Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.

Purpose: The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit.

Materials And Methods: In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients.

Universal 120-kV Dual-Source Ultra-High Pitch Protocol on the Photon-Counting CT System for Pediatric Abdomen of All Sizes: A Phantom Investigation Comparing With Energy-Integrating CT.

Objectives: The purpose of this study is to determine if a universal 120-kV ultra-high pitch and virtual monoenergetic images (VMIs) protocol on the photon-counting computed tomography (PCCT) system can provide sufficient image quality for pediatric abdominal imaging, regardless of size, compared with protocols using a size-dependent kV and dual-source flash mode on the energy-integrating CT (EICT) system.

Materials And Methods: One solid water insert and 3 iodine (2, 5, 10 mg I/mL) inserts were attached or inserted into phantoms of variable sizes, simulating the abdomens of a newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. Each phantom setting was scanned on an EICT using clinical size-specific kV dual-source protocols with a pitch of 3.

Radiation dose reduction using spectral shaping in pediatric non-contrast sinus CT.

Background: CT is the standard imaging technique to evaluate pediatric sinuses. Given the potential risks of radiation exposure in children, it is important to reduce pediatric CT dose and maintain image quality.

Objective: To study the utility of spectral shaping with tin filtration to improve dose efficiency for pediatric sinus CT exams.

Utilization of Mid-Thigh Magnetic Resonance Imaging to Predict Lean Body Mass and Knee Extensor Strength in Obese Adults.

Purpose: To train and test a machine learning model to automatically measure mid-thigh muscle cross-sectional area (CSA) to provide rapid estimation of appendicular lean mass (ALM) and predict knee extensor torque of obese adults.

Methods: Obese adults [body mass index (BMI) = 30-40 kg/m, age = 30-50 years] were enrolled for this study. Participants received full-body dual-energy X-ray absorptiometry (DXA), mid-thigh MRI, and completed knee extensor and flexor torque assessments isokinetic dynamometer.

Abdominal computed tomography localizer image generation: A deep learning approach.

Background And Objective: Computed Tomography (CT) has become an important clinical imaging modality, as well as the leading source of radiation dose from medical imaging procedures. Modern CT exams are usually led by two quick orthogonal localization scans, which are used for patient positioning and diagnostic scan parameter definition. These two localization scans contribute to the patient dose but are not used for diagnosis purposes.

Large Vessel Occlusion Prediction in the Emergency Department with National Institutes of Health Stroke Scale Components: A Machine Learning Approach.

Objective: To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).

Materials And Methods: A retrospective cohort of consecutive ED stroke alerts at a large comprehensive stroke center was analyzed. The primary outcome was diagnosis of LVO at discharge.

Generalized Radiographic View Identification with Deep Learning.

To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) views at different levels was examined with a retrospective, HIPAA-compliant, and IRB-approved study performed on 15,046 radiographic images acquired between 2013 and 2018 from nine clinical sites affiliated with our institution. Images were labeled according to four classification levels: level 1 (anatomy level, 25 classes), level 2 (laterality level, 41 classes), level 3 (projection level, 108 classes), and level 4 (detailed level, 143 classes). An Inception V3 model pre-trained with ImageNet dataset was trained with transfer learning to classify the image at all levels.

Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations. In this study, we investigated an important factor affecting the CT dose-the scan length, for this CT exam. A neural network model based on the "UNET" framework was established to segment the lung region in the CT scout images.

Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging.

Purpose: In partially parallel imaging, most k-space-based reconstruction algorithms such as GRAPPA adopt a single finite-size kernel to approximate the true relationship between sampled and nonsampled signals. However, the estimation of this kernel based on k-space signals is imperfect, and the authors are investigating methods dealing with local variation of k-space signals.

Methods: To model nonstationarity of kernel weights, similar to performing a spatially adaptive regularization, the authors fit a set of linear functions using concepts from geographically weighted regression, a methodology used in geophysical analysis.

X-PROP: a fast and robust diffusion-weighted propeller technique.

Diffusion-weighted imaging (DWI) has shown great benefits in clinical MR exams. However, current DWI techniques have shortcomings of sensitivity to distortion or long scan times or combinations of the two. Diffusion-weighted echo-planar imaging (EPI) is fast but suffers from severe geometric distortion.

A parallel imaging technique using mutual calibration for split-blade diffusion-weighted PROPELLER.

Split-blade diffusion-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (DW-PROPELLER) was proposed to address the issues associated with diffusion-weighted echo planar imaging such as geometric distortion and difficulty in high-resolution imaging. The major drawbacks with DW-PROPELLER are its high SAR (especially at 3T) and violation of the Carr-Purcell-Meiboom-Gill condition, which leads to a long scan time and narrow blade. Parallel imaging can reduce scan time and increase blade width; however, it is very challenging to apply standard k-space-based techniques such as GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) to split-blade DW-PROPELLER due to its narrow blade.

Turboprop IDEAL: a motion-resistant fat-water separation technique.

Suppression of the fat signal in MRI is very important for many clinical applications. Multi-point water-fat separation methods, such as IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation), can robustly separate water and fat signal, but inevitably increase scan time, making separated images more easily affected by patient motions. PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) and Turboprop techniques offer an effective approach to correct for motion artifacts.

Quantitative image quality evaluation of MR images using perceptual difference models.

The authors are using a perceptual difference model (Case-PDM) to quantitatively evaluate image quality of the thousands of test images which can be created when optimizing fast magnetic resonance (MR) imaging strategies and reconstruction techniques. In this validation study, they compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to Case-PDM and similar models. The authors found that Case-PDM compared very favorably to human observers in double-stimulus continuous-quality scale and functional measurement theory studies over a large range of image quality.

Robust GRAPPA reconstruction and its evaluation with the perceptual difference model.

Purpose: To develop and optimize a new modification of GRAPPA (generalized autocalibrating partially parallel acquisitions) MR reconstruction algorithm named "Robust GRAPPA."

Materials And Methods: In Robust GRAPPA, k-space data points were weighted before the reconstruction. Small or zero weights were assigned to "outliers" in k-space.

Methods for quantitative image quality evaluation of MRI parallel reconstructions: detection and perceptual difference model.

Many reconstruction algorithms are being proposed for parallel magnetic resonance imaging (MRI), which uses multiple coils and subsampled k-space data, and a quantitative method for comparison of algorithms is sorely needed. On such images, we compared three methods for quantitative image quality evaluation: human detection, computer detection model and a computer perceptual difference model (PDM). One-quarter sampling and three different reconstruction methods were investigated: a regularization method developed by Ying et al.

Using the Perceptual Difference Model (PDM) to Optimize GRAPPA Reconstruction.

Parallel imaging techniques are being applied in MRI to improve the spatial or temporal resolution. Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is one of the most popular reconstruction techniques in parallel imaging. In GRAPPA, several k-space lines are acquired in addition to the normal subsampled data acquisition.

Application of perceptual difference model on regularization techniques of parallel MR imaging.

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or to improve image SNR. The quality of reconstructed images is limited by the inaccurate estimation of the sensitivity map, noise in the acquired k-space data and the ill-conditioned nature of the coefficient matrix. Tikhonov regularization is a popular method to reduce or eliminate the ill-conditioned nature of the problem.

Optimization of spiral MRI using a perceptual difference model.

We systematically evaluated a variety of MR spiral imaging acquisition and reconstruction schemes using a computational perceptual difference model (PDM) that models the ability of humans to perceive a visual difference between a degraded "fast" MRI image with subsampling of k-space and a "gold standard" image mimicking full acquisition. Human subject experiments performed using a modified double-stimulus continuous-quality scale (DSCQS) correlated well with PDM, over a variety of images. In a smaller set of conditions, PDM scores agreed very well with human detectability measurements of image quality.