Background: Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. Result: First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. Conclusion: We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.
Doc number: 1 Abstract Background: Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format. Results: Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment.heiberg.se . Conclusions: Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.
Background: Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data. Methods: In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models. Results: The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy. Conclusions: Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.
Background Magnetic particle imaging (MPI) is a new tomographic imaging technique capable of imaging magnetic tracer material at high temporal and spatial resolution. Image reconstruction requires solving a system of linear equations, which is characterized by a "system function" that establishes the relation between spatial tracer position and frequency response. This paper for the first time reports on the structure and properties of the MPI system function. Methods An analytical derivation of the 1D MPI system function exhibits its explicit dependence on encoding field parameters and tracer properties. Simulations are used to derive properties of the 2D and 3D system function. Results It is found that for ideal tracer particles in a harmonic excitation field and constant selection field gradient, the 1D system function can be represented by Chebyshev polynomials of the second kind. Exact 1D image reconstruction can thus be performed using the Chebyshev transform. More realistic particle magnetization curves can be treated as a convolution of the derivative of the magnetization curve with the Chebyshev functions. For 2D and 3D imaging, it is found that Lissajous excitation trajectories lead to system functions that are closely related to tensor products of Chebyshev functions. Conclusion Since to date, the MPI system function has to be measured in time-consuming calibration scans, the additional information derived here can be used to reduce the amount of information to be acquired experimentally and can hence speed up system function acquisition. Furthermore, redundancies found in the system function can be removed to arrive at sparser representations that reduce memory load and allow faster image reconstruction.
In animal studies tumor size is used to assess responses to anticancer therapy. Current standard for volumetric measurement of xenografted tumors is by external caliper, a method often affected by error. The aim of the present study was to evaluate if microCT gives more accurate and reproducible measures of tumor size in mice compared with caliper measurements. Furthermore, we evaluated the accuracy of tumor volume determined from 18F-fluorodeoxyglucose (18F-FDG) PET. Subcutaneously implanted human breast adenocarcinoma cells in NMRI nude mice served as tumor model. Tumor volume (n = 20) was determined in vivo by external caliper, microCT and 18F-FDG-PET and subsequently reference volume was determined ex vivo. Intra-observer reproducibility of the microCT and caliper methods were determined by acquiring 10 repeated volume measurements. Volumes of a group of tumors (n = 10) were determined independently by two observers to assess inter-observer variation. Tumor volume measured by microCT, PET and caliper all correlated with reference volume. No significant bias of microCT measurements compared with the reference was found, whereas both PET and caliper had systematic bias compared to reference volume. Coefficients of variation for intra-observer variation were 7% and 14% for microCT and caliper measurements, respectively. Regression coefficients between observers were 0.97 for microCT and 0.91 for caliper measurements. MicroCT was more accurate than both caliper and 18F-FDG-PET for in vivo volumetric measurements of subcutaneous tumors in mice.18F-FDG-PET was considered unsuitable for determination of tumor size. External caliper were inaccurate and encumbered with a significant and size dependent bias. MicroCT was also the most reproducible of the methods.
Background: Carotid plaque echolucency as detected by Color Doppler ultrasonography (CDUS) has been used as a potential marker of plaque vulnerability. However, contrast-enhanced ultrasound (CEUS) has recently been shown to be a valuable method to evaluate the vulnerability and neovascularization within carotid atherosclerotic plaques. The aim of this study was to compare CEUS and CDUS in the assessment of plaque vulnerability using transcranial color Doppler (TCD) monitoring of microembolic signals (MES) as a reference technique. Methods: A total of 46 subjects with arterial stenosis (>= 50%) underwent a carotid duplex ultrasound, TCD monitoring of MES and CEUS (SonoVue doses of 2.0 mL) within a span of 3 days. The agreement between the CEUS, CDUS, and MES findings was assessed with a chi-square test. A p-value less than 0.05 was considered statistically significant. Results: Neovascularization was observed in 30 lesions (44.4%). The vascular risk factors for stroke were similar and there were no age or gender differences between the 2 groups. Using CEUS, MES were identified in 2 patients (12.5%) within class 1 (non-neovascularization) as opposed to 15 patients (50.0%) within class 2 (neovascularization) (p = 0.023). CDUS revealed no significant differences in the appearance of the MES between the 2 groups (hyperechoic and hypoechoic) (p = 0.237). Conclusion: This study provides preliminary evidence to suggest that intraplaque neovascularization detected by CEUS is associated with the presence of MESs, where as plaque echogenicity on traditional CDUS does not. These findings argue that CEUS may better identify high-risk plaques.
Background: Whole body fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) is the standard of care in oncologic diagnosis and staging, and patient radiation dose must be well understood to balance exam benefits with the risk from radiation exposure. Although reference PET/CT patient doses are available, the potential for widely varying total dose prompts evaluation of clinic-specific patient dose. The aims of this study were to use exam-specific information to characterize the radiation dosimetry of PET/CT exams that used two different CT techniques for adult oncology patients and evaluate the practicality of employing an exam-specific approach to dose estimation. Methods: Whole body PET/CT scans from two sets of consecutive adult patients were retrospectively reviewed. One set received a PET scan with a standard registration CT and the other a PET scan with a diagnostic quality CT. PET dose was calculated by modifying the standard reference phantoms in OLINDA/EXM 1.1 with patient-specific organ mass. CT dose was calculated using patient-specific data in ImPACT. International Commission on Radiological Protection publication 103 tissue weighting coefficients were used for effective dose. Results: One hundred eighty three adult scans were evaluated (95 men, 88 women). The mean patient-specific effective dose from a mean injected 18F-FDG activity of 450 +/- 32 MBq was 9.0 +/- 1.6 mSv. For all standard PET/CT patients, mean effective mAs was 39 +/- 11 mAs, mean CT effective dose was 5.0 +/- 1.0 mSv and mean total effective dose was 14 +/- 1.3 mSv. For all diagnostic PET/CT patients, mean effective mAs was 120 +/- 51 mAs, mean CT effective dose was 15.4 +/- 5.0 mSv and mean total effective dose was 24.4 +/- 4.3 mSv. The five organs receiving the highest organ equivalent doses in all exams were bladder, heart, brain, liver and lungs. Conclusions: Patient-specific parameters optimize the patient dosimetry utilized in the medical justification of whole body PET/CT referrals and optimization of PET and CT acquisition parameters. Incorporating patient-specific data into dose estimates is a worthwhile effort for characterizing patient dose, and the specific dosimetric information assists in the justification of risk and optimization of PET/CT.
Background: To assess whether CT-derived texture features predict survival in patients undergoing resection for pancreatic ductal adenocarcinoma (PDAC). Methods: Thirty patients with pre-operative CT from 2007 to 2012 for PDAC were included. Tumor size and five texture features namely uniformity, entropy, dissimilarity, correlation, and inverse difference normalized were calculated. Mann-Whitney rank sum test was used to compare tumor with normal pancreas. Receiver operating characteristics (ROC) analysis, Cox regression and Kaplan-Meier tests were used to assess association of texture features with overall survival (OS). Results: Uniformity (p < 0.001), entropy (p = 0.009), correlation (p < 0.001), and mean intensity (p < 0.001) were significantly different in tumor regions compared to normal pancreas. Tumor dissimilarity (p = 0.045) and inverse difference normalized (p = 0.046) were associated with OS whereas tumor intensity (p = 0.366), tumor size (p = 0.611) and other textural features including uniformity (p = 0.334), entropy (p = 0.330) and correlation (p = 0.068) were not associated with OS. Conclusion: CT-derived PDAC texture features of dissimilarity and inverse difference normalized are promising prognostic imaging biomarkers of OS for patients undergoing curative intent surgical resection.
Background: Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Methods: Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. Results: The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. Conclusions: The suggested method has the potential to discover previously unknown 3D shape biomarkers from medical imaging data. Thus, it could contribute to improving diagnosis and risk stratification in complex cardiac disease.
BackgroundAlthough independent component analysis (ICA) has been widely applied to functional magnetic resonance imaging (fMRI) data to reveal spatially independent brain networks, the order indetermination of ICA leads to the problem of target component selection. The temporally constrained independent component analysis (TCICA) is capable of automatically extracting the desired spatially independent components by adding the temporal prior information of the task to the mixing matrix for fMRI data analysis. However, the TCICA method can only extract a single component that tends to be a mix of multiple task-related components when there exist several independent components related to one task.MethodsIn this study, we proposed a TCICA with threshold (TCICA-Thres) method that performed TCICA outside the threshold and performed FastICA inside the threshold to automatically extract all the target components related to one task. The proposed approach was tested using simulated fMRI data and was applied to a real fMRI experiment using 13 subjects. Additionally, the performance of TCICA-Thres was compared with that of FastICA and TCICA.ResultsThe results from the simulation and the fMRI data demonstrated that TCICA-Thres better extracted the task-related components than TCICA. Moreover, TCICA-Thres outperformed FastICA in robustness to noise, spatial detection power and computational time.ConclusionsThe proposed TCICA-Thres solves the limitations of TCICA and extends the application of TCICA in fMRI data analysis.
Background: The 'Cytocam' is a third generation video-microscope, which enables real time visualisation of the in vivo microcirculation. Based upon the principle of incident dark field (IDF) illumination, this hand held computer-controlled device was designed to address the technical limitations of its predecessors, orthogonal polarization spectroscopy and sidestream dark field (SDF) imaging. In this manuscript, we aimed to compare the quality of sublingual microcirculatory image acquisition between the IDF and SDF devices. Methods: Using the microcirculatory image quality scoring (MIQS) system, (six categories scored as either 0 = optimal, 1 = acceptable, or 10 = unacceptable), two independent raters compared 30 films acquired using the Cytocam IDF video-microscope, to an equal number obtained with an SDF device. Blinded to the origin of the films, the raters were therefore able to score between 0 and 60 for each film analysed. The scores' distributions between the two techniques were compared. Results: The median MIQS (95 % CI) given to the SDF camera was 7 (1.5-12), as compared to 1 (0.5-1.0) for the IDF device (p <0.0001). Of the six categories assessed by the MIQS, nearly one fifth of the SDF videos were scored as unacceptable for pressure (20 %), content (20 %), and stability (17 %), with focus scoring deficiently 13 % of the time. High agreement between the two raters scoring values was evident, with an intra-class correlation coefficient (ICC) of 0.96 (95 % CI: 0.94, 0.98). Conclusions: These results demonstrate that the quality of sublingual microcirculatory image acquisition is superior in the Cytocam IDF video-microscope, as compared to the SDF video-microscope
Background: Ensuring an adequate blood supply is essential to the safe performance of an anastomosis during esophagectomy and the prevention of anastomotic leakage. Recently, indocyanine green (ICG) fluorescence imaging has been used to visualize the blood supply when anastomosis is performed in vascular surgery. We used ICG fluorescence imaging to visualize the blood supply for reconstruction during esophagectomy. Methods: Since January 2009, we have performed ICG fluorescence imaging in 33 patients with thoracic esophageal cancer who underwent thoracic esophagectomy. After pulling up the reconstructed stomach, 2.5 mg of ICG was injected as a bolus. ICG fluorescence imaging was performed with a near-infrared camera, and the images were recorded. Results: ICG fluorescence was easily detected in all patients 1 min after injection. Vascular networks were well visualized in the gastric wall and omentum. The blood supply route was located in the greater omentum beside the splenic hilum in 22 (66.7%) of the 33 patients. Conclusions: ICG fluorescence can be used to evaluate the blood supply to the reconstructed stomach in patients undergoing esophagectomy for esophageal cancer. On ICG fluorescence imaging, the splenic hiatal vessels were the major blood supply for the anastomosis in most patients.
The purpose was to investigate the difference of detection rate of incidental pancreatic cystic lesions (PCLs) with computed tomography (CT) and magnetic resonance imaging (MRI) and to compare the difference between CT and MRI and to explore the effect of this difference on surgical resection. We reviewed the diagnostic reports for incidental PCLs between 2013 and 2016. Images of PCLs would be re-evaluated. Clinical and imaging data were recorded. The chi-square and independent t-test were conducted for categorical and continuous variables. The prevalence of PCLs was 1.91% (1038/54210) and 3.36% (1282/38099) on CT and MRI respectively, and increased with increasing age (P < 0.001). No significant differences were found in the annual prevalence of PCLs on CT (P = 0.796) and MRI (P = 0.213) from 2013 to 2016 while the number of examinations was increasing every year. The annual detection rate of MRI for small PCLs (< 20 mm) was significantly higher than CT (P < 0.001), but was not significantly different for large PCLs (≥20 mm). The rate of surgical resection of PCLs (≥20 mm) in MRI group was higher than CT (55.2% vs. 37.0%, P < 0.001). The detection rate of PCLs on CT and MRI tended to be stable despite increasing scan volumes. Female had a slightly more frequency of PCLs than male. MRI detected more small PCLs(< 20 mm) and had higher impact on surgical resection of large PCL(≥20 mm) compared with CT.
BackgroundImage restoration is one of the fundamental and essential tasks within image processing. In medical imaging, developing an effective algorithm that can automatically remove random noise in brain magnetic resonance (MR) images is challenging. The collateral filter has been shown a more powerful algorithm than many existing methods. However, the computation of the collateral filter is more time-consuming and the selection of the filter parameters is also laborious. This paper proposes an automatic noise removal system based on the accelerated collateral filter for brain MR images.MethodsTo solve these problems, we first accelerated the collateral filter with parallel computing using the graphics processing unit (GPU) architecture. We adopted the compute unified device architecture (CUDA), an application programming interface for the GPU by NVIDIA, to hasten the computation. Subsequently, the optimal filter parameters were selected and the automation was achieved by artificial neural networks. Specifically, an artificial neural network system associated with image feature analysis was adopted to establish the automatic image restoration framework. The best feature combination was selected by the paired t-test and the sequential forward floating selection (SFFS) methods.ResultsExperimental results indicated that not only did the proposed automatic image restoration algorithm perform dramatically faster than the traditional collateral filter, but it also effectively removed the noise in a wide variety of brain MR images. A speed up gain of 34 was attained to process an MR image, which completed within 0.1s. Representative illustrations of brain tumor images demonstrated the capability of identifying lesion boundaries, which outperformed many existing methods.ConclusionsWe believe that our accelerated and automated restoration framework is promising for achieving robust filtering in many brain MR image restoration applications.
BackgroundThis study was performed to assess changes in diffusion tensor imaging (DTI) over time in patients with amyotrophic lateral sclerosis (ALS).MethodsWe performed DTI in 23 ALS patients who had two magnetic resonance imaging (MRI) scans at 6month intervals and to correlate results with clinical features. The revised ALS functional rating scale (ALSFRS-R) was administered at each clinical visit. Data analysis included voxel-based white matter tract-based spatial statistics (TBSS) and atlas-based region-of-interest (ROI) analysis of fractional anisotropy (FA) and mean diffusivity (MD).ResultsWith TBSS, there were no significant changes between the two scans. The average change in FA and MD in the ROIs over 6months was small and not significant after allowing for multiple comparisons. After allowing for multiple comparisons, there was no significant correlation of FA or MD with ALSFRS-R.ConclusionThis study shows that there is little evidence of progressive changes in DTI over time in ALS. This could be because white matter is already substantially damaged by the time of onset of symptoms of ALS.
BackgroundThe assessment of liver percentage fat fraction (%FF) using proton density fat fraction sequences is becoming increasingly accessible. Previous studies have tended to use multiple small ROIs that focus on Couinaud segments. In an effort to simplify day-to-day analysis, this study assesses the impact of using larger, elliptical ROIs focused on a single hepatic lobe. Additionally, we assess the impact of sampling fewer transhepatic slices when measuring %FF.MethodsRetrospective analysis of prospectively obtained images from 34 volunteers using an IDEAL IQ sequence. Two observers independently measured %FF using three different protocols: freehand whole-liver ROI (fh-ROI), elliptical-ROI on the right lobe (rt-ROI) and elliptical-ROI on the left lobe (lt-ROI).ResultsInter-observer reliability for all measurements techniques was excellent' (Spearman's rank correlation coefficients 0.81-0.98). There was a significant difference (Paired Wilcoxon Test: p<0.001) between the median %FF obtained using fh-ROI when compared to the rt-ROI method, the maximum mean difference between the two techniques was 2.79% (95% CI). For all sampling methods a Kruskall-Wallis analysis demonstrated no significant difference in mean %FF when the number of slices sampled was reduced from 11 to 1. The mean coefficient of variance increased when more slices were sampled (3 slices=0.1, 11 slices=0.17, p<0.001).ConclusionSimplified ROIs focused on one hepatic lobe provide %FF measurements that are unlikely to be sufficiently accurate for use in clinical practice. Freehand whole-liver ROIs should be used in preference.A single freehand ROI measurement taken at the level of the hepatic hilum yields a %FF that is representative of the mean whole liver % FF. Multiple slices are needed to measure heterogeneity.
BackgroundThis study aimed to assess the effect of exposure parameters such as milliampere (mA) and field of view (FOV) of cone beam computed tomography (CBCT) on a metal artifact of dental implants placed in different bone densities.MethodsA total of 27 bone blocks with different densities (nine were type 1, nine were types 2 and 3, and nine were type 4) were used in this in vitro, experimental study. These blocks were placed in mandibular wax models. The blocks were scanned after drilling (hole preparation) and after implant placement using Cranex3D imaging system with a 4x6 cm(2)and 6x8 cm(2) FOV and 4 and 10mA. Gray value of the bone blocks was recorded before and after placement of implants.ResultsIn general, irrespective of bone density, the amount of artifacts was lower in small FOV compared to large FOV (P0.05). Artifacts in type 4 bone were greater than in other bone types (P0.05).ConclusionAccording to the results of this study, Peri-implant artifacts were seen in all bone types; the amount of artifacts in type 4 bone was higher than that in other types. Size of FOV and bone density affect the metal artifacts around dental implants; so that a smaller FOV can be used to decrease metal artifacts.
BackgroundRecent studies have highlighted the correlation between diabetes and pancreatic fat infiltration. Notably, pancreatic fat content (PFC) is a potential biomarker in diabetic patients, and magnetic resonance imaging (MRI) provides an effective method for noninvasive assessment of pancreatic fat infiltration. However, most reports of quantitative measurement of pancreatic fat have lacked comparisons of pathology results. The primary objective of this study was to determine the feasibility and accuracy of pancreatic MRI by using pancreatic fat fraction (PFF) measurements with the IDEAL-IQ sequence; the secondary objective was to explore changes in PFC between pigs with and without diabetes.MethodsIn this prospective study, 13 Bama Mini-pigs (7 females, 6 males; median age, 2weeks) were randomly assigned to diabetes (n=7) or control (n=6) groups. Pigs in the diabetes group received high fat/high sugar feed, combined with streptozotocin injections. At the end of 15months, biochemical changes were evaluated. All pigs underwent axial MRI with the IDEAL-IQ sequence to measure PFF; PFC of fresh pancreatic parenchyma was measured by the Soxhlet extraction method; and pancreatic fat distribution was observed by histopathology. Results of all analyses were compared between the diabetes and control groups by using the Mann-Whitney U-test. Correlations of PFF and PFC, fasting blood glucose (GLU), and serum insulin (INS) were calculated by using the Spearman correlation coefficient. Single-measure intraclass correlation coefficient (ICC) was used to assess interreader agreement.ResultsThere were significant differences between diabetes and control groups: GLU (mmol/L) was 18.066.03 and 5.06 +/- 1.41 (P<0.001); INS (mU/L) was 21.59 +/- 2.93 and 29.32 +/- 3.27 (P=0.003); PFC (%) was 34.60 +/- 3.52 and 28.63 +/- 3.25 (P=0.027); and PFF (%) was 36.51 +/- 4.07 and 27.75 +/- 3.73 (P=0.003). There was a strongly positive correlation between PFF and PFC (r=0.934, P<0.001); there were moderate correlations between PFF and GLU (r=0.736, P=0.004; positive correlation), and between PFF and INS (r=-0.747, P=0.003; negative correlation). Excellent interreader agreement was observed for PFF measurements (ICC, 0.954).Conclusions Pancreatic fat infiltration shows a clear association with diabetes. MRI with the IDEAL-IQ sequence can be used to accurately and reproducibly quantify PFC.
BackgroundIn this study, we explored how various preprocessing approaches can be employed to enhance the capability of dental CBCT to accurately estimate trabecular bone microarchitectural parameters.MethodsIn total, 30 bovine vertebrae cancellous bone specimens were used for in study. Voxel resolution 18-m micro-computed tomography (micro-CT) and 100-m dental CBCT were used to scan each specimen. Micro-CT images were used to calculate trabecular bone microarchitectural parameters; the results were set as the gold standard. Subsequently, before the dental CBCT images were converted into binary images to calculate trabecular bone microarchitectural parameters, three preprocessing approaches were used to process the dental CBCT images. For Group 1, no preprocessing approach was applied. For Group 2, images were sharpened and despeckable noises were removed. For Group 3, the function of local thresholding was added to Group 2 to form Group 3. For Group 4, the air pixels was removed from Group 3 to form Group 4. Subsequently, all images were imported into a software package to estimate trabecular bone microarchitectural parameters (bone volume fraction (BV/TV), trabecular thickness (TbTh), trabecular number (TbN), and trabecular separation (TbSp)). Finally, a paired t-test and a Pearson correlation test were performed to compare the capability of micro-CT with the capability of dental CBCT for estimating trabecular bone microarchitectural parameters.ResultsRegardless of whether dental CBCT images underwent image preprocessing (Groups 1 to 4), the four trabecular bone microarchitectural parameters measured using dental CBCT images were significantly different from those measured using micro-CT images. However, after three image preprocessing approaches were applied to the dental CBCT images (Group 4), the BV/TV obtained using dental CBCT was highly positively correlated with that obtained using micro-CT (r = 0.87, p < 0.001); the correlation coefficient was greater than that of Group 1 (r = -0.15, p = 0.412), Group 2 (r = 0.16, p = 0.386), and Group 3 (r = 0.47, p = 0.006). After dental CBCT images underwent image preprocessing, the efficacy of using dental CBCT for estimating TbN and TbSp was enhanced.ConclusionsImage preprocessing approaches can be used to enhance the efficacy of using dental CBCT for predicting trabecular bone microarchitectural parameters.
Diagnosis of giant cell arteritis by temporal artery biopsy is time-consuming and visual loss lies in the first week after its diagnosis. The purpose of the study was to test the hypothesis that ultrasound can reduce the risk of overdiagnosis and overtreatment in giant cell arteritis. Data regarding physical/ clinical features examinations, temporal artery biopsy examinations, ultrasound findings, and magnetic resonance imaging examinations of 980 suspected patients for giant cell arteritis were included in the study. Decision curve analysis was applied to get a beneficial score for selected diagnostic modalities. Cost analysis was performed for each patient. Fewer numbers of false positive giant cell arteritis results were reported under physical/ clinical features examinations following ultrasound detection than physical/clinical features examinations following temporal artery biopsy examinations (45 vs. 127, p < 0.0001). The working area that detects giant cell arteritis at least one time for physical/ clinical features examinations following ultrasound detection and physical/ clinical features examinations following temporal artery biopsy examinations were 0-91% and 0-86%. No significant difference for true negative results between magnetic resonance imaging and physical and clinical features examinations following ultrasound detection (p = 0.007). Physical and clinical features examinations following ultrasound detection were less expensive method than physical/ clinical features examinations following temporal artery biopsy examinations (14,023 ± 982 ¥/patient vs. 18,551 ± 1231 ¥/patient, p < 0.0001) and MRI. Physical and clinical features examinations following ultrasound are recommended for diagnosis of patients with suspected giant cell arteritis.