied in clinical practice. The tool may be used to standardize measurements of maxillary volume. Such standardization is extremely important for allowing comparisons between studies, providing a better understanding of the role of the maxillary sinus, and determining the factors that influence maxillary sinus size under normal and pathological conditions.
| S-EPMC5755892 | biostudies-literature
Project description:Three-dimensional high-resolution imaging methods are important for cellular-level research. Optical coherence microscopy (OCM) is a low-coherence-based interferometry technology for cellular imaging with both high axial and lateral resolution. Using a high-numerical-aperture objective, OCM normally has a shallow depth of field and requires scanning the focus through the entire region of interest to perform volumetric imaging. With a higher-numerical-aperture objective, the image quality of OCM is affected by and more sensitive to aberrations. Interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO) are computed imaging techniques that overcome the depth-of-field limitation and the effect of optical aberrations in optical coherence tomography (OCT), respectively. In this work we combine OCM with ISAM and CAO to achieve high-speed volumetric cellular imaging. Experimental imaging results of ex vivo human breast tissue, ex vivo mouse brain tissue, in vitro fibroblast cells in 3D scaffolds, and in vivo human skin demonstrate the significant potential of this technique for high-speed volumetric cellular imaging.
| S-EPMC4230871 | biostudies-other
Project description:This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.
| S-EPMC5728109 | biostudies-literature