Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Models, Anatomic”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13

Space Technology - Game Changing Development NASA Facts: Autonomous Medical Operations

The AMO (Autonomous Medical Operations) Project is working extensively to train medical models on the reliability and confidence of computer-aided interpretation of ultrasound images in various clinical settings, and of various anatomical structures. AI (Artificial Intelligence) algorithms recognize and classify features in the ultrasound images, and these are compared to those features that clinicians use to diagnose diseases. The acquisition of clinically validated image assessment and the use of the AI algorithms constitutes fundamental baseline for a Medical Decision Support System that will advise crew on long-duration, remote missions.

Autonomous Medical Operations Project↗

Arm and shoulder muscle segmentation in axial MRI with UNet deep learning model

Quantifying individual upper-limb muscle volumes from MRI provides key insight into muscle-specific strength, deficits, and adaptations. Manual delineation is the gold standard but time‑intensive, and the performance of current deep learning approaches, particularly for small or anatomically complex muscles, remains incompletely characterized. We evaluated a state‑of‑the‑art deep learning framework across the entire upper limb and analyzed factors governing segmentation performance, with attention to the forearm. Three previously published MRI datasets (1.5 T, 3D GRE T1‑weighted; total n = 39) spanning young, middle‑aged, and older adults were curated and quality‑checked, including expert manual segmentations for 31 muscles. Following multiclass mask reconstruction, we trained three 3D nnU‑Net multiclass models matched to the muscle subsets present across datasets, using five‑fold cross‑validation and a composite Dice Similarity Coefficient (DSC) + cross entropy loss. Segmentation accuracy was assessed with DSC. Performance varied across muscles (mean DSC = 0.806 ± 0.098), ranging from 0.920 (Deltoid) to 0.461 (Extensor pollicis brevis). In uncertainty‑weighted regressions, muscle volume was positively associated with DSC (R2 = 0.36, p < 0.001), whereas training segmentation count and muscle orientation showed negligible associations (R2 ≤ 0.06). A weighted mixed‑effects model identified volume as the strongest evaluated predictor, explaining 23.9% of variance in DSC; orientation and training count each contributed <1%, leaving 61.5% unexplained. These results indicate that deep learning–based segmentation can accurately quantify muscle volume for many upper‑limb muscles but remains constrained for small, low‑contrast forearm muscles.

Gillespie, Samuel↗

A Logical Model of Homology for Comparative Biology

There is a growing body of research on the evolution of anatomy in a wide variety of organisms. Discoveries in this field could be greatly accelerated by computational methods and resources that enable these findings to be compared across different studies and different organisms and linked with the genes responsible for anatomical modifications. Homology is a key concept in comparative anatomy; two important types are historical homology (the similarity of organisms due to common ancestry) and serial homology (the similarity of repeated structures within an organism). We explored how to most effectively represent historical and serial homology across anatomical structures to facilitate computational reasoning. We assembled a collection of homology assertions from the literature with a set of taxon phenotypes for the skeletal elements of vertebrate fins and limbs from the Phenoscape Knowledgebase. Using seven competency questions, we evaluated the reasoning ramifications of two logical models: the Reciprocal Existential Axioms (REA) homology model and the Ancestral Value Axioms (AVA) homology model. The AVA model returned all user-expected results in addition to the search term and any of its subclasses. The AVA model also returns any superclass of the query term in which a homology relationship has been asserted. The REA model returned the user-expected results for five out of seven queries. We identify some challenges of implementing complete homology queries due to limitations of OWL reasoning. This work lays the foundation for homology reasoning to be incorporated into other ontology-based tools, such as those that enable synthetic supermatrix construction and candidate gene discovery.

59 BASIC BIOLOGICAL SCIENCES↗

Organ localization: Toward prospective patient-specific organ dosimetry in computed tomography

Purpose: With increased focus on radiation dose from medical imaging, prospective radiation dose estimates are becoming increasingly desired. Using available populations of adult and pediatric patient phantoms, radiation dose calculations can be catalogued and prospectively applied to individual patients that best match certain anatomical characteristics. In doing so, the knowledge of organ size and location is a required element. Here, the authors develop a predictive model of organ locations and volumes based on an analysis of adult and pediatric computed tomography (CT) data. Methods: Fifty eight adult and 69 pediatric CT datasets were segmented and utilized in the study. The maximum and minimum points of the organs were recorded with respect to the axial distance from the tip of the sacrum. The axial width, midpoint, and volume of each organ were calculated. Linear correlations between these three organ parameters and patient age, BMI, weight, and height were determined. Results: No statistically significant correlations were found in adult patients between the axial width, midpoint, and volume of the organs versus the patient age or BMI. Slight, positive linear trends were found for organ midpoint versus patient weight (max r{sup 2} = 0.382, mean r{sup 2} = 0.236). Similar trends were found for organ midpoint versus height (max r{sup 2} = 0.439, mean r{sup 2} = 0.200) and for organ volume versus height (max r{sup 2} = 0.410, mean r{sup 2} = 0.153). Gaussian fits performed on probability density functions of the adult organs resulted in r{sup 2}-values ranging from 0.96 to 0.996. The pediatric patients showed much stronger correlations overall. Strong correlations were observed between organ axial midpoint versus age, height, and weight (max r{sup 2} = 0.842, mean r{sup 2} = 0.790; max r{sup 2} = 0.949, mean r{sup 2} = 0.894; and max r{sup 2} = 0.870, mean r{sup 2} = 0.847, respectively). Moderate linear correlations were also observed for organ axial width versus height (max r{sup 2} = 0.772, mean r{sup 2} = 0.562) and for organ volume versus height (max r{sup 2} = 0.781, mean r{sup 2} = 0.601). Conclusions: Adult patients exhibited small variations in organ volume and location with respect to height and weight, but no meaningful correlation existed between these parameters and age or BMI. Once adulthood is reached, organ morphology and positioning seem to remain static. However, clear trends are evident between pediatric organ locations versus age, height, and weight. Such information can be incorporated into a matching methodology that may provide the highest probability of representing the anatomy of a patient undergoing a clinical exam to prospectively estimate the radiation dose.

60 APPLIED LIFE SCIENCES↗

Introduction to a Virtual Issue on root traits

Plant traits – ‘morphological, anatomical, physiological, biochem-ical and phenological characteristics of plants and their organs’(Kattge et al., 2011) – are increasingly being harnessed byempiricists and modelers as a framework to understand patternsin the structure and function of specie s across the globe. Trait-basedecology, which emphasizes functional traits over the taxonomicalrelationships among organisms (Laliberte, 2017), promises toimprove generality, synthesis, and predictive ability across ecolog-ical scales (Shipley et al., 2016). Indeed, plant trait studies areincreasingly prominent in the literature: a simple Web of Sciencesearch on the term indicates a surge in publications from 2576during the three-year period from 1999 to 2001 to 13 234 in thethree-year period between 2014 and 2016. However, the mostcommon plant traits described in the literature relate to above -ground organs and their function, including leaf morphology,photosynthetic parameters, and above ground growth rate. Roottraits, particularly those of fine roots associated with criticalbelowground plant functions, are much less studied – they are, afterall, harder to measure and less likely to have a role in ecosystemmodels as they are encoded today. Although the TRY database ofplant traits (Kattge et al., 2011) has been a highly valuable resourcefor plant and ecosystem ecologists, < 1% of the data entries describefine-root functional traits. This glaring gap in our knowledge of thebelowground half of ecosystem function has led to a chorus of pleasin recent years for a stronger emphasis on the measurement andunderstanding of root traits (e.g . Bardgett et al., 2014).

59 BASIC BIOLOGICAL SCIENCES↗

The FaceBase Consortium: A comprehensive resource for craniofacial researchers

The FaceBase Consortium, funded by the National Institute of Dental and Craniofacial Research, National Institutes of Health, is designed to accelerate understanding of craniofacial developmental biology by generating comprehensive data resources to empower the research community, exploring high-throughput technology, fostering new scientific collaborations among researchers and human/computer interactions, facilitating hypothesis-driven research, and translating science into improved health care to benefit patients. The resources generated by the FaceBase projects include a number of dynamic imaging modalities, genome-wide association studies, software tools for analyzing human facial abnormalities, detailed phenotyping, anatomical and molecular atlases, global and specific gene expression patterns, and transcriptional profiling over the course of embryonic and postnatal development in animal models and humans. The integrated data visualization tools, faceted search infrastructure, and curation provided by the FaceBase Hub offer flexible and intuitive ways to interact with these multidisciplinary data. In parallel, the datasets also offer unique opportunities for new collaborations and training for researchers coming into the field of craniofacial studies. Here we highlight the focus of each spoke project and the integration of datasets contributed by the spokes to facilitate craniofacial research.

60 APPLIED LIFE SCIENCES↗

Biomechanical Comparison of Anatomic Restoration of the Ulnar Footprint vs Traditional Ulnar Tunnels in Ulnar Collateral Ligament Reconstruction

Background: Current techniques for ulnar collateral ligament (UCL) reconstruction do not reproduce the anatomic ulnar footprint of the UCL. The purpose of this study was to describe a novel UCL reconstruction technique that utilizes proximal-to-distal ulnar bone tunnels to better re-create the anatomy of the UCL and to compare the biomechanical profile at time zero among this technique, the native UCL, and the traditional docking technique. Hypothesis: The biomechanical profile of the anatomic technique is similar to the native UCL and traditional docking technique. Study Design: Controlled laboratory study. Methods: Ten matched cadaveric elbows were potted with the forearm in neutral rotation. The palmaris longus tendon graft was harvested, and bones were sectioned 14 cm proximal and distal to the elbow joint. Specimen testing included (1) native UCL testing performed at 90° of flexion with 0.5 N·m of valgus moment preload, (2) cyclic loading from 0.5 to 5 N·m of valgus moment for 1000 cycles at 1 Hz, and (3) load to failure at 0.2 mm/s. Elbows then underwent UCL reconstruction with 1 elbow of each pair receiving the classic docking technique using either anatomic (proximal to distal) or traditional (anterior to posterior) tunnel locations. Specimen testing was then repeated as described. Results: There were no differences in maximum load at failure between the anatomic and traditional tunnel location techniques (mean ± SD, 34.90 ± 10.65 vs 37.28 ± 14.26 N·m; P = .644) or when including the native UCL (45.83 ± 17.03 N·m; P = .099). Additionally, there were no differences in valgus angle after 1000 cycles across the anatomic technique (4.58°± 1.47°), traditional technique (4.08°± 1.28°), and native UCL (4.07°± 1.99°). The anatomic group and the native UCL had similar valgus angles at failure (24.13°± 5.86° vs 20.13°± 5.70°; P = .083), while the traditional group had a higher valgus angle at failure when compared with the native UCL (24.88°± 6.18° vs 19.44°± 5.86°; P = .015). Conclusion: In this cadaveric model, UCL reconstruction with the docking technique utilizing proximal-to-distal ulnar tunnels better restored the ulnar footprint while providing valgus stability comparable with reconstruction with the docking technique using traditional anterior-to-posterior ulnar tunnel locations. These results suggest that utilization of the anatomic tunnel location in UCL reconstruction has similar biomechanical properties to the traditional method at the time of initial fixation (ie, not accounting for healing after reconstruction in vivo) while keeping the ulnar tunnels farther from the ulnar nerve. Further studies are warranted to determine if an anatomically based UCL reconstruction results in differing outcomes than traditional reconstruction techniques. Clinical Relevance: Current UCL reconstruction techniques do not accurately re-create the ulnar UCL footprint. The UCL is a dynamic constraint to valgus loads at the elbow, and a more anatomic reconstruction may afford more natural joint kinematics. This more anatomic technique performs similarly to the traditional docking technique at time zero, and the results of this study may offer a starting point for future in vivo studies.

Chang, Edward S.↗

Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

Juhong, Aniwat↗

Commentary: Duckweeds as model organisms for metabolic studies

Duckweeds have many practical applications, for example in human nutrition, as animal feed, in the production of bioplastics or vaccines, and phytoremediation (Acosta et al., 2021). Under most conditions, they reproduce asexually which provides genetically uniform material with predictable patterns of growth that make them ideal as sentinel organisms for phytotoxicity testing (Park et al., 2021). Asexual growth also results in high biomass production which makes duckweeds promising candidates as biofuel feedstocks (Acosta et al., 2021; Liang et al., 2023). In addition, duckweed species like Lemna minor and Spirodela polyrhiza are also reemerging as model organisms in plant biology as high-quality full genome assemblies and other genomic resources become available (Chang et al., 2016; Acosta et al., 2021). We argue that duckweed species are particularly of interest for the study of primary plant metabolism. Primary metabolism concerns the part of metabolism that is directly involved in the growth and development of plants, and which tends to be highly conserved among plant species. What makes duckweeds particularly attractive is that when grown on liquid media more precise control of physiological conditions can be attained relative to growth of plants in soil. Also, due to their relatively simple anatomical structure and asexual reproduction of fronds by budding, precise characterization of the physiological state under study is possible through one simple metric, i.e., the specific growth rate (rate of dry weight increase per existing dry weight), which can be incorporated relatively easily into metabolic models. This is not possible for land plants, such as Arabidopsis, because over the course of their life cycle, they go through multiple growth stages and phases of anatomical differentiation, which are much more complex to quantify. Furthermore, duckweeds can grow on organic substrates under heterotrophic or photomixotrophic conditions that facilitate isotope tracer studies. For example, in a previous study on duckweed by one of the authors, Lemna gibba (L). was grown on glucose with a position-specific 13 C-label that can be detected and resolved by Mass Spectrometry or Nuclear Magnetic Resonance spectrometry. Using this approach, the 13 C-label was traced into biomass compounds formed from glucose, particularly isoprenoid compounds. Some of the resulting labeling patterns were in apparent disagreement with predictions based on known metabolic pathways for the biosynthesis of isopentenyl pyrophosphate, the universal building block for isoprenoids, (Lichtenthaler et al., 1997). From this data it was deduced that isoprenoid compounds such as carotenoids and isoprenoid chains of phytol and plastoquinone, synthesized in the chloroplast, are produced via a previously unreported plant metabolic pathway, now known as the methylerythitol/deoxyxylulose-5-phosphate pathway (Lichtenthaler et al., 1997).

59 BASIC BIOLOGICAL SCIENCES↗

Finite Element Modeling of the Posterior Eye in Microgravity

Microgravity experienced during spaceflight affects astronauts in various ways, including weakened muscles and loss of bone density. Recently, visual impairment and intracranial pressure (VIIP) syndrome has become a major concern for space missions lasting longer than 30 days. Astronauts suffering from VIIP syndrome have changes in ocular anatomical and visual impairment that persist after returning to earth. It is hypothesized that a cephalad fluid shift in microgravity may increase the intracranial pressure (ICP), which leads to an altered biomechanical environment of the posterior globe and optic nerve sheath (ONS).Currently, there is a lack of knowledge of how elevated ICP may lead to vision impairment and connective tissue changes in VIIP. Our goal was to develop a finite element model to simulate the acute effects of elevated ICP on the posterior eye and optic nerve sheath. We used a finite element (FE) analysis approach to understand the response of the lamina cribrosa and optic nerve to the elevations in ICP thought to occur in microgravity and to identify which tissue components have the greatest impact on strain experienced by optic nerve head tissues.

intraocular pressure↗

An Integrated Biomechanical Model for Microgravity-Induced Visual Impairment

When gravitational unloading occurs upon entry to space, astronauts experience a major shift in the distribution of their bodily fluids, with a net headward movement. Measurements have shown that intraocular pressure spikes, and there is a strong suspicion that intracranial pressure also rises. Some astronauts in both short- and long-duration spaceflight develop visual acuity changes, which may or may not reverse upon return to earth gravity. To date, of the 36 U.S. astronauts who have participated in long-duration space missions on the International Space Station, 15 crew members have developed minor to severe visual decrements and anatomical changes. These ophthalmic changes include hyperopic shift, optic nerve distension, optic disc edema, globe flattening, choroidal folds, and elevated cerebrospinal fluid pressure. In order to understand the physical mechanisms behind these phenomena, NASA is developing an integrated model that appropriately captures whole-body fluids transport through lumped-parameter models for the cerebrospinal and cardiovascular systems. This data feeds into a finite element model for the ocular globe and retrobulbar subarachnoid space through time-dependent boundary conditions. Although tissue models and finite element representations of the corneo-scleral shell, retina, choroid and optic nerve head have been integrated to study pathological conditions such as glaucoma, the retrobulbar subarachnoid space behind the eye has received much less attention. This presentation will describe the development and scientific foundation of our holistic model.

Nelson, Emily S.↗

A limited-angle intrafraction verification (LIVE) system for radiation therapy

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beam's eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Three-dimensional visual guidance improves the accuracy of calculating right ventricular volume with two-dimensional echocardiography

Three-dimensional guidance programs have been shown to increase the reproducibility of 2-dimensional (2D) left ventricular volume calculations, but these systems have not been tested in 2D measurements of the right ventricle. Using magnetic fields to identify the probe location, we developed a new 3-dimensional guidance system that displays the line of intersection, the plane of intersection, and the numeric angle of intersection between the current image plane and previously saved scout views. When used by both an experienced and an inexperienced sonographer, this guidance system increases the accuracy of the 2D right ventricular volume measurements using a monoplane pyramidal model. Furthermore, a reconstruction of the right ventricle, with a computed volume similar to the calculated 2D volume, can be displayed quickly by tracing a few anatomic structures on 2D scans.

Non-NASA Center↗

SU-E-J-111: Finite Element-Based Deformable Image Registration of Pleural Cavity for Photodynamic Therapy

Purpose: The pleural volumes will deform during surgery portion of the pleural photodynamic therapy (PDT) of lung cancer when the pleural cavity is opened. This impact the delivered dose when using highly conformal treatment techniques. In this study, a finite element-based (FEM) deformable image registration is used to quantify the anatomical variation between the contours for the pleural cavities obtained in the operating room and those determined from pre-surgery computed tomography (CT) scans. Methods: An infrared camera-based navigation system (NDI) is used during PDT to track the anatomical changes and contour the lung and chest cavity. A series of CTs of the lungs, in the same patient, are also acquired before the surgery. The structure contour of lung and the CTs are processed and contoured in Matlab and MeshLab. Then, the contours are imported into COMSOL Multiphysics 5.0, where the FEM-based deformable image registration is obtained using the deformed mesh - moving mesh (ALE) model. The NDI acquired lung contour is considered as the reference contour, and the CT contour is used as the target one, which will be deformed. Results: The reconstructed three-dimensional contours from both NDI and CT can be converted to COMSOL so that a three-dimensional ALE model can be developed. The contours can be registered using COMSOL ALE moving mesh model, which takes into account the deformation along x, y and z-axes. The deformed contour has good matches to the reference contour after the dynamic matching process. The resulting 3D deformation map can be used to obtain the locations of other critical anatomic structures, e.g., heart, during surgery. Conclusion: Deformable image registration can fuse images acquired by different modalities. It provides insights into the development of phenomenon and variation in normal anatomical structures over time. The initial assessments of three-dimensional registration show good agreement.

60 APPLIED LIFE SCIENCES↗

TH-E-202-00: PET for Radiation Therapy

PET/CT is a very important imaging tool in the management of oncology patients. PET/CT has been applied for treatment planning and response evaluation in radiation therapy. This educational session will discuss: Pitfalls and remedies in PET/CT imaging for RT planning The use of hypoxia PET imaging for radiotherapy PET for tumor response evaluation The first presentation will address the issue of mis-registration between the CT and PET images in the thorax and the abdomen. We will discuss the challenges of respiratory gating and introduce an average CT technique to improve the registration for dose calculation and image-guidance in radiation therapy. The second presentation will discuss the use of hypoxia PET Imaging for radiation therapy. We will discuss various hypoxia radiotracers, the choice of clinical acquisition protocol (in particular a single late static acquisition versus a dynamic acquisition), and the compartmental modeling with different transfer rate constants explained. We will demonstrate applications of hypoxia imaging for dose escalation/de-escalation in clinical trials. The last presentation will discuss the use of PET/CT for tumor response evaluation. We will discuss anatomic response assessment vs. metabolic response assessment, visual evaluation and semi-quantitative evaluation, and limitations of current PET/CT assessment. We will summarize clinical trials using PET response in guiding adaptive radiotherapy. Finally, we will summarize recent advancements in PET/CT radiomics and non-FDG PET tracers for response assessment. Learning Objectives: Identify the causes of mis-registration of CT and PET images in PET/CT, and review the strategies to remedy the issue. Understand the basics of PET imaging of tumor hypoxia (radiotracers, how PET measures the hypoxia selective uptake, imaging protocols, applications in chemo-radiation therapy). Understand the basics of dynamic PET imaging, compartmental modeling and parametric images. Understand the basics of using FDG PET/CT for tumor response evaluation. Learn about recent advancement in PET/CT radiomics and non-FDG PET tracers for response assessment. This work was supported in part by the National Cancer Institute Grants R01CA172638.; W. Lu, This work was supported in part by the National Cancer Institute Grants R01CA172638.

60 APPLIED LIFE SCIENCES↗

TH-E-202-01: Pitfalls and Remedies in PET/CT Imaging for RT Planning

PET/CT is a very important imaging tool in the management of oncology patients. PET/CT has been applied for treatment planning and response evaluation in radiation therapy. This educational session will discuss: Pitfalls and remedies in PET/CT imaging for RT planning The use of hypoxia PET imaging for radiotherapy PET for tumor response evaluation The first presentation will address the issue of mis-registration between the CT and PET images in the thorax and the abdomen. We will discuss the challenges of respiratory gating and introduce an average CT technique to improve the registration for dose calculation and image-guidance in radiation therapy. The second presentation will discuss the use of hypoxia PET Imaging for radiation therapy. We will discuss various hypoxia radiotracers, the choice of clinical acquisition protocol (in particular a single late static acquisition versus a dynamic acquisition), and the compartmental modeling with different transfer rate constants explained. We will demonstrate applications of hypoxia imaging for dose escalation/de-escalation in clinical trials. The last presentation will discuss the use of PET/CT for tumor response evaluation. We will discuss anatomic response assessment vs. metabolic response assessment, visual evaluation and semi-quantitative evaluation, and limitations of current PET/CT assessment. We will summarize clinical trials using PET response in guiding adaptive radiotherapy. Finally, we will summarize recent advancements in PET/CT radiomics and non-FDG PET tracers for response assessment. Learning Objectives: Identify the causes of mis-registration of CT and PET images in PET/CT, and review the strategies to remedy the issue. Understand the basics of PET imaging of tumor hypoxia (radiotracers, how PET measures the hypoxia selective uptake, imaging protocols, applications in chemo-radiation therapy). Understand the basics of dynamic PET imaging, compartmental modeling and parametric images. Understand the basics of using FDG PET/CT for tumor response evaluation. Learn about recent advancement in PET/CT radiomics and non-FDG PET tracers for response assessment. This work was supported in part by the National Cancer Institute Grants R01CA172638.; W. Lu, This work was supported in part by the National Cancer Institute Grants R01CA172638.

60 APPLIED LIFE SCIENCES↗

TH-E-202-02: The Use of Hypoxia PET Imaging for Radiotherapy

PET/CT is a very important imaging tool in the management of oncology patients. PET/CT has been applied for treatment planning and response evaluation in radiation therapy. This educational session will discuss: Pitfalls and remedies in PET/CT imaging for RT planning The use of hypoxia PET imaging for radiotherapy PET for tumor response evaluation The first presentation will address the issue of mis-registration between the CT and PET images in the thorax and the abdomen. We will discuss the challenges of respiratory gating and introduce an average CT technique to improve the registration for dose calculation and image-guidance in radiation therapy. The second presentation will discuss the use of hypoxia PET Imaging for radiation therapy. We will discuss various hypoxia radiotracers, the choice of clinical acquisition protocol (in particular a single late static acquisition versus a dynamic acquisition), and the compartmental modeling with different transfer rate constants explained. We will demonstrate applications of hypoxia imaging for dose escalation/de-escalation in clinical trials. The last presentation will discuss the use of PET/CT for tumor response evaluation. We will discuss anatomic response assessment vs. metabolic response assessment, visual evaluation and semi-quantitative evaluation, and limitations of current PET/CT assessment. We will summarize clinical trials using PET response in guiding adaptive radiotherapy. Finally, we will summarize recent advancements in PET/CT radiomics and non-FDG PET tracers for response assessment. Learning Objectives: Identify the causes of mis-registration of CT and PET images in PET/CT, and review the strategies to remedy the issue. Understand the basics of PET imaging of tumor hypoxia (radiotracers, how PET measures the hypoxia selective uptake, imaging protocols, applications in chemo-radiation therapy). Understand the basics of dynamic PET imaging, compartmental modeling and parametric images. Understand the basics of using FDG PET/CT for tumor response evaluation. Learn about recent advancement in PET/CT radiomics and non-FDG PET tracers for response assessment. This work was supported in part by the National Cancer Institute Grants R01CA172638.; W. Lu, This work was supported in part by the National Cancer Institute Grants R01CA172638.

60 APPLIED LIFE SCIENCES↗

TH-E-202-03: PET for Tumor Response Evaluation

PET/CT is a very important imaging tool in the management of oncology patients. PET/CT has been applied for treatment planning and response evaluation in radiation therapy. This educational session will discuss: Pitfalls and remedies in PET/CT imaging for RT planning The use of hypoxia PET imaging for radiotherapy PET for tumor response evaluation The first presentation will address the issue of mis-registration between the CT and PET images in the thorax and the abdomen. We will discuss the challenges of respiratory gating and introduce an average CT technique to improve the registration for dose calculation and image-guidance in radiation therapy. The second presentation will discuss the use of hypoxia PET Imaging for radiation therapy. We will discuss various hypoxia radiotracers, the choice of clinical acquisition protocol (in particular a single late static acquisition versus a dynamic acquisition), and the compartmental modeling with different transfer rate constants explained. We will demonstrate applications of hypoxia imaging for dose escalation/de-escalation in clinical trials. The last presentation will discuss the use of PET/CT for tumor response evaluation. We will discuss anatomic response assessment vs. metabolic response assessment, visual evaluation and semi-quantitative evaluation, and limitations of current PET/CT assessment. We will summarize clinical trials using PET response in guiding adaptive radiotherapy. Finally, we will summarize recent advancements in PET/CT radiomics and non-FDG PET tracers for response assessment. Learning Objectives: Identify the causes of mis-registration of CT and PET images in PET/CT, and review the strategies to remedy the issue. Understand the basics of PET imaging of tumor hypoxia (radiotracers, how PET measures the hypoxia selective uptake, imaging protocols, applications in chemo-radiation therapy). Understand the basics of dynamic PET imaging, compartmental modeling and parametric images. Understand the basics of using FDG PET/CT for tumor response evaluation. Learn about recent advancement in PET/CT radiomics and non-FDG PET tracers for response assessment. This work was supported in part by the National Cancer Institute Grants R01CA172638.; W. Lu, This work was supported in part by the National Cancer Institute Grants R01CA172638.

60 APPLIED LIFE SCIENCES↗