28 research outputs found
An excursion set model of the cosmic web: The abundance of sheets, filaments and halos
We discuss an analytic approach for modeling structure formation in sheets,
filaments and knots. This is accomplished by combining models of triaxial
collapse with the excursion set approach: sheets are defined as objects which
have collapsed along only one axis, filaments have collapsed along two axes,
and halos are objects in which triaxial collapse is complete. In the simplest
version of this approach, which we develop here, large scale structure shows a
clear hierarchy of morphologies: the mass in large-scale sheets is partitioned
up among lower mass filaments, which themselves are made-up of still lower mass
halos. Our approach provides analytic estimates of the mass fraction in sheets,
filaments and halos, and its evolution, for any background cosmological model
and any initial fluctuation spectrum. In the currently popular CDM
model, our analysis suggests that more than 99% of the cosmic mass is in
sheets, and 72% in filaments, with mass larger than at the
present time. For halos, this number is only 46%. Our approach also provides
analytic estimates of how halo abundances at any given time correlate with the
morphology of the surrounding large-scale structure, and how halo evolution
correlates with the morphology of large scale structure.Comment: 22 pages, 7 figures, Accepted for publication in Ap
Spectral Decomposition of Broad-Line AGNs and Host Galaxies
Using an eigenspectrum decomposition technique, we separate the host galaxy
from the broad line active galactic nucleus (AGN) in a set of 4666 spectra from
the Sloan Digital Sky Survey (SDSS), from redshifts near zero up to about 0.75.
The decomposition technique uses separate sets of galaxy and quasar
eigenspectra to efficiently and reliably separate the AGN and host
spectroscopic components. The technique accurately reproduces the host galaxy
spectrum, its contributing fraction, and its classification. We show how the
accuracy of the decomposition depends upon S/N, host galaxy fraction, and the
galaxy class. Based on the eigencoefficients, the sample of SDSS broad-line AGN
host galaxies spans a wide range of spectral types, but the distribution
differs significantly from inactive galaxies. In particular, post-starburst
activity appears to be much more common among AGN host galaxies. The
luminosities of the hosts are much higher than expected for normal early-type
galaxies, and their colors become increasingly bluer than early-type galaxies
with increasing host luminosity. Most of the AGNs with detected hosts are
emitting at between 1% and 10% of their estimated Eddington luminosities, but
the sensitivity of the technique usually does not extend to the Eddington
limit. There are mild correlations among the AGN and host galaxy
eigencoefficients, possibly indicating a link between recent star formation and
the onset of AGN activity. The catalog of spectral reconstruction parameters is
available as an electronic table.Comment: 18 pages; accepted for publication in A
Ion Chamber Collection Efficiencies for Proton Spot Scanning Calibration
Charge accumulation was measured under calibration conditions in the
spread-out Bragg peak (SOBP) using the calibration bias as well as a range of
voltages from 10V to 500V and a Farmer-style ion chamber. Collection efficiency
was determined by extrapolating to infinite voltage. Similar measurements were
taken in an identical dose distribution with a much shorter spot duration. The
impact of each of the three models on calibration was then quantified using the
TRS-398 protocol. The collection efficiency for the standard calibration was
determined to agree well with the prediction of a continuous beam recombination
correction. The standard calibration field was found to persistently agree with
a continuous beam recombination correction for much lower operating biases. The
collection efficiency result for the short spot duration field did not agree
with either the continuous or pulsed-beam correction. Using the incorrect
recombination model under the standard calibration conditions resulted in a
0.5% calibration difference. We have determined that our spot scanning system
would be most appropriately calibrated using a recombination correction with
continuous beam model. Physicists responsible for the calibration of such
systems are advised to take measurements described here to correctly identify
the applicable recombination model for their clinics.Comment: Submitted December 16, 2015 to Medical Physic
Evaluating Large Language Models on a Highly-specialized Topic, Radiation Oncology Physics
We present the first study to investigate Large Language Models (LLMs) in
answering radiation oncology physics questions. Because popular exams like AP
Physics, LSAT, and GRE have large test-taker populations and ample test
preparation resources in circulation, they may not allow for accurately
assessing the true potential of LLMs. This paper proposes evaluating LLMs on a
highly-specialized topic, radiation oncology physics, which may be more
pertinent to scientific and medical communities in addition to being a valuable
benchmark of LLMs. We developed an exam consisting of 100 radiation oncology
physics questions based on our expertise at Mayo Clinic. Four LLMs, ChatGPT
(GPT-3.5), ChatGPT (GPT-4), Bard (LaMDA), and BLOOMZ, were evaluated against
medical physicists and non-experts. ChatGPT (GPT-4) outperformed all other LLMs
as well as medical physicists, on average. The performance of ChatGPT (GPT-4)
was further improved when prompted to explain first, then answer. ChatGPT
(GPT-3.5 and GPT-4) showed a high level of consistency in its answer choices
across a number of trials, whether correct or incorrect, a characteristic that
was not observed in the human test groups. In evaluating ChatGPTs (GPT-4)
deductive reasoning ability using a novel approach (substituting the correct
answer with "None of the above choices is the correct answer."), ChatGPT
(GPT-4) demonstrated surprising accuracy, suggesting the potential presence of
an emergent ability. Finally, although ChatGPT (GPT-4) performed well overall,
its intrinsic properties did not allow for further improvement when scoring
based on a majority vote across trials. In contrast, a team of medical
physicists were able to greatly outperform ChatGPT (GPT-4) using a majority
vote. This study suggests a great potential for LLMs to work alongside
radiation oncology experts as highly knowledgeable assistants
Applications of various range shifters for proton pencil beam scanning radiotherapy
Background A range pull-back device, such as a machine-related range shifter (MRS) or a universal patient-related range shifter (UPRS), is needed in pencil beam scanning technique to treat shallow tumors. Methods Three UPRS made by QFix (Avondale, PA, USA) allow treating targets across the body: U-shaped bolus (UB), anterior lateral bolus (ALB), and couch top bolus. Head-and-neck (HN) patients who used the UPRS were tested. The in-air spot sizes were measured and compared in this study at air gaps: 6 cm, 16 cm, and 26 cm. Measurements were performed in a solid water phantom using a single-field optimization pencil beam scanning field with the ALB placed at 0, 10, and 20 cm air gaps. The two-dimensional dose maps at the middle of the spread-out Bragg peak were measured using ion chamber array MatriXX PT (IBA-Dosimetry, Schwarzenbruck, Germany) located at isocenter and compared with the treatment planning system. Results A UPRS can be consistently placed close to the patient and maintains a relatively small spot size resulting in improved dose distributions. However, when a UPRS is non-removable (e.g. thick couch top), the quality of volumetric imaging is degraded due to their high Z material construction, hindering the value of Image-Guided Radiation Therapy (IGRT). Limitations of using UPRS with small air gaps include reduced couch weight limit, potential collision with patient or immobilization devices, and challenges using non-coplanar fields with certain UPRS. Our experience showed the combination of a U-shaped bolus exclusively for an HN target and an MRS as the complimentary device for head-and-neck targets as well as for all other treatment sites may be ideal to preserve the dosimetric advantages of pencil beam scanning proton treatments across the body. Conclusion We have described how to implement UPRS and MRS for various clinical indications using the PBS technique, and comprehensively reviewed the advantage and disadvantages of UPRS and MRS. We recommend the removable UB only to be employed for the brain and HN treatments while an automated MRS is used for all proton beams that require RS but not convenient or feasible to use UB
Study of linear energy transfer effect on rib fracture in breast patients receiving pencil-beam-scanning proton therapy
Purpose: To study the effect of proton linear energy transfer (LET) on rib
fracture in breast cancer patients treated with pencil-beam scanning proton
therapy (PBS) using a novel tool of dose-LET volume histogram (DLVH).
Methods: From a prospective registry of patients treated with post-mastectomy
proton therapy to the chest wall and regional lymph nodes for breast cancer
between 2015 and 2020, we retrospectively identified rib fracture cases
detected after completing treatment. Contemporaneously treated control patients
that did not develop rib fracture were matched to patients 2:1 considering
prescription dose, boost location, reconstruction status, laterality, chest
wall thickness, and treatment year. The DLVH index, V(d, l), defined as
volume(V) of the structure with at least dose(d) and LET(l), was calculated.
DLVH plots between the fracture and control group were compared. Conditional
logistic regression (CLR) model was used to establish the relation of V(d, l)
and the observed fracture at each combination of d and l. The p-value derived
from CLR model shows the statistical difference between fracture patients and
the matched control group. Using the 2D p-value map, the DLVH features
associated with the patient outcomes were extracted.
Results: Seven rib fracture patients were identified, and fourteen matched
patients were selected for the control group. The median time from the
completion of proton therapy to rib fracture diagnosis was 12 months (range 5
to 14 months). Two patients had grade 2 symptomatic rib fracture while the
remaining 5 were grade 1 incidentally detected on imaging. The derived p-value
map demonstrated larger V(0-36 Gy[RBE], 4.0-5.0 keV/um) in patients
experiencing fracture (p<0.1).
Conclusions: In breast cancer patients receiving PBS, a larger volume of
chest wall receiving moderate dose and high LET may result in increased risk of
rib fracture.Comment: 1 Table and 3 Figure
Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy
Purpose: To enhance an in-house graphic-processing-unit (GPU) accelerated
virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model
aperture blocks in both dose calculation and optimization for pencil beam
scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS). Methods
and Materials: A block aperture module was integrated into VPMC. VPMC was
validated by an opensource code, MCsquare, in eight water phantom simulations
with 3cm thick brass apertures: four were with aperture openings of 1, 2, 3,
and 4cm without a range shifter, while the other four were with same aperture
opening configurations with a range shifter of 45mm water equivalent thickness.
VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small
targets (average volume 8.4 cc). Finally, 3 patients were selected for robust
optimization with aperture blocks using VPMC. Results: In the water phantoms,
3D gamma passing rate (2%/2mm/10%) between VPMC and MCsquare were
99.710.23%. In the patient geometries, 3D gamma passing rates (3%/2mm/10%)
between VPMC/MCsquare and RayStation MC were 97.792.21%/97.781.97%,
respectively. The calculation time was greatly decreased from 112.45114.08
seconds (MCsquare) to 8.206.42 seconds (VPMC), both having statistical
uncertainties of about 0.5%. The robustly optimized plans met all the
dose-volume-constraints (DVCs) for the targets and OARs per our institutional
protocols. The mean calculation time for 13 influence matrices in robust
optimization by VPMC was 41.6 seconds. Conclusion: VPMC has been successfully
enhanced to model aperture blocks in dose calculation and optimization for the
PBSPT-based SRS.Comment: 3 tables, 3 figure
The Radiation Oncology NLP Database
We present the Radiation Oncology NLP Database (ROND), the first dedicated
Natural Language Processing (NLP) dataset for radiation oncology, an important
medical specialty that has received limited attention from the NLP community in
the past. With the advent of Artificial General Intelligence (AGI), there is an
increasing need for specialized datasets and benchmarks to facilitate research
and development. ROND is specifically designed to address this gap in the
domain of radiation oncology, a field that offers many opportunities for NLP
exploration. It encompasses various NLP tasks including Logic Reasoning, Text
Classification, Named Entity Recognition (NER), Question Answering (QA), Text
Summarization, and Patient-Clinician Conversations, each with a distinct focus
on radiation oncology concepts and application cases. In addition, we have
developed an instruction-tuning dataset consisting of over 20k instruction
pairs (based on ROND) and trained a large language model, CancerChat. This
serves to demonstrate the potential of instruction-tuning large language models
within a highly-specialized medical domain. The evaluation results in this
study could serve as baseline results for future research. ROND aims to
stimulate advancements in radiation oncology and clinical NLP by offering a
platform for testing and improving algorithms and models in a domain-specific
context. The ROND dataset is a joint effort of multiple U.S. health
institutions. The data is available at
https://github.com/zl-liu/Radiation-Oncology-NLP-Database.Comment: 10 pages, 7 figures, 6 table
Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy
Purpose: To develop a DL-based PBSPT dose prediction workflow with high
accuracy and balanced complexity to support on-line adaptive proton therapy
clinical decision and subsequent replanning.
Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer
patients previously treated at our institution were included in the study, each
with CTs, structure sets, and plan doses calculated by the in-house developed
Monte-Carlo dose engine. For the ablation study, we designed three experiments
corresponding to the following three methods: 1) Experiment 1, the conventional
region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by
raytracing of proton beams) method to improve proton dose prediction. 3)
Experiment 3, the sliding window method for the model to focus on local details
to further improve proton dose prediction. A fully connected 3D-Unet was
adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing
rates, and dice coefficients for the structures enclosed by the iso-dose lines
between the predicted and the ground truth doses were used as the evaluation
metrics. The calculation time for each proton dose prediction was recorded to
evaluate the method's efficiency.
Results: Compared to the conventional ROI method, the beam mask method
improved the agreement of DVH indices for both targets and OARs and the sliding
window method further improved the agreement of the DVH indices. For the 3D
Gamma passing rates in the target, OARs, and BODY (outside target and OARs),
the beam mask method can improve the passing rates in these regions and the
sliding window method further improved them. A similar trend was also observed
for the dice coefficients. In fact, this trend was especially remarkable for
relatively low prescription isodose lines. The dose predictions for all the
testing cases were completed within 0.25s
The Black Hole-Bulge Relationship in Luminous Broad-Line Active Galactic Nuclei and Host Galaxies
We have measured the stellar velocity dispersions (\sigma_*) and estimated
the central black hole (BH) masses for over 900 broad-line active galactic
nuclei (AGNs) observed with the Sloan Digital Sky Survey. The sample includes
objects which have redshifts up to z=0.452, high quality spectra, and host
galaxy spectra dominated by an early-type (bulge) component. The AGN and host
galaxy spectral components were decomposed using an eigenspectrum technique.
The BH masses (M_BH) were estimated from the AGN broad-line widths, and the
velocity dispersions were measured from the stellar absorption spectra of the
host galaxies. The range of black hole masses covered by the sample is
approximately 10^6 < M_BH < 10^9 M_Sun. The host galaxy luminosity-velocity
dispersion relationship follows the well-known Faber-Jackson relation for
early-type galaxies, with a power-law slope 4.33+-0.21. The estimated BH masses
are correlated with both the host luminosities (L_{H}) and the stellar velocity
dispersions (\sigma_*), similar to the relationships found for low-redshift,
bulge-dominated galaxies. The intrinsic scatter in the correlations are large
(~0.4 dex), but the very large sample size allows tight constraints to be
placed on the mean relationships: M_BH ~ L_H^{0.73+-0.05} and M_BH ~
\sigma_*^{3.34+-0.24}. The amplitude of the M_BH-\sigma_* relation depends on
the estimated Eddington ratio, such that objects with larger Eddington ratios
have smaller black hole masses than expected at a given velocity dispersion.Comment: Accepted for publication in A