Raw Detector Data Research Articles

Open-Source Data Analysis Tool for Spectral Small-Angle X-ray Scattering Using Spectroscopic Photon-Counting Detector.

Spectral small-angle X-ray scattering (sSAXS) is a powerful technique for material characterization from thicker samples by capturing elastic X-ray scattering data in angle- and energy-dispersive modes at small angles. This approach is enabled by the use of a 2D spectroscopic photon-counting detector that provides energy and position information of scattered photons when a sample is irradiated by a polychromatic X-ray beam. Here, we describe an open-source tool with a graphical interface for analyzing sSAXS data obtained from a 2D spectroscopic photon-counting detector with a large number of energy bins. The tool takes system geometry parameters and raw detector data to output 1D scattering patterns and a 2D spatially-resolved scattering map in the energy range of interest. We validated these features using data from samples of caffeine powder with well-known scattering peaks. This open-source tool will facilitate sSAXS data analysis for various material characterization applications.

Towards Real-Time Machine Learning-Based Signal/Background Selection in the CMS Detector Using Quantized Neural Networks and Input Data Reduction

The Large Hadron Collider (LHC) is being prepared for an extensive upgrade to boost its particle discovery potential. The new phase, High Luminosity LHC, will operate at a factor-of-five-increased luminosity (the number proportional to the rate of collisions). Consequently, such an increase in luminosity will result in enormous quantities of generated data that cannot be transmitted or stored with the currently available resources and time. However, the vast majority of the generated data consist of uninteresting data or pile-up data containing few interesting events or electromagnetic showers. High-Luminosity LHC detectors, including the Compact Muon Solenoid (CMS), will thus have to rely on innovative approaches like the proposed one to select interesting collision data. In charge of data reduction/selection at the early stages of data streaming is a level 1 trigger (L1T), a real-time event selection system. The final step of the L1T is a global trigger, which uses sub-system algorithms to make a final decision about signal acceptance/rejection within a decision time of around 12 microseconds. For one of these sub-system L1T algorithms, we propose using quantized neural network models deployed in targeted L1T devices, namely, field-programmable gate arrays (FPGA), as a classifier between electromagnetic and pile-up/quantum chromodynamics showers. The developed quantized neural network operates in an end-to-end manner using raw detector data to speed up the classification process. The proposed data reduction methods further decrease model size while retaining accuracy. The proposed approach was tested with simulated data (since the detector is still in the production stage) and took less than 1 microsecond, achieving real-time signal–background classification with a classification accuracy of 97.37% for 2-bit-only quantization and 97.44% for quantization augmented with the data reduction approach (compared to 98.61% for the full-precision, standard network).

High-Throughput FPGA-Based Inference of Gradient Tree Boosting Models for Position Estimation in PET Detectors

In modern large-scale positron emission tomography (PET) systems, transferring the digitized raw detector data at high count rates to a centralized processing unit is a challenge. Processing data on field programmable gate arrays (FPGAs) close to the detectors can reduce data early on and improve scalability of the PET system. We present and evaluate an FPGA implementation of gradient tree boosting (GTB) for 1-D position estimation of gamma interactions in the scintillator. GTB is a supervised machine learning algorithm based on building ensembles of binary decision trees. Models were trained offline and inferred in an FPGA (XC7K410T-2FFG676 Kintex-7). Input features and GTB parameters influencing both positioning performance and model size were varied while evaluating the inferred models concerning data throughput and FPGA resource consumption as well as positioning performance. We achieved throughputs per detector between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.94\times 10^{6}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.55\times 10^{6}$ </tex-math></inline-formula> gamma interactions per second. For an optimized GTB model, resource consumption could be reduced by factors of 17 and 10 to less than 1% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.51\times 10^{3}$ </tex-math></inline-formula> look-up tables) of available logic and 1.26% (20 BRAMs) of memory resources, while maintaining a positioning performance of 98.63% when compared to the model with the best positioning performance. The presented framework can be easily adapted to other photosensors and scintillator influencing.

Ultralow-parameter denoising: Trainable bilateral filter layers in computed tomography.

Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.

Transfer learning-based highway crash risk evaluation considering manifold characteristics of traffic flow

The main objective of this study is to evaluate highway crash risk and improve the spatial and temporal transferability of crash risk models. The predictive performance is affected by the difficulty of existing models to quantify crash risk from historical traffic flow data at different locations and times and may not fully capture the complex nonlinear relationships between high-dimensional factors in traffic flow states. Oregon US26W freeway data from 2016 and 2017 and I5N freeway data from 2017 were used. Raw detector data collected from two consecutive detector stations upstream-downstream detector stations were converted into 30 traffic variables. The averages, standard deviations, and coefficients of variation were obtained by aggregating traffic values using each lane. Candidate variables for traffic flow were extracted, and the importance of each variable was calculated using LightGBM, which reveals that variable differences between lanes contributed more. The manifold distance was then applied to quantify the crash risk and classify traffic crashes or not. When the manifold distance is 0.4, it could effectively distinguish traffic crashes. TransferBoost was further employed to build a crash risk model. Modeling using 2016 and 2017 data from the US26W freeway revealed a significant decrease in AUC and a gradual decrease in the model’s sensitivity. However, the crash risk prediction performance of TransferBoost improved by 5.2% when modeling using 2017 data from US26W and I5N freeways. The results show that the model developed for one time period cannot be directly used to predict crash risk for another period on the same freeway. However, the model developed for one highway cannot be directly used to predict the crash risk of another highway either, maintaining some transferability at low false alarm rates. TransferBoost provides a fresh perspective on the transferability of the model. The findings of this study could facilitate more accurate proactive safety management and improvement countermeasure development.

Trigger Performance Monitoring and Rate Predictions Preparation for Run 3 at ATLAS Experiment

Bespoke Cost Monitoring software collates data on the performance of all aspects of the ATLAS experiment's High Level Trigger software. These data are exported for subsequent analysis offline, and are used to understand the resource usage of the individual trigger selections in terms of the amount of CPU time and the amount of raw detector data which was required to perform the selection. For the LHC's Run 3, the ATLAS High Level Trigger is re-implemented in a multi-threaded framework with both intra-event and inter-event algorithm parallelism. We will describe some of the complications and considerations which arise from monitoring event metrics in a highly parallel environment.

Pulse shape discrimination and exploration of scintillation signals using convolutional neural networks

We demonstrate the use of a convolutional neural network to perform neutron-gamma pulse shape discrimination, where the only inputs to the network are the raw digitised silicon photomultiplier signals from a dual scintillator detector element made of 6Li F:ZnS(Ag) scintillator and PVT plastic. A realistic labelled dataset was created to train the network by exposing the detector to an AmBe source, and a data-driven method utilising a separate photomultiplier tube was used to assign labels to the recorded signals. This approach is compared to the charge integration and continuous wavelet transform methods and a simpler artificial neural net. It is found to provide superior levels of discrimination, achieving an area under the curve of 0.996 ± 0.003. We find that the neural network is capable of extracting interpretable features directly from the raw data. In addition, by visualising the high-dimensional representations of the network with the t-SNE algorithm, we discover that not only is this method robust to minor mislabeling of the training dataset but that it is possible to identify an underlying substructure within the signals that goes beyond the original labelling. This technique could be utilised to explore and cluster complex, raw detector data in a novel way that may reveal more insights than standard analysis methods.

A delivery quality assurance tool based on the actual leaf open times in tomotherapy.

To validate a delivery quality assurance (DQA) protocol for tomotherapy based on the measurement of the leaf open times (LOTs). In addition, to show the correlation between the mean relative LOT discrepancy and the dose deviation in the planning target volume (PTV). We used a LOT measurement algorithm presented in a previous work on our two tomotherapy treatment units (TOMO1 and TOMO2). We generated TomoPhant plans with intentional random LOT discrepancies following Gaussian distributions of -6%, -4%, -2%, 2%, 4%, and 6%. We irradiated them on the Cheese Phantom with two ion chambers and collected the raw data on both our treatment units. Using the raw data, we measured the actual LOTs and verified that the induced discrepancies were highlightable. Then, we calculated the actual dose using Accuray's standalone dose calculator and verified that the calculated dose agreed with the ion chamber measurement. We randomly chose 60 clinical treatment plans, delivered them in air, and collected the raw detector data. We measured the actual LOTs from the raw data and calculated the corresponding dose distributions using Accuray's standalone dose calculator. We assessed the Pearson coefficient correlation of the deviation between expected and actual dose in the PTV (a) with the mean relative LOT discrepancy and (b) with the γ-index pass rate for different tolerances. The mean relative discrepancy between actual (measured by the algorithm) and expected LOTs on the modified TomoPhant plans was 1.10±0.05% on TOMO1 and 0.02±0.03% on TOMO2, respectively. The agreement between measured and calculated dose was 0.2±0.3% on TOMO1 and 0.1±0.3% on TOMO2, respectively. On clinical plans, the means of the relative LOT discrepancies ranged from -3.0 % to 1.4%. The dose deviation in the PTVs ranged from -1.6% to 2.4%. The Pearson coefficient correlation between the mean relative LOT discrepancy and the dose deviation in the PTV was 0.76 (P≈10-15 ) on TOMO1 and 0.65 (P≈10-10 ) on TOMO2, respectively. There was no correlation between the γ-index pass rate and the dose deviation in the PTV. The method made it possible to measure and to correctly highlight the LOT discrepancies on the TomoPhant plans. The dose subsequently calculated was accurate. On clinical plans, the mean LOT discrepancy correlated with the dose deviation in the PTV. This makes the mean LOT discrepancy a handy indicator of the plan quality.

A fast and efficient SIMD track reconstruction algorithm for the LHCb upgrade 1 VELO-PIX detector

The upgraded CERN LHCb detector, due to start data taking in 2021, will have to reconstruct 4 TB/s of raw detector data in real time using commodity processors. This is one of the biggest real-time data processing challenges in any scientific domain. We present an intrinsically parallel reconstruction algorithm for the vertex detector of the LHCb experiment designed to optimally exploit multi-core general purpose architectures. We evaluate the algorithm on two high-end architectures from two different vendors and discuss in detail the impact of different SIMD Instruction Set Architecture extensions on the performance. We further compare the algorithm to current state-of-the-art scalar pattern recognition algorithms. We show a factor 2 speedup while achieving similar or better levels of physics performance.

Reconstruction of track candidates at the LHC crossing rate using FPGAs

In 2021 the LHCb experiment will be upgraded, and the DAQ system will be based on full reconstruction of events, at the full LHC crossing rate. This requires an entirely new system, capable of reading out, building and reconstructing events at an average rate of 30 MHz. In facing this challenge, the system could take advantage of a fast pre-processing of data on dedicated FPGAs. The results of an R&amp;D on these technologies, developed in the context of the LHCb Upgrade I, are presented in this document. In particular, the details and potential benefits of an approach based on producing in real-time sorted collections of hits in the VELO detector (pre-tracks) are discussed. These pre-processed data can then be used as seeds by the High Level Trigger (HLT) farm to find tracks for the Level 1 trigger with much lower computational effort than possible by starting from the raw detector data, thus freeing an important fraction of the power of the CPU farm for higher level processing tasks.

Cinema:Bandit: a visualization application for beamline science demonstrated on XFEL shock physics experiments.

A new visualization tool, Cinema:Bandit, and its demonstration with a continuous workflow for analyzing shock physics experiments and visually exploring the data in real time at X-ray light sources is presented. Cinema:Bandit is an open-source, web-based visualization application in which the experimenter may explore an aggregated dataset to inform real-time beamline decisions and enable post hoc data analysis. The tool integrates with experimental workflows that process raw detector data into a simple database format, and it allows visualization of disparate data types, including experimental parameters, line graphs, and images. Use of parallel coordinates accommodates the irregular sampling of experimental parameters and allows for display and filtering of both experimental inputs and measurements. The tool is demonstrated on a dataset of shock-compressed titanium collected at the Matter in Extreme Conditions hutch at the Linac Coherent Light Source.

Silicon carbide detectors for high flux neutron monitoring at near-core locations

Silicon carbide (SiC) and gallium nitride (GaN) semiconductors are two detector candidates for high flux neutron monitoring entailing high temperature and high radiation environments owing to their wide band gap and high resistance to radiation damage. While the neutron elastic scattering with carbon (C) and silicon (Si) makes SiC intrinsically sensitive to neutrons, neutron interaction with nitrogen (N) by way of 14N(n,p)14C reaction provides neutron sensitivity to GaN. In this study, we investigated reactor-based high flux neutron monitoring with in-house fabricated SiC detectors and studied the feasibility of neutron detection using GaN. As proof-of-concept for GaN neutron sensitivity, we evaluated SiC detectors coupled with nitrogen based (N-based) neutron converter materials, and commercially purchased AlGaN photo sensors. Spectral response as well as raw waveform data from SiC, SiC coupled with N-based converter layers, and AlGaN sensors were acquired while exposing the detectors to a mixed neutron–gamma field of a research reactor at ex-core locations at various power levels. The experimental SiC detector spectra were compared against Geant4 Monte Carlo (MC) simulations, which agreed with the measurement results. The reactor power determined using SiC detector raw data correlated well with that indicated by a standard compensated ion chamber (CIC). The AlGaN sensors showed promising results with a correlation between sensor response and reactor power. The current study demonstrates SiC as a suitable detector for high flux neutron monitoring with good radiation tolerance based on near-core irradiation.

Identifying Recurring Bottlenecks on Urban Expressway Using a Fusion Method Based on Loop Detector Data

The accurate identification of recurrent bottlenecks has been an important assumption of many studies on traffic congestion analysis and management. As one of the most widely used traffic detection devices, loop detectors can provide reliable multidimensional data for traffic bottleneck identification. Although great efforts have been put on developing bottleneck identification methods based on loop detector data, the existing studies are less informative with respect to providing accurate position of the bottlenecks and discussing the algorithm efficiency when facing with large amount of real-time data. This paper aims at improving the quality of bottleneck identification as well as avoiding excessive data processing burden. A fusion method of loop detector data with different collection cycles is proposed. It firstly determines the occurrence and the approximate locations of bottlenecks using large cycle data considering its high accuracy in determining bottlenecks occurrence. Then, the small cycle data are used to determine the accurate location and the duration time of the bottlenecks. A case study is introduced to verify the proposed method. A large set of 30 s raw loop detector data from a selected urban expressway segment in California is used. Also, the identification result is compared with the classical transformed cumulative curves method. The results show that the fusion method is valid with bottleneck identification and location positioning. We finally conclude by discussing some future improvements and potential applications.

A comprehensive real-time analysis model at the LHCb experiment

An evolved real-time data processing strategy is proposed for high-energy physics experiments, and its implementation at the LHCb experiment is presented. The reduced event model allows not only the signal candidate firing the trigger to be persisted, as previously available, but also an arbitrary set of other reconstructed or raw objects from the event. This allows for higher trigger rates for a given output data bandwidth, when compared to the traditional model of saving the full raw detector data for each trigger, whilst accommodating inclusive triggers and preserving data mining capabilities. The gains in physics reach and savings in computing resources already made possible by the model are discussed, along with the prospects of employing it more widely for Run 3 of the Large Hadron Collider.

Abstracts of the Total Body PET conference 2018

Abstracts of the Total Body PET conference 2018

Crystal Delay and Time Walk Correction Methods for Coincidence Resolving Time Improvements of a Digital-Silicon-Photomultiplier-Based PET/MRI Insert

Our group has built the MRI compatible PET insert Hyperion IID, which is based on digital silicon photomultipliers [digital photon counters (DPCs)]. We characterized its performance for two different scintillator configurations already in previous publications. For this paper, we reused the raw detector data that were used in previous publications and stored for offline analysis. We reprocessed these data in order to improve the timing performance of the PET scanner. Different crystal delay and time walk calibration and correction methods are evaluated with respect to the coincidence resolving time (CRT) of the scanner. For all applied correction methods, we report CRTs both for a preclinical and a clinical scintillator configuration for all DPC trigger schemes and several energy windows. The newly obtained results were compared with the previously published values, and the additional benefit of using a time walk correction was evaluated. Previously published CRTs could be improved using a refined version of the original crystal delay calibration method without applying a walk correction by 6.3%–10.9% for a wide energy window ranging from 250–625 keV, and we could show an additional improvement of up to 2.6% by adding a walk correction. Using trigger scheme 1 and a very narrow energy window around the photopeak (500–520 keV), we could reach CRT values on system level of 208/240 ps for the clinical/preclinical scintillator configuration.

Suppression of the CT Beam Hardening Streak Artifact Using Predictive Correction on Detector Data

The purpose of the research was to develop an automated program incorporating a predictive artifact correction technique (PACT) to correct for the signal deviations from metal beam hardening artifacts in Computed Tomography (CT) detector raw data. Thin-slice sequential CT scans were performed on a dosimetry head phantom using a Somatom Sensation 16 scanner to establish a ground truth image. Metal pins were then affixed to either side of the phantom at the three and nine o’clock positions to cause streak artifact in detector raw data and a subsequent streak image. The program automatically detected the extent of the overlap peaks in the detector raw data causing the artifact. It profiled a correction using adjacent projections so that the peak error could be corrected rather than simply being removed or smoothed by interpolation. The PACT algorithm modified raw data was then reconstructed on a SYNGO CT reconstruction workstation. This image was then compared against ground truth and that produced by commercially available metal artifact reduction projection completion and also a research based iterative technique. Qualitative results illustrate superior suppression of streak artifact in images using PACT when compared directly to tested projection completion methods but inferior to iterative reconstruction. Recovery of voxel data underlying the streak is also demonstrated to be quantitatively superior with PACT when referenced to the original ground truth image. Limitations were however detected with the threshold technique for initial localisation of the streak sources. The work still demonstrates the feasibility of this predictive artifact correction technique in correcting beam hardening affected voxel data without recourse to expensive additional options such as iterative reconstruction or dual energy that are not so commonly available in the clinical setting.

Software-Based Real-Time Acquisition and Processing of PET Detector Raw Data.

In modern positron emission tomography (PET) readout architectures, the position and energy estimation of scintillation events (singles) and the detection of coincident events (coincidences) are typically carried out on highly integrated, programmable printed circuit boards. The implementation of advanced singles and coincidence processing (SCP) algorithms for these architectures is often limited by the strict constraints of hardware-based data processing. In this paper, we present a software-based data acquisition and processing architecture (DAPA) that offers a high degree of flexibility for advanced SCP algorithms through relaxed real-time constraints and an easily extendible data processing framework. The DAPA is designed to acquire detector raw data from independent (but synchronized) detector modules and process the data for singles and coincidences in real-time using a center-of-gravity (COG)-based, a least-squares (LS)-based, or a maximum-likelihood (ML)-based crystal position and energy estimation approach (CPEEA). To test the DAPA, we adapted it to a preclinical PET detector that outputs detector raw data from 60 independent digital silicon photomultiplier (dSiPM)-based detector stacks and evaluated it with a [(18)F]-fluorodeoxyglucose-filled hot-rod phantom. The DAPA is highly reliable with less than 0.1% of all detector raw data lost or corrupted. For high validation thresholds (37.1 ± 12.8 photons per pixel) of the dSiPM detector tiles, the DAPA is real time capable up to 55 MBq for the COG-based CPEEA, up to 31 MBq for the LS-based CPEEA, and up to 28 MBq for the ML-based CPEEA. Compared to the COG-based CPEEA, the rods in the image reconstruction of the hot-rod phantom are only slightly better separable and less blurred for the LS- and ML-based CPEEA. While the coincidence time resolution (∼ 500 ps) and energy resolution (∼12.3%) are comparable for all three CPEEA, the system sensitivity is up to 2.5 × higher for the LS- and ML-based CPEEA.

Data Processing for a High Resolution Preclinical PET Detector Based on Philips DPC Digital SiPMs

In positron emission tomography (PET) systems, light sharing techniques are commonly used to readout scintillator arrays consisting of scintillation elements, which are smaller than the optical sensors. The scintillating element is then identified evaluating the signal heights in the readout channels using statistical algorithms, the center of gravity (COG) algorithm being the simplest and mostly used one. We propose a COG algorithm with a fixed number of input channels in order to guarantee a stable calculation of the position. The algorithm is implemented and tested with the raw detector data obtained with the Hyperion-II $^{\rm D}$ preclinical PET insert which uses Philips Digital Photon Counting’s (PDPC) digitial SiPMs. The gamma detectors use LYSO scintillator arrays with $30 \times 30$ crystals of $1 \times 1 \times 12~\hbox{mm}^3$ in size coupled to $4 \times 4$ PDPC DPC 3200-22 sensors (DPC) via a 2-mm-thick light guide. These self-triggering sensors are made up of $2 \times 2$ pixels resulting in a total of 64 readout channels. We restrict the COG calculation to a main pixel, which captures most of the scintillation light from a crystal, and its (direct and diagonal) neighboring pixels and reject single events in which this data is not fully available. This results in stable COG positions for a crystal element and enables high spatial image resolution. Due to the sensor layout, for some crystals it is very likely that a single diagonal neighbor pixel is missing as a result of the low light level on the corresponding DPC. This leads to a loss of sensitivity, if these events are rejected. An enhancement of the COG algorithm is proposed which handles the potentially missing pixel separately both for the crystal identification and the energy calculation. Using this advancement, we show that the sensitivity of the Hyperion-II $^{\rm D}$ insert using the described scintillator configuration can be improved by 20–100% for practical useful readout thresholds of a single DPC pixel ranging from 17–52 photons. Furthermore, we show that the energy resolution of the scanner is superior for all readout thresholds if singles with a single missing pixel are accepted and correctly handled compared to the COG method only accepting singles with all neighbors present by 0–1.6% (relative difference). The presented methods can not only be applied to gamma detectors employing DPC sensors, but can be generalized to other similarly structured and self-triggering detectors, using light sharing techniques, as well.

Development of A Novel Image Guidance Alternative for Patient Localization using Topographic Images for TomoTherapy

To develop a faster and lower dose topogram based image registration for TomoTherapy as an alternative image guidance tool to volumetric megavoltage computed tomography (MVCT). Topogram procedures were performed for an anthropomorphic thorax phantom on a TomoTherapy HD unit (Accuray Inc., Sunnyvale, CA) using couch speeds from 1-4 cm/s and gantry angles of 0 and 90 degrees, other scanning parameters are: 1 mm imaging jaw, compression factor of 1, 30 seconds scanning duration with all multileaf collimators (MLCs) open. The raw exit detector data was exported after each scan. The topogram was reconstructed from a fan beam source for TomoTherapy beam and detector geometry at a SSD of 85 cm. A reference image, so called Digitally Reconstructed Topogram (DRT) was created by integrating the trajectories through the kVCT simulation with the topogram geometry. Image registration was performed by visually aligning the bony structure in topogram to the DRT. Image resolution was determined by the radius of curvature for the detector array, source to axis distance, source to detector distance, detector spacing, and number of detectors. The localization errors were 1.5, 2.5 mm in medio-lateral and anterior-posterior direction, larger errors in cranial-caudal direction was observed for faster couch speeds (i.e., ≥3cm/s). The topographic imaging time was 30 sec (versus 3-5 minutes for MVCT thorax scan) with imaging dose less than 1% of MVCT scan. Topograms with appropriate couch speed provide reliable patient localization images while significantly reducing pre-treatment imaging time. Topogram can be used as an alternative and/or additional patient alignment tool to MVCT on TomoTherapy.