Influence of object location in cone beam computed tomography (NewTom 5G and 3D Accuitomo 170) on gray value measurements at an implant site

Objectives

The aim of this study was to determine the gray value variation at an implant site with different object location within the selected field of view (FOV) in two cone beam computed tomography (CBCT) scanners.

Methods

A 1-cm-thick section from the edentulous region of a dry human mandible was scanned by two CBCT scanners: 3D Accuitomo 170 (J. Morita, Kyoto, Japan) and NewTom 5G (QR Verona, Verona, Italy). Five FOVs were used with each CBCT scanner. Within each FOV, the specimen was located at different positions. The scans were converted to DICOM format. Data analysis was performed using 3Diagnosys (ver. 3.1, 3DIEMME, Cantu, Italy) and Geomagic software (Studio 2012, Morrisville, NC). On one of the scans, a probe designating the site for pre-operative implant placement was selected. The inserted virtual implant was transformed on the same region on each CBCT scan by a three-dimensional registration algorithm. The mean voxel gray value of the region around the probe was derived separately from all CBCT scans. The influence of object location within each FOV on variability of voxel gray values was assessed.

Results

In both CBCT systems, object location had a significant influence on gray value measurements (F _4,16 = 3.71, p = 0.0255 for Accuitomo and F _4,16 = 9.31, p = 0.0000 for NewTom).

Conclusions

Gray level values from CBCT images are influenced by object location within the FOV.     

Exploring the Proposed DSM-5 Criteria in a Clinical Sample

The proposed DSM-5 criteria for Autism Spectrum Disorder (ASD) depart substantially from the previous DSM-IV criteria. In this file review study of 131 children aged 2-12, previously diagnosed with either Autistic Disorder or Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS), 63?% met the new DSM-5 ASD criteria, including 81?% previously diagnosed with Autistic Disorder and only 17?% of those with PDD-NOS. The proportion of children meeting DSM-5 differed by IQ grouping as well, with higher rates in lower IQ groups. Children who did meet criteria for ASD had significantly lower levels of cognitive and adaptive skills and greater autism severity but were similar in age. These findings raise concerns that the new DSM-5 criteria may miss a number of children who would currently receive a diagnosis.     

Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study

In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient’s scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.