Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised ClusteringRead the full article
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Automatic Detection of AMD and DME Retinal Pathologies Using Deep Learning
Diabetic macular edema (DME) and age-related macular degeneration (AMD) are two common eye diseases. They are often undiagnosed or diagnosed late. This can result in permanent and irreversible vision loss. Therefore, early detection and treatment of these diseases can prevent vision loss, save money, and provide a better quality of life for individuals. Optical coherence tomography (OCT) imaging is widely applied to identify eye diseases, including DME and AMD. In this work, we developed automatic deep learning-based methods to detect these pathologies using SD-OCT scans. The convolutional neural network (CNN) from scratch we developed gave the best classification score with an accuracy higher than 99% on Duke dataset of OCT images.
Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence
Background. The 3D T1W turbo field echo sequence is a standard imaging method for acquiring high-contrast images of the brain. However, the contrast-to-noise ratio (CNR) can be affected by the turbo factor, which could affect the delineation and segmentation of various structures in the brain and may consequently lead to misdiagnosis. This study is aimed at evaluating the effect of the turbo factor on image quality and volumetric measurement reproducibility in brain magnetic resonance imaging (MRI). Methods. Brain images of five healthy volunteers with no history of neurological diseases were acquired on a 1.5 T MRI scanner with varying turbo factors of 50, 100, 150, 200, and 225. The images were processed and analyzed with FreeSurfer. The influence of the TFE factor on image quality and reproducibility of brain volume measurements was investigated. Image quality metrics assessed included the signal-to-noise ratio (SNR) of white matter (WM), CNR between gray matter/white matter (GM/WM) and gray matter/cerebrospinal fluid (GM/CSF), and Euler number (EN). Moreover, structural brain volume measurements of WM, GM, and CSF were conducted. Results. Turbo factor 200 produced the best SNR () and GM/WM CNR (), but turbo factor 100 offered the most reproducible SNR () and GM/WM CNR (). Turbo factor 50 had the worst and the least reproducible SNR, whereas turbo factor 225 had the worst and the least reproducible GM/WM CNR. Turbo factor 200 again had the best GM/CSF CNR but offered the least reproducible GM/CSF CNR. Turbo factor 225 had the best performance on EN (-21), while turbo factor 200 was next to the most reproducible turbo factor on EN (11). The results showed that turbo factor 200 had the least data acquisition time, in addition to superior performance on SNR, GM/WM CNR, GM/CSF CNR, and good reproducibility characteristics on EN. Both image quality metrics and volumetric measurements did not vary significantly () with the range of turbo factors used in the study by one-way ANOVA analysis. Conclusion. Since no significant differences were observed in the performance of the turbo factors in terms of image quality and volume of brain structure, turbo factor 200 with a 74% acquisition time reduction was found to be optimal for brain MR imaging at 1.5 T.
Assessing Predictive Ability of Dynamic Time Warping Functional Connectivity for ASD Classification
Functional connectivity MRI (fcMRI) is a technique used to study the functional connectedness of distinct regions of the brain by measuring the temporal correlation between their blood oxygen level-dependent (BOLD) signals. fcMRI is typically measured with the Pearson correlation (PC), which assumes that there is no lag between time series. Dynamic time warping (DTW) is an alternative measure of similarity between time series that is robust to such time lags. We used PC fcMRI data and DTW fcMRI data as predictors in machine learning models for classifying autism spectrum disorder (ASD). When combined with dimension reduction techniques, such as principal component analysis, functional connectivity estimated with DTW showed greater predictive ability than functional connectivity estimated with PC. Our results suggest that DTW fcMRI can be a suitable alternative measure that may be characterizing fcMRI in a different, but complementary, way to PC fcMRI that is worth continued investigation. In studying different variants of cross validation (CV), our results suggest that, when it is necessary to tune model hyperparameters and assess model performance at the same time, a -fold CV nested within leave-one-out CV may be a competitive contender in terms of performance and computational speed, especially when sample size is not large.
Prediction of Esophageal Varices in Viral Hepatitis C Cirrhosis: Performance of Combined Ultrasonography and Clinical Predictors
Objectives. This study is aimed at evaluating the diagnostic performance of clinical predictors and the Doppler ultrasonography in predicting esophageal varices (EV) in patients with hepatitis C-related cirrhosis and exploring the practical predictors of EV. Methods. We conducted a prospective study from July 2020 to January 2021, enrolling 65 patients with mild hepatitis C-related cirrhosis. We obtained clinical data and performed grayscale and the Doppler ultrasound to explore the predictors of EV. Esophagogastroduodenoscopy (EGD) was performed as the reference test by the gastroenterologist within a week. Results. The prevalence of EV in the study was 41.5%. Multivariable regression analysis revealed that gender (female, , ), platelet count (<150000 per ml, , ), splenic length (>11 cm, , ), and absent right hepatic vein (RHV) triphasicity (, ) were significant predictors of EV. However, the diagnostic accuracy indices for isolated predictors were not good (–0.66). A combination of these four predictors increases the diagnostic accuracy in predicting the presence of EV (, 95% CI 0.69-0.91). Furthermore, the Doppler assessment of the right hepatic vein waveform showed good reproducibility (). Conclusion. Combining clinical and Doppler ultrasound features can be used as a screening test for predicting the presence of EV in patients with hepatitis C-related cirrhosis. The practical predictors identified in this study could serve as an alternative to invasive EGD in EV diagnosis. Further studies are needed to explore the diagnostic accuracy of additional noninvasive predictors, such as elastography, to improve EV screening.
Multiparametric Magnetic Resonance Imaging and Magnetic Resonance Elastography to Evaluate the Early Effects of Bariatric Surgery on Nonalcoholic Fatty Liver Disease
Background. Bariatric surgery is the most effective treatment for morbid obesity and reduces the severity of nonalcoholic fatty liver disease (NAFLD) in the long term. Less is known about the effects of bariatric surgery on liver fat, inflammation, and fibrosis during the early stages following bariatric surgery. Aims. This exploratory study utilises advanced imaging methods to investigate NAFLD and fibrosis changes during the early metabolic transitional period following bariatric surgery. Methods. Nine participants with morbid obesity underwent sleeve gastrectomy. Multiparametric MRI (mpMRI) and magnetic resonance elastography (MRE) were performed at baseline, during the immediate (1 month), and late (6 months) postsurgery period. Liver fat was measured using proton density fat fraction (PDFF), disease activity using iron-correct T1 (cT1), and liver stiffness using MRE. Repeated measured ANOVA was used to assess longitudinal changes and Dunnett’s method for multiple comparisons. Results. All participants (Age years, BMI ) had elevated hepatic steatosis at baseline (PDFF >5%). In the immediate postsurgery period, PDFF decreased significantly from to 8 () and cT1 from to (). These improvements continued to the later postsurgery period. Bariatric surgery did not reduce liver stiffness measurements. Conclusion. Our findings support using MRI as a noninvasive tool to monitor NAFLD in patient with morbid obesity during the early stages following bariatric surgery.
In Vivo Fluorine Imaging Using 1.5 Tesla MRI for Depiction of Experimental Myocarditis in a Rodent Animal Model
The usefulness of perfluorocarbon nanoemulsions for the imaging of experimental myocarditis has been demonstrated in a high-field 9.4 Tesla MRI scanner. Our proof-of-concept study investigated the imaging capacity of PFC-based 19F/1H MRI in an animal myocarditis model using a clinical field strength of 1.5 Tesla. To induce experimental myocarditis, five male rats (weight ~300 g, age ~50 days) were treated with one application per week of doxorubicin (2 mg/kg BW) over a period of six weeks. Three control animals received the identical volume of sodium chloride 0.9% instead. Following week six, all animals received a single 4 ml injection of an 20% oil-in-water perfluorooctylbromide nanoemulsion 24 hours prior to in vivo1H/19F imaging on a 1.5 Tesla MRI. After euthanasia, cardiac histology and immunohistochemistry using CD68/ED1 macrophage antibodies were performed, measuring the inflamed myocardium in μm2 for further statistical analysis to compare the extent of the inflammation with the 19F-MRI signal intensity. All animals treated with doxorubicin showed a specific signal in the myocardium, while no myocardial signal could be detected in the control group. Additionally, the doxorubicin group showed a significantly higher SNR for 19F and a stronger CD68/ED1 immunhistoreactivity compared to the control group. This proof-of-concept study demonstrates that perfluorocarbon nanoemulsions could be detected in an in vivo experimental myocarditis model at a currently clinically relevant field strength.