Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges.

A small DC magnetic field can induce an enormous response in the impedance of a soft magnetic conductor in various forms of wire, ribbon, and thin film. Also known as the giant magnetoimpedance (GMI) effect, this phenomenon forms the basis for the development of high-performance magnetic biosensors with magnetic field sensitivity down to the picoTesla regime at room temperature. Over the past decade, some state-of-the-art prototypes have become available for trial tests due to continuous efforts to improve the sensitivity of GMI biosensors for the ultrasensitive detection of biological entities and biomagnetic field detection of human activities through the use of magnetic nanoparticles as biomarkers.

Robustness and reproducibility of an artificial intelligence-assisted online segmentation and adaptive planning process for online adaptive radiation therapy.

Clinical implementation of online adaptive radiation therapy requires initial and ongoing performance assessment of the underlying auto-segmentation and adaptive planning algorithms, although a straightforward and efficient process for this in phantom is lacking. The purpose of this work was to investigate robustness and repeatability of the artificial intelligence-assisted online segmentation and adaptive planning process on the Varian Ethos adaptive platform, and to develop an end-to-end test strategy for online adaptive radiation therapy. Five synthetic deformations were generated and applied to a computed tomography image of an anthropomorphic pelvis phantom, and reference treatment plans were generated from each of the resulting deformed images.

Targetability of cervical cancer by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-mediated hyperthermia (HT) for patients receiving radiation therapy.

Purpose: To evaluate the targetability of late-stage cervical cancer by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-induced hyperthermia (HT) as an adjuvant to radiation therapy (RT).

Methods: Seventy-nine cervical cancer patients (stage IIIB-IVA) who received RT with lesions visible on positron emission tomography-computed tomography (PET-CT) were retrospectively analyzed for targetability using a commercially-available HT-capable MRgHIFU system. Targetability was assessed for both primary targets and/or any metastatic lymph nodes using both posterior (supine) and anterior (prone) patient setups relative to the transducer.

Long-term Inter-protocol kV CBCT image quality assessment for a ring-gantry linac via automated QA approach.

We develop a fully automated QA process to compare the image quality of all kV CBCT protocols on a Halcyon linac with ring gantry design, and evaluate image quality stability over a 10-month period. A total of 19 imaging scan and reconstruction protocols were characterized with measurement on a newly released QUART phantom. A set of image analysis algorithms were developed and integrated into an automated analysis suite to derive key image quality metrics, including HU value accuracy on density inserts, HU uniformity using the background plate, high contrast resolution with the modulation transfer function (MTF) from the edge profiles, low contrast resolution using the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), slice thickness with the air gap modules, and geometric accuracy with the diameter of the phantom.

Characterization of magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU)-induced large-volume hyperthermia in deep and superficial targets in a porcine model.

Purpose: To characterize temperature fields and tissue damage profiles of large-volume hyperthermia (HT) induced by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) in deep and superficial targets in a porcine model.

Methods: Nineteen HT sessions were performed with a commercial MRgHIFU system (Sonalleve V2, Profound Medical Inc., Mississauga, ON, Canada) in hind leg muscles of eight pigs with temperature fields of cross-sectional diameter of 58-mm.

Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.

Purpose: Intensity-modulated radiation therapy (IMRT) quality assurance (QA) measurements are routinely performed prior to treatment delivery to verify dose calculation and delivery accuracy. In this work, we applied a machine learning-based approach to predict portal dosimetry based IMRT QA gamma passing rates.

Methods: 182 IMRT plans for various treatment sites were planned and delivered with portal dosimetry on two TrueBeam and two Trilogy LINACs.

Toward adaptive proton therapy guided with a mobile helical CT scanner.

Purpose: To evaluate the feasibility of image-guided adaptive proton therapy (IGAPT) with a mobile helical-CT without rails.

Method: CT images were acquired with a 32-slice mobile CT (mCT) scanning through a 6 degree-of-freedom robotic couch rotated isocentrically 90 degrees from an initial setup position. The relationship between the treatment isocenter and the mCT imaging isocenter was established by a stereotactic reference frame attached to the treatment couch.

Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017.

Purpose: This report presents the methods and results of the Thoracic Auto-Segmentation Challenge organized at the 2017 Annual Meeting of American Association of Physicists in Medicine. The purpose of the challenge was to provide a benchmark dataset and platform for evaluating performance of autosegmentation methods of organs at risk (OARs) in thoracic CT images.

Methods: Sixty thoracic CT scans provided by three different institutions were separated into 36 training, 12 offline testing, and 12 online testing scans.

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

Purpose: Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system.

Unsupervised Feature Learning Classification With Radial Basis Function Extreme Learning Machine Using Graphic Processors.

Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy.

Study of the development of the mouse thoracic aorta three-dimensional macromolecular structure using two-photon microscopy.

Using the intrinsic optical properties of collagen and elastin, two-photon microscopy was applied to evaluate the three-dimensional (3D) macromolecular structural development of the mouse thoracic aorta from birth to 60 days old. Baseline development was established in the Scavenger Receptor Class B Type I-Deficient, Hypomorphic Apolipoprotein ER61 (SR-BI KO/ApoeR61(h/h)) mouse in preparation for modeling atherosclerosis. Precise dissection enabled direct observation of the artery wall in situ.

In vivo microscopy reveals extensive embedding of capillaries within the sarcolemma of skeletal muscle fibers.

Objective: To provide insight into mitochondrial function in vivo, we evaluated the 3D spatial relationship between capillaries, mitochondria, and muscle fibers in live mice.

Methods: 3D volumes of in vivo murine TA muscles were imaged by MPM. Muscle fiber type, mitochondrial distribution, number of capillaries, and capillary-to-fiber contact were assessed.