ICD lead and primary metal artifact detection and inpainting in cardiac CT images.

Background: Quantifying cardiac motion on pre-treatment CT imaging for stereotactic arrhythmia radiotherapy patients is difficult due to image artifacts caused by metal leads of implantable cardioverter-defibrillators (ICDs). The CT scanners' onboard metal artifact reduction tool does not sufficiently reduce these artifacts. More advanced artifact reduction techniques require the raw CT projection data and thus do not apply to already reconstructed CT images.

A vessel bifurcation landmark pair dataset for abdominal CT deformable image registration (DIR) validation.

Purpose: Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. DIRs of intra-patient abdominal CTs are among the most challenging registration scenarios due to significant organ deformations and inconsistent image content.

Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.

Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.

A Vessel Bifurcation Landmark Pair Dataset for Abdominal CT Deformable Image Registration (DIR) Validation.

Purpose: Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. DIRs of intra-patient abdominal CTs are among the most challenging registration scenarios due to significant organ deformations and inconsistent image content.

Real-time 3D MR guided radiation therapy through orthogonal MR imaging and manifold learning.

Background: In magnetic resonance image (MRI)-guided radiotherapy (MRgRT), 2D rapid imaging is commonly used to track moving targets with high temporal frequency to minimize gating latency. However, anatomical motion is not constrained to 2D, and a portion of the target may be missed during treatment if 3D motion is not evaluated. While some MRgRT systems attempt to capture 3D motion by sequentially tracking motion in 2D orthogonal imaging planes, this approach assesses 3D motion via independent 2D measurements at alternating instances, lacking a simultaneous 3D motion assessment in both imaging planes.

Fast motion-compensated reconstruction for 4D-CBCT using deep learning-based groupwise registration.

. Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations.

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT.

A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms.

Purpose: Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library.

Small metal artifact detection and inpainting in cardiac CT images.

Background: Quantification of cardiac motion on pre-treatment CT imaging for stereotactic arrhythmia radiotherapy patients is difficult due to the presence of image artifacts caused by metal leads of implantable cardioverter-defibrillators (ICDs). The CT scanners' onboard metal artifact reduction tool does not sufficiently reduce these artifacts. More advanced artifact reduction techniques require the raw CT projection data and thus are not applicable to already reconstructed CT images.

A comprehensive lung CT landmark pair dataset for evaluating deformable image registration algorithms.

Purpose: Deformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high-quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations.

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Background: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT).

Purpose: This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT.

Methods: Fourteen healthy volunteers (Group A) were scanned on a 0.

A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

Purpose: Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep-learning approach to generate NCECT directly from CECT.

Implementation of a knowledge-based decision support system for treatment plan auditing through automation.

Background: Independent auditing is a necessary component of a comprehensive quality assurance (QA) program and can also be utilized for continuous quality improvement (QI) in various radiotherapy processes. Two senior physicists at our institution have been performing a time intensive manual audit of cross-campus treatment plans annually, with the aim of further standardizing our planning procedures, updating policies and guidelines, and providing training opportunities of all staff members.

Purpose: A knowledge-based automated anomaly-detection algorithm to provide decision support and strengthen our manual retrospective plan auditing process was developed.

Respiratory motion management using a single rapid MRI scan for a 0.35 T MRI-Linac system.

Background: MRI has a rapidly growing role in radiation therapy (RT) for treatment planning, real-time image guidance, and beam gating (e.g., MRI-Linac).

Contour interpolation by deep learning approach.

Purpose: Contour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in the gaps, therefore saving time and efforts. The most used conventional shape-based interpolation (SBI) algorithm, which operates on shape information, often performs suboptimally near the superior and inferior borders of organs and for the gastrointestinal structures.

Deep learning-based motion compensation for four-dimensional cone-beam computed tomography (4D-CBCT) reconstruction.

Background: Motion-compensated (MoCo) reconstruction shows great promise in improving four-dimensional cone-beam computed tomography (4D-CBCT) image quality. MoCo reconstruction for a 4D-CBCT could be more accurate using motion information at the CBCT imaging time than that obtained from previous 4D-CT scans. However, such data-driven approaches are hampered by the quality of initial 4D-CBCT images used for motion modeling.

Real-time B compensation during gantry rotation in a 0.35-T MRI-Linac.

Background: Rotation of the ferromagnetic gantry of a low magnetic field MRI-Linac was previously demonstrated to cause large center frequency offsets of ±400 Hz. The B off-resonances cause image artifacts and imaging isocenter shifts that would preclude MRI-guided arc therapy.

Purpose: The purpose of this study was to measure and compensate for center frequency offsets in real time during gantry rotation on a 0.

A reconstruction approach for proton computed tomography by modeling the integral depth dose of the scanning proton pencil beam.

Purpose: To present a proton computed tomography (pCT) reconstruction approach that models the integral depth dose (IDD) of the clinical scanning proton beam into beamlets. Using a multilayer ionization chamber (MLIC) as the imager, the proposed pCT system and the reconstruction approach can minimize extra ambient neutron dose and simplify the beamline design by eliminating an additional collimator to confine the proton beam.

Methods: Monte Carlo simulation was applied to digitally simulate the IDDs of the exiting proton beams detected by the MLIC.

Technical Note: Effects of rotating gantry on magnetic field and eddy currents in 0.35 T MRI-guided radiotherapy (MR-IGRT) system.

Purpose: The purpose of this study was to identify the cause of severe image artifacts that occurred during gantry rotation in a 0.35 T MRI-Linac by comparing measurements of eddy currents, center frequency, and field inhomogeneities made with the gantry in motion and stationary.

Methods: Gradient and B eddy currents were calculated from the free induction decays (FIDs) resulting from selective excitation at a temporal resolution of 200 ms/measurement.

Development of a storage phosphor imaging system for proton pencil beam spot profile determination.

Purpose: Accurate two-dimensional (2D) profile measurements at submillimeter precision are necessary for proton beam commissioning and periodic quality assurance (QA) purposes and are currently performed at our institution with a commercial scintillation detector (Lynx PT) with limited means for independent checks. The purpose of this work was to create an independent dosimetry system consisting of an in-house optical scanner and a BaFBrI:Eu storage phosphor dosimeter by: (a) determining the optimal settings for the optical scanner, (b) measuring 2D proton spot profiles with the storage phosphors, and (c) comparing them to similar measurements using a commercial scintillation detector.

Methods: An in-house 2D laboratory optical scanner was constructed and spatially calibrated for accurate 2D photostimulated luminescence (PSL) dosimetry.

Effects of B eddy currents on imaging isocenter shifts in 0.35-T MRI-guided radiotherapy (MR-IGRT) system.

Purpose: The purpose of this study was to measure gantry angle-related eddy currents in a 0.35-T MRI-Linac and determine if B (zeroth order) eddy currents are the primary cause of gantry angle-dependent imaging isocenter shifts vs other potential causes like B inhomogeneities and gradient (first order) eddy currents. For conventional Cartesian acquisitions, B eddy currents can cause imaging isocenter shifts along both phase encode and readout directions.

Dual-storage phosphor proton therapy dosimetry: Simultaneous quantification of dose and linear energy transfer.

Purpose: To investigate the feasibility of using the high Z storage phosphor material BaFBrI:Eu in conjunction with the low Z storage phosphor material KCl:Eu for simultaneous proton dose and linear energy transfer (LET) measurements by (a) measuring the fundamental optical and dosimetric properties of BaFBrI:Eu , (b) evaluating its compatibility in being readout simultaneously with KCl:Eu dosimeters, and (c) modeling and validating its LET dependence under elevated proton LET irradiation.

Methods: A commercial BaFBrI:Eu storage phosphor detector (Model ST-VI, Fujifilm) was characterized with energy dispersive x-ray spectroscopy (EDS) analysis to obtain its elemental composition. The dosimeters were irradiated using both a Mevion S250 proton therapy unit (at the center of a spread-out Bragg peak, SOBP) and a Varian Clinac iX linear accelerator with the latter being a low LET irradiation.

Using prediction models to evaluate magnetic resonance image guided radiation therapy plans.

Comprehensive analysis of daily, online adaptive plan quality and safety in magnetic resonance imaging (MRI) guided radiation therapy is critical to its widespread use. Artificial neural network models developed with offline plans created after simulation were used to analyze and compare online plans that were adapted and reoptimized in real time prior to treatment. Roughly one third of Co adapted plans were of inferior quality relative to fully optimized, offline plans, but MRI-linac adapted plans were essentially equivalent to offline plans.

GroupRegNet: a groupwise one-shot deep learning-based 4D image registration method.

Accurate deformable four-dimensional (4D) (three-dimensional in space and time) medical images registration is essential in a variety of medical applications. Deep learning-based methods have recently gained popularity in this area for the significantly lower inference time. However, they suffer from drawbacks of non-optimal accuracy and the requirement of a large amount of training data.

Factorization Theorem Connecting the Light-Cone Distribution Amplitudes of Heavy-Flavor Mesons in QCD and Heavy-Quark Effective Theory.

The light-cone distribution amplitude (LCDA) of a heavy-light meson defined in heavy quark effective theory (HQET) is a fundamental nonperturbative input to account for innumerable B meson exclusive decay and production processes. On the other hand, the conventional heavy-flavored meson LCDA defined in QCD also ubiquitously enters the factorization formula for hard exclusive B production processes. Inspired by the observation that these two LCDAs exhibit the identical infrared behaviors, yet differ in the ultraviolet scale of order m_{b} or greater, we propose a novel factorization theorem for the heavy-light mesons, that the LCDA defined in QCD can be further expressed as a convolution between the LCDA in HQET and a perturbatively calculable coefficient function thanks to asymptotic freedom.