Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Purpose To develop a machine learning approach for classifying disease progression in chest radiographs using weak labels automatically derived from radiology reports. Materials and Methods In this retrospective study, a twin neural network was developed to classify anatomy-specific disease progression into four categories: improved, unchanged, worsened, and new. A two-step weakly supervised learning approach was employed, pretraining the model on 243 008 frontal chest radiographs from 63 877 patients (mean age, 51.

Quantitative Interstitial Abnormality Progression and Outcomes in the Genetic Epidemiology of COPD and Pittsburgh Lung Screening Study Cohorts.

Background: The risk factors and clinical outcomes of quantitative interstitial abnormality progression over time have not been characterized.

Research Questions: What are the associations of quantitative interstitial abnormality progression with lung function, exercise capacity, and mortality? What are the demographic and genetic risk factors for quantitative interstitial abnormality progression?

Study Design And Methods: Quantitative interstitial abnormality progression between visits 1 and 2 was assessed from 4,635 participants in the Genetic Epidemiology of COPD (COPDGene) cohort and 1,307 participants in the Pittsburgh Lung Screening Study (PLuSS) cohort. We used multivariable linear regression to determine the risk factors for progression and the longitudinal associations between progression and FVC and 6-min walk distance, and Cox regression models for the association with mortality.

Intravoxel incoherent motion analysis of renal allograft diffusion with clinical and histopathological correlation in pediatric kidney transplant patients: A preliminary cross-sectional observational study.

The purpose of this study was to compare IVIM values in pediatric renal transplants with histopathology and clinical management change. Fifteen pediatric renal transplant recipients (mean 15.7±2.

On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners.

Purpose: To propose an MR-based method for generating continuous-valued head attenuation maps and to assess its accuracy and reproducibility. Demonstrating that novel MR-based photon attenuation correction methods are both accurate and reproducible is essential prior to using them routinely in research and clinical studies on integrated PET/MR scanners.

Methods: Continuous-valued linear attenuation coefficient maps ("μ-maps") were generated by combining atlases that provided the prior probability of voxel positions belonging to a certain tissue class (air, soft tissue, or bone) and an MR intensity-based likelihood classifier to produce posterior probability maps of tissue classes.

Quantitative susceptibility mapping by inversion of a perturbation field model: correlation with brain iron in normal aging.

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or "QSIP.

Morphometric differences in planum temporale in schizophrenia and bipolar disorder revealed by statistical analysis of labeled cortical depth maps.

Differences in cortical thickness in the lateral temporal lobe, including the planum temporale (PT), have been reported in MRI studies of schizophrenia (SCZ) and bipolar disorder (BPD) patients. Most of these studies have used a single-valued global or local measure for thickness. However, additional and complementary information can be obtained by generating labeled cortical distance maps (LCDMs), which are distances of labeled gray matter (GM) voxels from the nearest point on the GM/white matter (WM) (inner) cortical surface.

Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners.

We present a new MRI-based attenuation correction (AC) approach for integrated PET/MRI systems that combines both segmentation- and atlas-based methods by incorporating dual-echo ultra-short echo-time (DUTE) and T1-weighted (T1w) MRI data and a probabilistic atlas. Segmented atlases were constructed from CT training data using a leave-one-out framework and combined with T1w, DUTE, and CT data to train a classifier that computes the probability of air/soft tissue/bone at each voxel. This classifier was applied to segment the MRI of the subject of interest and attenuation maps (μ-maps) were generated by assigning specific linear attenuation coefficients (LACs) to each tissue class.

Morphometry of superior temporal gyrus and planum temporale in schizophrenia and psychotic bipolar disorder.

Structural abnormalities in temporal lobe, including the superior temporal gyrus (STG) and planum temporale (PT), have been reported in schizophrenia (SCZ) and bipolar disorder (BPD) patients. While most MRI studies have suggested gray matter volume and surface area reduction in temporal lobe regions, few have explored changes in laminar thickness in PT and STG in SCZ and BPD. ROI subvolumes of the STG from 94 subjects were used to yield gray matter volume, gray/white surface area and laminar thickness for STG and PT cortical regions.

Cortico-cortical, cortico-striatal, and cortico-thalamic white matter fiber tracts generated in the macaque brain via dynamic programming.

Probabilistic methods have the potential to generate multiple and complex white matter fiber tracts in diffusion tensor imaging (DTI). Here, a method based on dynamic programming (DP) is introduced to reconstruct fibers pathways whose complex anatomical structures cannot be resolved beyond the resolution of standard DTI data. DP is based on optimizing a sequentially additive cost function derived from a Gaussian diffusion model whose covariance is defined by the diffusion tensor.

Atlas-based improved prediction of magnetic field inhomogeneity for distortion correction of EPI data.

We describe a method for atlas-based segmentation of structural MRI for calculation of magnetic fieldmaps. CT data sets are used to construct a probabilistic atlas of the head and corresponding MR is used to train a classifier that segments soft tissue, air, and bone. Subject-specific fieldmaps are computed from the segmentations using a perturbation field model.

Fieldmap-free retrospective registration and distortion correction for EPI-based functional imaging.

We describe a method for correcting the distortions present in echo planar images (EPI) and registering the EPI to structural MRI. A fieldmap is predicted from an air / tissue segmentation of the MRI using a perturbation method and subsequently used to unwarp the EPI data. Shim and other missing parameters are estimated by registration.

tGolgin-1 (p230, golgin-245) modulates Shiga-toxin transport to the Golgi and Golgi motility towards the microtubule-organizing centre.

tGolgin-1 (trans-Golgi p230, golgin-245) is a member of a family of large peripheral membrane proteins that associate with the trans-Golgi network (TGN) via a C-terminal GRIP domain. Some GRIP-domain proteins have been implicated in endosome-to-TGN transport but no function for tGolgin-1 has been described. Here, we show that tGolgin-1 production is required for efficient retrograde distribution of Shiga toxin from endosomes to the Golgi.