bluuebunny/crossref_metadata_2025_split huggingface dataset:
MO‐PIS‐Exhibit Hall‐01: Tools for TG‐142 Linac Imaging QA I
Authors: Clements M , Wiesmeyer M 
Published: July 2014

Partners in Solutions is an exciting new program in which AAPM partners with our vendors to present practical “hands‐on” information about the equipment and software systems that we use in our clinics. The therapy topic this year is solutions for TG‐142 recommendations for linear accelerator imaging QA. Note that the sessions are being held in a special purpose room built on the Exhibit Hall Floor, to encourage further interaction with the vendors.Automated Imaging QA for TG‐142 with RITPresentation Time: 2:45 – 3:15 PMThis presentation will discuss software tools for automated imaging QA and phantom analysis for TG‐142. All modalities used in radiation oncology will be discussed, including CBCT, planar kV imaging, planar MV imaging, and imaging and treatment coordinate coincidence. Vendor supplied phantoms as well as a variety of third‐party phantoms will be shown, along with appropriate analyses, proper phantom setup procedures and scanning settings, and a discussion of image quality metrics. Tools for process automation will be discussed which include: RIT Cognition (machine learning for phantom image identification), RIT Cerberus (automated file system monitoring and searching), and RunQueueC (batch processing of multiple images). In addition to phantom analysis, tools for statistical tracking, trending, and reporting will be discussed. This discussion will include an introduction to statistical process control, a valuable tool in analyzing data and determining appropriate tolerances.An Introduction to TG‐142 Imaging QA Using Standard Imaging ProductsPresentation Time: 3:15 – 3:45 PMMedical Physicists want to understand the logic behind TG‐142 Imaging QA. What is often missing is a firm understanding of the connections between the EPID and OBI phantom imaging, the software “algorithms” that calculate the QA metrics, the establishment of baselines, and the analysis and interpretation of the results. The goal of our brief presentation will be to establish and solidify these connections.Our talk will be motivated by the Standard Imaging, Inc. phantom and software solutions. We will present and explain each of the image quality metrics in TG‐142 in terms of the theory, mathematics, and algorithms used to implement them in the Standard Imaging PIPSpro software. In the process, we will identify the regions of phantom images that are analyzed by each algorithm. We then will discuss the process of the creation of baselines and typical ranges of acceptable values for each imaging quality metric.
MO‐G‐18A‐01: Radiation Dose Reducing Strategies in CT, Fluoroscopy and Radiography
Authors: Mahesh M , Gingold E , Jones A 
Published: July 2014

Advances in medical x‐ray imaging have provided significant benefits to patient care.According to NCRP 160, there are more than 400 million x‐ray procedures performed annually in the United States alone that contributes to nearly half of all the radiation exposure to the US population. Similar growth trends in medical x‐ray imaging are observed worldwide. Apparent increase in number of medical x‐ray imaging procedures, new protocols and the associated radiation dose and risk has drawn considerable attention. This has led to a number of technological innovations such as tube current modulation, iterative reconstruction algorithms, dose alerts, dose displays, flat panel digital detectors, high efficient digital detectors, storage phosphor radiography, variable filters, etc. that are enabling users to acquire medical x‐ray images at a much lower radiation dose. Along with these, there are number of radiation dose optimization strategies that users can adapt to effectively lower radiation dose in medical x‐ray procedures. The main objectives of this SAM course are to provide information and how to implement the various radiation dose optimization strategies in CT, Fluoroscopy and Radiography.Learning Objectives:

To update impact of technological advances on dose optimization in medical imaging.
To identify radiation optimization strategies in computed tomography.
To describe strategies for configuring fluoroscopic equipment that yields optimal images at reasonable radiation dose.
To assess ways to configure digital radiography systems and recommend ways to improve image quality at optimal dose.
MO‐G‐BRE‐03: Automated Continuous Monitoring of Patient Setup with Second‐Check Independent Image Registration
Authors: Jiang X , Fox T , Schreibmann E 
Published: July 2014

Purpose:To create a non‐supervised quality assurance program to monitor image‐based patient setup. The system acts a secondary check by independently computing shifts and rotations and interfaces with Varian's database to verify therapist's work and warn against sub‐optimal setups.Methods:Temporary digitally‐reconstructed radiographs (DRRs) and OBI radiographic image files created by Varian's treatment console during patient setup are intercepted and used as input in an independent registration module customized for accuracy that determines the optimal rotations and shifts. To deal with the poor quality of OBI images, a histogram equalization of the live images to the DDR counterparts is performed as a pre‐processing step. A search for the most sensitive metric was performed by plotting search spaces subject to various translations and convergence analysis was applied to ensure the optimizer finds the global minima. Final system configuration uses the NCC metric with 150 histogram bins and a one plus one optimizer running for 2000 iterations with customized scales for translations and rotations in a multi‐stage optimization setup that first corrects and translations and subsequently rotations.Results:The system was installed clinically to monitor and provide almost real‐time feedback on patient positioning. On a 2 month‐basis uncorrected pitch values were of a mean 0.016° with standard deviation of 1.692°, and couch rotations of − 0.090°± 1.547°. The couch shifts were −0.157°±0.466° cm for the vertical, 0.045°±0.286 laterally and 0.084°± 0.501° longitudinally. Uncorrected pitch angles were the most common source of discrepancies. Large variations in the pitch angles were correlated with patient motion inside the mask.Conclusion:A system for automated quality assurance of therapist's registration was designed and tested in clinical practice. The approach complements the clinical software's automated registration in terms of algorithm configuration and performance and constitutes a practical approach to implement safe and cost‐effective radiotherapy.
MO‐G‐BRD‐01: Point/Counterpoint Debate: Arc Based Techniques Will Make Conventional IMRT Obsolete
Authors: Shepard D , Popple R , Balter P 
Published: July 2014

A variety of intensity modulated radiation therapy (IMRT) delivery techniques have been developed that have provided clinicians with the ability to deliver highly conformal dose distributions. The delivery techniques include compensators, step‐and‐shoot IMRT, sliding window IMRT, volumetric modulated arc therapy (VMAT), and tomotherapy. A key development in the field of IMRT was the introduction of new planning algorithms and delivery control systems in 2007 that made it possible to coordinate the gantry rotation speed, dose rate, and multileaf collimator leaf positions during the delivery of arc therapy. With these developments, VMAT became a routine clinical tool. The use of VMAT has continued to grow in recent years and some would argue that this will soon make conventional IMRT obsolete, and this is the premise of this debate. To introduce the debate, David Shepard, Ph.D. will provide an overview of IMRT delivery techniques including historical context and how they are being used today. The debate will follow with Richard Popple, Ph.D. arguing FOR the Proposition and Peter Balter, Ph.D. arguing AGAINST it.Learning Objectives:

Understand the different delivery techniques for IMRT.
Understand the potential benefits of conventional IMRT.
Understand the potential benefits of arc‐based IMRT delivery.
MO‐G‐BRF‐02: Enhancement of Texture‐Based Metastasis Prediction Models Via the Optimization of PET/MRI Acquisition Protocols
Authors: Vallieres M , Laberge S , R. Levesque I , El Naqa I 
Published: July 2014

Purpose:We have previously identified a prediction model of lung metastases at diagnosis of soft‐tissue sarcomas (STS) that is composed of two textural features extracted from FDG‐PET and T1‐weighted (T1w) MRI scans. The goal of this study is to evaluate whether the optimization in FDGPET and MRI acquisition parameters would enhance the prediction performance of texture‐based models.Methods:Ten FDG‐PET and T1w‐ MRI digitized tumor models were generated from imaging data of STS patients who underwent pre‐treatment clinical scans between 2005 and 2011. Five of ten patients eventually developed lung metastases. Numerically simulated MR images were produced using echo times (TE) of 2 and 4 times the nominal clinical parameter (TEc), and repetition times (TR) of 0.5, 0.67, 1.5 and 2 times the nominal clinical parameter (TRc) found in the DICOM headers (TEc range: 9–13 ms, TRc range: 410‐667 ms). PET 2D images were simulated using Monte‐Carlo and were reconstructed using an ordered‐subsets expectation maximization (OSEM) algorithm with 1 to 32 iterations and a post‐reconstruction Gaussian filter of 0, 2, 4 or 6 mm width. For all possible combinations of PET and MRI acquisition parameters, the prediction model was constructed using logistic regression with new coefficients, and its associated prediction performance for lung metastases was evaluated using the area under the ROC curve (AUC).Results:The prediction performance over all simulations yielded AUCs ranging from 0.7 to 1. Notably, TR values below or equal to TRc and higher PET post‐reconstruction filter widths yielded higher prediction performance. The best results were obtained with a combination of 4*TEc, TRc, 30 OSEM iterations and 2mm filter width.Conclusion:This work indicates that texture‐based metastasis prediction models could be improved using optimized choices of FDG‐PET and MRI acquisition protocols. This principle could be generalized to other texture‐based models.
MO‐G‐BRF‐05: Determining Response to Anti‐Angiogenic Therapies with Monte Carlo Tumor Modeling
Authors: Valentinuzzi D , Simoncic U , Titz B , Jeraj R 
Published: July 2014

Purpose:Patient response to anti‐angiogenic therapies with vascular endothelial growth factor receptor – tyrosine kinase inhibitors (VEGFR TKIs) is heterogeneous. This study investigates key biological characteristics that drive differences in patient response via Monte Carlo computational modeling capable of simulating tumor response to therapy with VEGFR TKI.Methods:VEGFR TKIs potently block receptors, responsible for promoting angiogenesis in tumors. The model incorporates drug pharmacokinetic and pharmacodynamic properties, as well as patientspecific data of cellular proliferation derived from [18F]FLT‐PET data. Sensitivity of tumor response was assessed for multiple parameters, including initial partial oxygen tension (pO2), cell cycle time, daily vascular growth fraction, and daily vascular regression fraction. Results were benchmarked to clinical data (patient 2 weeks on VEGFR TKI, followed by 1‐week drug holiday). The tumor pO2 was assumed to be uniform.Results:Among the investigated parameters, the simulated proliferation was most sensitive to the initial tumor pO2. Initial change of 5 mmHg can already Result in significantly different levels of proliferation. The model reveals that hypoxic tumors (pO2 ≥ 20 mmHg) show the highest decrease of proliferation, experiencing mean FLT standardized uptake value (SUVmean) decrease for at least 50% at the end of the clinical trial (day 21). Oxygenated tumors (pO2 20 mmHg) show a transient SUV decrease (30–50%) at the end of the treatment with VEGFR TKI (day 14) but experience a rapid SUV rebound close to the pre‐treatment SUV levels (70–110%) at the time of a drug holiday (day 14–21) – the phenomenon known as a proliferative flare.Conclusion:Model's high sensitivity to initial pO2 clearly emphasizes the need for experimental assessment of the pretreatment tumor hypoxia status, as it might be predictive of response to antiangiogenic therapies and the occurrence of proliferative flare. Experimental assessment of other model parameters would further improve understanding of patient response.
MO‐G‐17A‐05: PET Image Deblurring Using Adaptive Dictionary Learning
Authors: Valiollahzadeh S , Clark J , Mawlawi O 
Published: July 2014

Purpose:The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques.Methods:The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images.Results:For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively.Conclusion:PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care
MO‐G‐17A‐07: Improved Image Quality in Brain F‐18 FDG PET Using Penalized‐Likelihood Image Reconstruction Via a Generalized Preconditioned Alternating Projection Algorithm: The First Patient Results
Authors: Schmidtlein CR , Li S , Wu Z , Zhang J , Vogelsang L , Shen L , Xu Y , Feiglin D , Beattie B , Humm J , Krol A 
Published: July 2014

Purpose:To investigate the performance of a new penalized‐likelihood PET image reconstruction algorithm using the 11‐norm total‐variation (TV) sum of the 1st through 4th‐order gradients as the penalty. Simulated and brain patient data sets were analyzed.Methods:This work represents an extension of the preconditioned alternating projection algorithm (PAPA) for emission‐computed tomography. In this new generalized algorithm (GPAPA), the penalty term is expanded to allow multiple components, in this case the sum of the 1st to 4th order gradients, to reduce artificial piece‐wise constant regions (“staircase” artifacts typical for TV) seen in PAPA images penalized with only the 1st order gradient. Simulated data were used to test for “staircase” artifacts and to optimize the penalty hyper‐parameter in the root‐mean‐squared error (RMSE) sense. Patient FDG brain scans were acquired on a GE D690 PET/CT (370 MBq at 1‐hour post‐injection for 10 minutes) in time‐of‐flight mode and in all cases were reconstructed using resolution recovery projectors. GPAPA images were compared PAPA and RMSE‐optimally filtered OSEM (fully converged) in simulations and to clinical OSEM reconstructions (3 iterations, 32 subsets) with 2.6 mm XYGaussian and standard 3‐point axial smoothing post‐filters.Results:The results from the simulated data show a significant reduction in the “staircase” artifact for GPAPA compared to PAPA and lower RMSE (up to 35%) compared to optimally filtered OSEM. A simple power‐law relationship between the RMSE‐optimal hyper‐parameters and the noise equivalent counts (NEC) per voxel is revealed. Qualitatively, the patient images appear much sharper and with less noise than standard clinical images. The convergence rate is similar to OSEM.Conclusions:GPAPA reconstructions using the 11‐norm total‐variation sum of the 1st through 4th‐order gradients as the penalty show great promise for the improvement of image quality over that currently achieved with clinical OSEM reconstructions.
MO‐G‐18C‐01: BEST IN PHYSICS (IMAGING) – Novel Correction of Signal Modulation and Motion Artifacts in Temporal Bone BSSFP MRI
Authors: Hoff M , Xiang Q , Wilson G , Andre J 
Published: July 2014

Purpose:Phase cycled balanced steady state free precession (bSSFP) magnetic resonance (MR) images demonstrate high contrast in the temporal bone region, but are limited by motion artifacts stemming from globe motion, CSF and carotid artery pulsation, and by signal loss/modulation typical of bSSFP imaging of complex tissue environments. We sought to generate images delineating the fine morphology of temporal bone structures while minimizing signal loss and motion artifacts.Methods:Four axial 3D bSSFP MR images with Δθ = 0°, 90°, 180°, and 270° phase cycling respectively were acquired with a Philips Ingenia 3T MRI scanner in 2.3 minutes of total scan time. Other parameters included a 30° flip angle applied with a transmit/receive radiofrequency head coil, TE/TR = 4.2/2.1ms, receiver bandwidth = 890 Hz/pixel, and 180/180/120 matrix size and 1/1/1 mm voxel size along frequency/phase/slice directions. Complex image data were input into the Geometric Solution (GS) algorithm for calculation of artifact‐free bSSFP signal on a pixel‐by‐pixel basis. SNR was improved by linearizing the solution in a second‐pass regional optimization.Results:The GS eliminated the bSSFP signal intensity modulations while minimizing motion artifacts present in the original four images. The efficacy of the GS is validated through comparison with a complex average of the four original bSSFP images; unlike the GS, the complex average only moderates artifacts.Conclusion:The GS represents a novel method of correcting bSSFP MRI signal modulation and motion artifacts while maintaining contrast near the temporal bone. The technique is fast, easy to implement, and requires minimal post‐processing. This technique may inspire early clinical adoption, as it overcomes two major artifactual obstacles which have historically limited the widespread use of bSSFP MRI.
MO‐G‐18C‐06: MRI QC: Effects of Multi‐Channel Coil Element Failure On Image Quality
Authors: Li G , Dodge C , Reeve D 
Published: July 2014

Purpose:To quantitatively evaluate changes in phantom quality control (QC) measurements resulting from malfunctioning channels in multichannel brain coils during routine MRI QC.Methods:The large ACR MRI phantom was scanned on a 1.5T GE HDXt scanner using an 8‐channel brain array and on a 1.5T Siemens Aera using a 20‐channel head‐neck array. For the 8‐channel array, images were acquired by sequentially turning off an increasing number of adjacent channels. For the 20‐channel array, images were acquired with various combinations of active coil elements. Percent integral uniformity (PIU), signal‐to‐noise ratio (SNR) and ghosting ratio were measured with and without uniformity correction (UC).Results:For the 8‐channel array, SNR and PIU decreased with the number of inactive coil elements (57% and 34%, respectively). UC increased PIU to >82% and SNR to 80%, but also increased apparent ghosting due to background nonuniformity. For the 20‐channel array, the central pair of element groups (H3‐H4) closest to the phantom had the greatest impact on image quality. Without UC, PIU and SNR were similar when all groups or both H3‐H4 were active. Without UC, PIU decreased by 50% when either H3 or H4 was deactivated. Noise greatly decreased resulting in a ∼500% increase in SNR. Without UC, SNR decreased by 60% and PIU increased when both H3‐H4 were deactivated. PIU was most affected by UC (50%–160% increase). For both coils, ghosting ratio generally increased with UC but not above ACR limit (2.5%). With UC applied, PIU failed the ACR limit (87.5%) when 2 elements of the 8‐channel array or H3‐H4 were inactive.Conclusion:Channel failure for the two multichannel arrays tested was reflected in changes in SNR and PIU. For daily MR QC, setting coil‐specific SNR, PIU and ghosting QC limits to ensure the detection of malfunctioning elements is necessary due to coil design variations.
MO‐G‐18C‐08: Increasing Throughput of a Clinical MR Protocol for Prostate Cancer with a Factor of Two Using Multiband Imaging
Authors: Stemkens B , Andreychenko A , Crijns S , Sbrizzi A , Lagendijk J , van den Berg C , Tijssen R 
Published: July 2014

Purpose:To increase throughput of a clinical, multi‐parametric, prostate MRI protocol by individual scan acceleration using multiband excitation, in which multiple slices are excited simultaneously, and CAIPIRINHA.Methods:A healthy subject was scanned on a 3T clinical MR‐system with a 16‐channel torso receive array. A T2w‐TSE scan from a clinical prostate MR protocol was acquired to simulate three acceleration Methods: (1) traditional in‐plane parallel imaging (SENSE) (R=2), (2) multi‐slice parallel imaging (MS‐SENSE, R=2), in which two slices are acquired simultaneously and (3) multi‐slice CAIPIRINHA (R=2), in which one of the simultaneously acquired slices is shifted with FOV/2 with respect to the other slice. The normalized expected SNR was calculated within an ROI in the center of the slice. To demonstrate the feasibility of multiband MRI for the prostate, two 2D gradient echo slices (TE/TR=2.4ms/5.0ms, FA=20°, FOV=250×405mm2, voxelsize=0.5×0.5×4mm3, slice gap=20mm) were excited simultaneously using a numerically designed multiband RF pulse. The slices were acquired either with or without shifting the second slice by FOV/2 (i.e. CAIPIRINHA).Results:Simulations showed an expected relative SNR of 0.67 for in‐plane two‐fold SENSE acceleration, 0.14 for multi‐slice SENSE, and 0.94 for multi‐slice CAIPIRINHA within the ROI (SNR=1 for non‐accelerated acquisition). In‐vivo multiband experiments demonstrated that separation of the simultaneously acquired slices was practically impossible when slices folded on top of each other. However, the multi‐slice CAIPIRINHA experiment, showed low g‐factors and good image quality after unfolding.Conclusion:We demonstrated that the twofold multiband acceleration with CAIPIRINHA technique is the most SNR beneficial compared to the other acceleration techniques with only a moderate SNR penalty compared to the non‐accelerated acquisition. In‐vivo experiments demonstrated the feasibility of two‐fold CAIPIRINHA acceleration for pelvic MRI. This can be used to e.g. increase patient throughput, SNR or temporal resolution of the scans included in the multiparametric prostate MR protocol.