OpenMandible: An open-source framework for highly realistic numerical modelling of lower mandible physiology.

Objective: Computer modeling of lower mandible physiology remains challenging because prescribing realistic material characteristics and boundary conditions from medical scans requires advanced equipment and skill sets. The objective of this study is to provide a framework that could reduce simplifications made and inconsistency (in terms of geometry, materials, and boundary conditions) among further studies on the topic.

Methods: The OpenMandible framework offers: 1) the first publicly available multiscale model of the mandible developed by combining cone beam computerized tomography (CBCT) and μCT imaging modalities, and 2) a C++ software tool for the generation of simulation-ready models (tet4 and hex8 elements).

Graph convolution network with similarity awareness and adaptive calibration for disease-induced deterioration prediction.

Graph convolution networks (GCN) have been successfully applied in disease prediction tasks as they capture interactions (i.e., edges and edge weights on the graph) between individual elements.

Deep Learning-Based Denoising for High b-Value at 2000 s/mm2 Diffusion-Weighted Imaging.

Diffusion-weighted imaging (DWI) allows white matter quantification of the white matter tracts of the brain. However, at a high b-value (≥ 2000 s/mm2), DWI acquisition suffers from noise due to longer acquisition times obscuring white matter interpretation. DWI denoising techniques can be used to acquire high b-value DWI without increasing the number of signal averages.

Multivariate functional partial least squares for classification using longitudinal data.

The use of statistical methods to predict outcomes using high dimensional datasets in medicine is becoming increasingly popular for forecasting and monitoring patient health. Our work is motivated by a longitudinal dataset containing 1H NMR spectra of metabolites of 18 patients undergoing a kidney transplant alongside their graft outcomes that fall into one of three categories: acute rejection, delayed graft function and primary function. We proposed a functional partial least squares (FPLS) model that extends existing PLS methods for the analysis of longitudinally measured scalar omics datasets to the case of longitudinally measured functional datasets.

Cost-Effective, Integrated In Vitro Phantoms for Cardiac MRI.

To provide lab scale in vitro phantom solutions for cardiac MR (CMR) studies that can be used for imaging structure and function as well as calorimetric measurements. The phantoms were purposed to accept user inputs such as beats per minute (BPM) and flow rate. We developed two generations of phantoms.

An Inexpensive Earth Field NMR Spectrometer with Improved Shimming.

A simple, cost-effective, do-it-yourself earth field nuclear magnetic resonance spectrometer with shimming is demonstrated. A spectrometer for understanding magnetic resonance physics in an academic environment is presented here along with its coil and system component design. This system was designed with inexpensive and readily available electronic components costing less than US $130.

Recovering from missing data in population imaging - Cardiac MR image imputation via conditional generative adversarial nets.

Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes.

Tissue microarray (TMA) use in post mortem neuropathology.

Background: Tissue microarrays (TMAs), where each block (and thus section) contains multiple tissue cores from multiple blocks potentially allow more efficient use of tissue, reagents and time in neuropathology.

New Method: The relationship between data from TMA cores and whole sections was investigated using 'virtual' TMA cores. This involved quantitative assessments of microglial pathology in white matter lesions and motor neuron disease, alongside qualitative TDP-43 inclusion status in motor neuron disease cases.

Groupwise registration with global-local graph shrinkage in atlas construction.

Graph-based groupwise registration methods are widely used in atlas construction. Given a group of images, a graph is built whose nodes represent the images, and whose edges represent a geodesic path between two nodes. The distribution of images on an image manifold is explored through edge traversal in a graph.

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

Purpose: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based on a convolutional neural network (CNN) for CT imaging in SIRT, and the ability of CNN to reduce inter-observer variability of the segmentation.

Methods: A multi-scale CNN was modified for liver segmentation for SIRT patients.

Mechanics of Psoas Tendon Snapping. A Virtual Population Study.

Internal snapping of the psoas tendon is a frequently reported condition, especially in young adolescents involved in sports. It is defined as an increased tendon excursion over bony or soft tissue prominence causing local irritation and inflammation of the tendon leading to groin pain and often is accompanied by an audible snap. Due to the lack of detailed dynamic visualization means, the exact mechanism of the condition remains poorly understood and different theories have been postulated related to the etiology and its location about the hip.

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning.

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment.

Facial soft tissue thicknesses in craniofacial identification: Data collection protocols and associated measurement errors.

Facial soft tissue thicknesses (FSTT) form a key component of craniofacial identification methods, but as for any data, embedded measurement errors are highly pertinent. These in part dictate the effective resolution of the measurements. As herein reviewed, measurement methods are highly varied in FSTT studies and associated measurement errors have generally not been paid much attention.

A Quantitative Evaluation of Joint Activity and Attenuation Reconstruction in TOF PET/MR Brain Imaging.

Time-of-flight (TOF) PET data provide an effective means for attenuation correction (AC) when no (or incomplete or inaccurate) attenuation information is available. Since MR scanners provide little information on photon attenuation of different tissue types, AC in hybrid PET/MR scanners has always been challenging. In this contribution, we aim at validating the activity reconstructions of the maximum-likelihood ordered-subsets activity and attenuation (OSAA) reconstruction algorithm on a patient brain data set.

An overview of the latest developments in facial imaging.

Facial imaging is a term used to describe methods that use facial images to assist or facilitate human identification. This pertains to two craniofacial identification procedures that use skulls and faces-facial approximation and photographic superimposition-as well as face-only methods for age progression/regression, the construction of facial graphics from eyewitness memory (including composites and artistic sketches), facial depiction, face mapping and newly emerging methods of molecular photofitting. Given the breadth of these facial imaging techniques, it is not surprising that a broad array of subject-matter experts participate in and/or contribute to the formulation and implementation of these methods (including forensic odontologists, forensic artists, police officers, electrical engineers, anatomists, geneticists, medical image specialists, psychologists, computer graphic programmers and software developers).

Partial volume and motion correction in cardiac PET: First results from an in vs ex vivo comparison using animal datasets.

Background: In a previous study on ex vivo, static cardiac datasets, we investigated the benefits of performing partial volume correction (PVC) in cardiac F-Fluorodeoxyglucose(FDG) PET datasets. In the present study, we extend the analysis to in vivo cardiac datasets, with the aim of defining which reconstruction technique maximizes quantitative accuracy and, ultimately, makes PET a better diagnostic tool for cardiac pathologies.

Methods: In vivo sheep datasets were acquired and reconstructed with/without motion correction and using several reconstruction algorithms (with/without resolution modeling, with/without non-anatomical priors).

Measuring asymmetry from high-density 3D surface scans: An application to human faces.

Perfect bilateral symmetry is the optimal outcome of the development of bilateral traits in the absence of developmental perturbations. Any random perturbation in this perfect symmetrical state is called Fluctuating Asymmetry (FA). Many studies have been conducted on FA as an indicator of Developmental Instability (DI) and its possible link with stress and individual quality in general and with attractiveness, health and level of masculinity or femininity in humans.

Publisher Correction: Using high-amplitude and focused transcranial alternating current stimulation to entrain physiological tremor.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Using high-amplitude and focused transcranial alternating current stimulation to entrain physiological tremor.

Transcranial alternating current stimulation (tACS) is a noninvasive neuromodulation method that can entrain physiological tremor in healthy volunteers. We conducted two experiments to investigate the effectiveness of high-amplitude and focused tACS montages at entraining physiological tremor. Experiment 1 used saline-soaked sponge electrodes with an extra-cephalic return electrode and compared the effects of a motor (MC) and prefrontal cortex (PFC) electrode location.

Pulseq-Graphical Programming Interface: Open source visual environment for prototyping pulse sequences and integrated magnetic resonance imaging algorithm development.

Purpose: To provide a single open-source platform for comprehensive MR algorithm development inclusive of simulations, pulse sequence design and deployment, reconstruction, and image analysis.

Methods: We integrated the "Pulseq" platform for vendor-independent pulse programming with Graphical Programming Interface (GPI), a scientific development environment based on Python. Our integrated platform, Pulseq-GPI, permits sequences to be defined visually and exported to the Pulseq file format for execution on an MR scanner.

Analysis of partial volume correction on quantification and regional heterogeneity in cardiac PET.

Background: The partial volume correction (PVC) of cardiac PET datasets using anatomical side information during reconstruction is appealing but not straightforward. Other techniques, which do not make use of additional anatomical information, could be equally effective in improving the reconstructed myocardial activity.

Methods: Resolution modeling in combination with different noise suppressing priors was evaluated as a means to perform PVC.

The effect of isoflurane on F-FDG uptake in the rat brain: a fully conscious dynamic PET study using motion compensation.

Background: In preclinical positron emission tomography (PET) studies an anaesthetic is used to ensure that the animal does not move during the scan. However, anaesthesia may have confounding effects on the drug or tracer kinetics under study, and the nature of these effects is usually not known.

Method: We have implemented a protocol for tracking the rigid motion of the head of a fully conscious rat during a PET scan and performing a motion compensated list-mode reconstruction of the data.

An iterative projection-based motion estimation and compensation scheme for head x-ray CT.

Purpose: Although current computed tomography (CT) systems can scan the head in a very short time, patient motion sometimes still induces artifacts. If motion occurs, one has to repeat the scan; to avoid motion, sedation or anesthesia is sometimes applied.

Methods: The authors propose a method to iteratively estimate and compensate this motion during the reconstruction.

Optimising rigid motion compensation for small animal brain PET imaging.

Motion compensation (MC) in PET brain imaging of awake small animals is attracting increased attention in preclinical studies since it avoids the confounding effects of anaesthesia and enables behavioural tests during the scan. A popular MC technique is to use multiple external cameras to track the motion of the animal's head, which is assumed to be represented by the motion of a marker attached to its forehead. In this study we have explored several methods to improve the experimental setup and the reconstruction procedures of this method: optimising the camera-marker separation; improving the temporal synchronisation between the motion tracker measurements and the list-mode stream; post-acquisition smoothing and interpolation of the motion data; and list-mode reconstruction with appropriately selected subsets.