bluuebunny/crossref_metadata_2025 huggingface dataset:
SU‐F‐R‐39: Effects of Radiation Dose Reduction On Renal Cell Carcinoma Discrimination Using Multi‐Phasic CT Imaging
Authors: Wahi‐Anwar M , Young S , Lo P , Raman S , Kim H , Brown M , McNitt‐Gray M , Coy H , Ashen‐Garry D , Pace‐Soler E
Published: July 2016

Purpose:

A method to discriminate different types of renal cell carcinoma (RCC) was developed using attenuation values observed in multiphasic contrast‐enhanced CT. This work evaluates the sensitivity of this RCC discrimination task at different CT radiation dose levels.

Methods:

We selected 5 cases of kidney lesion patients who had undergone four‐phase CT scans covering the abdomen to the lilac crest. Through an IRB‐approved study, the scans were conducted on 64‐slice CT scanners (Definition AS/Definition Flash, Siemens Healthcare) using automatic tube‐current modulation (TCM). The protocol included an initial baseline unenhanced scan, followed by three post‐contrast injection phases. CTDIvol (32 cm phantom) measured between 9 to 35 mGy for any given phase. As a preliminary study, we limited the scope to the cortico‐medullary phase—shown previously to be the most discriminative phase. A previously validated method was used to simulate a reduced dose acquisition via adding noise to raw CT sinogram data, emulating corresponding images at simulated doses of 50%, 25%, and 10%. To discriminate the lesion subtype, ROIs were placed in the most enhancing region of the lesion. The mean HU value of an ROI was extracted and used to discriminate to the worst‐case RCC subtype, ranked in the order of clear cell, papillary, chromophobe and the benign oncocytoma.

Results:

Two patients exhibited a change of worst case RCC subtype between original and simulated scans, at 25% and 10% doses. In one case, the worst‐case RCC subtype changed from oncocytoma to chromophobe at 10% and 25% doses, while the other case changed from oncocytoma to clear cell at 10% dose.

Conclusion:

Based on preliminary results from an initial cohort of 5 patients, worst‐case RCC subtypes remained constant at all simulated dose levels except for 2 patients. Further study conducted on more patients will be needed to confirm our findings.

Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics; NIH Grant Support from: U01 CA181156
SU‐F‐R‐40: Robustness Test of Computed Tomography Textures of Lung Tissues to Varying Scanning Protocols Using a Realistic Phantom Environment
Authors: Lee S , Markel D , Hegyi G , El Naqa I
Published: July 2016

Purpose:

The reliability of computed tomography (CT) textures is an important element of radiomics analysis. This study investigates the dependency of lung CT textures on different breathing phases and changes in CT image acquisition protocols in a realistic phantom setting.

Methods:

We investigated 11 CT texture features for radiation‐induced lung disease from 3 categories (first‐order, grey level co‐ocurrence matrix (GLCM), and Law's filter). A biomechanical swine lung phantom was scanned at two breathing phases (inhale/exhale) and two scanning protocols set for PET/CT and diagnostic CT scanning. Lung volumes acquired from the CT images were divided into 2‐dimensional sub‐regions with a grid spacing of 31 mm. The distribution of the evaluated texture features from these sub‐regions were compared between the two scanning protocols and two breathing phases. The significance of each factor on feature values were tested at 95% significance level using analysis of covariance (ANCOVA) model with interaction terms included. Robustness of a feature to a scanning factor was defined as non‐significant dependence on the factor.

Results:

Three GLCM textures (variance, sum entropy, difference entropy) were robust to breathing changes. Two GLCM (variance, sum entropy) and 3 Law's filter textures (S5L5, E5L5, W5L5) were robust to scanner changes. Moreover, the two GLCM textures (variance, sum entropy) were consistent across all 4 scanning conditions. First‐order features, especially Hounsfield unit intensity features, presented the most drastic variation up to 39%.

Conclusion:

Amongst the studied features, GLCM and Law's filter texture features were more robust than first‐order features. However, the majority of the features were modified by either breathing phase or scanner changes, suggesting a need for calibration when retrospectively comparing scans obtained at different conditions. Further investigation is necessary to identify the sensitivity of individual image acquisition parameters.
SU‐F‐R‐45: The Prognostic Value of Radiotherapy Based On the Changes of Texture Features Between Pre‐Treatment and Post‐Treatment FDG PET Image for NSCLC Patients
Authors: Ma C , Yin Y
Published: July 2016

Purpose:

The purpose of this research is investigating which texture features extracted from FDG‐PET images by gray‐level co‐occurrence matrix(GLCM) have a higher prognostic value than the other texture features.

Methods:

21 non‐small cell lung cancer(NSCLC) patients were approved in the study. Patients underwent 18F‐FDG PET/CT scans with both pre‐treatment and post‐treatment. Firstly, the tumors were extracted by our house developed software. Secondly, the clinical features including the maximum SUV and tumor volume were extracted by MIM vista software, and texture features including angular second moment, contrast, inverse different moment, entropy and correlation were extracted using MATLAB.The differences can be calculated by using post‐treatment features to subtract pre‐treatment features. Finally, the SPSS software was used to get the Pearson correlation coefficients and Spearman rank correlation coefficients between the change ratios of texture features and change ratios of clinical features.

Results:

The Pearson and Spearman rank correlation coefficient between contrast and SUV maximum is 0.785 and 0.709. The P and S value between inverse difference moment and tumor volume is 0.953 and 0.942.

Conclusion:

This preliminary study showed that the relationships between different texture features and the same clinical feature are different. Finding the prognostic value of contrast and inverse difference moment were higher than the other three textures extracted by GLCM.
SU‐F‐R‐46: Predicting Distant Failure in Lung SBRT Using Multi‐Objective Radiomics Model
Authors: Zhou Z , Folkert M , Iyengar P , Zhang Y , Wang J
Published: July 2016

Purpose:

To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non‐small cell lung cancer (NSCLC) by using a new multi‐objective radiomics model.

Methods:

Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi‐objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi‐objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting‐based multi‐objective evolutionary computation algorithm II (NSGA‐II) is used for solving the multi‐objective optimization.

Results:

The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%.

Conclusion:

A new multi‐objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining all features.
SU‐F‐R‐48: Early Prediction of Pathological Response of Locally Advanced Rectal Cancer Using Perfusion CT:A Prospective Clinical Study
Authors: Nie K , Shi L , Yue N , Jabbour S , Kim S , Mao T , Qian L , Hu X , Sun X , Niu T
Published: July 2016

Purpose:

To prospectively evaluate the tumor vascularity assessed by perfusion CT for prediction of chemo‐radiation treatment (CRT) response in locally advanced rectal cancer (LARC).

Methods:

Eighteen consecutive patients (61.9±8.8 years, from March–June 2015) diagnosed with LARC who underwent 6–8 weeks CRT followed by surgery were included. The pre‐treatment perfusion CT was acquired after a 5s delay of contrast agent injection for 45s with 1s interval. A total of 7‐cm craniocaudal range covered the tumor region with 3‐mm slice thickness. The effective radiation dose is around 15mSv, which is about 1.5 the conventional abdomen/pelvis CT dose. The parametric map of blood flow (BF), blood volume (BV), mean transit time (MTT), permeability (PMB), and maximum intensity map (MIP) were obtained from commercial software (Syngo‐CT 2011A, Siemens). An experienced radiation oncologist outlined the tumor based on the pre‐operative MR and pathologic residual region, but was blinded with regards to pathological tumor stage. The perfusion parameters were compared to histopathological response quantified by tumor regression grade as good‐responder (GR, TRG 0‐1) vs. non‐good responder (non‐GR). Furthermore, the predictive value for pathological complete response (pCR) was also investigated.

Results:

Both BV (p=0.02) and MTT (P=0.02) was significantly higher and permeambility was lower (p=0.004) in the good responders. The BF was higher in GR group but not statistically significant. Regarding the discrimination of pCR vs non‐pCR, the BF was higher in the pCR group (p=0.08) but none of those parameters showed statistically significant differences.

Conclusion:

BV and MTT can discriminate patients with a favorable response from those that fail to respond well, potentially selecting high‐risk patients with resistant tumors that may benefit from an aggressive preoperative treatment approach. However, future studies with more patient data are needed to verify the prognostic value of perfusion CT especially for pCR prediction.

This work is supported by the National High‐tech R&D program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917), Natural Science Foundation of China (NSFC Grant No. 81201091).
SU‐F‐R‐49: A Novel Kinetic Model for Prediction of Tumor Local Control for Patients with Lung Cancer
Authors: Zhong H , Siddiqui S , Zhao B , Li H , Barton K , Siddiqui F , Movsas B , Chetty I
Published: July 2016

Purpose:

Modeling tumor control probability (TCP) can help optimize treatment plans for better treatment outcomes. This study sought to quantify the radiobiological parameters of the TCP model for patients with lung cancer.

Methods:

A two‐compartment kinetic model was developed to model tumor regression for five NSCLC patients. The model has three parameters: cell survival fraction, dead‐cell‐resolving time and tumor doubling time. The last one was extended to a function of tumor volume. Each of these patients was treated with 2 Gyx33 fractions. Daily CBCT images were acquired during the course of treatment. Gross tumor volume (GTV) was delineated on each of these CBCT images, and the visible tumor volumes were fitted to the kinetic model to optimize the parameters, where the Jacobian of this model was constrained by daily tumor volumetric changes.

Results:

Among the five patients, three had tumor recurrence: 455, 520 and 590 days after the completion of treatment. Recurrent tumor volumes were, respectively, 28.9, 13.7 and 4.8 cm3, measured at distant locations. With the assumption of cell density = 108 cells/cm3, tumor doubling times required for circulating tumor cells to progress to the recurrent volumes are 16.1, 19.2 and 23.1 days. By fitting the kinetic model with 33 measured tumor volumes, the average cell survival fraction for the five patients is 0.5±0.21, and tumor doubling times are in the range of 10∼35 days, comparable to the doubling time derived from tumor recurrence data. The doubling times modeled from invisible cells are different from the median (357 days) or mean (166 days) of the doubling time measured directly from visible tumor volumes.

Conclusion:

A kinetic model has been developed to simulate the process of tumor progressing from invisible cells to visible tumor volumes. This model may be useful for TCP prediction for patients with locally advanced lung cancer.
SU‐F‐R‐52: A Comparison of the Performance of Radiomic Features From Free Breathing and 4DCT Scans in Predicting Disease Recurrence in Lung Cancer SBRT Patients
Authors: Huynh E , Coroller T , Narayan V , Agrawal V , Romano J , Franco I , Hou Y , Mak R , Aerts H
Published: July 2016

Purpose:

There is a clinical need to identify patients who are at highest risk of recurrence after being treated with stereotactic body radiation therapy (SBRT). Radiomics offers a non‐invasive approach by extracting quantitative features from medical images based on tumor phenotype that is predictive of an outcome. Lung cancer patients treated with SBRT routinely undergo free breathing (FB image) and 4DCT (average intensity projection (AIP) image) scans for treatment planning to account for organ motion. The aim of the current study is to evaluate and compare the prognostic performance of radiomic features extracted from FB and AIP images in lung cancer patients treated with SBRT to identify which image type would generate an optimal predictive model for recurrence.

Methods:

FB and AIP images of 113 Stage I‐II NSCLC patients treated with SBRT were analysed. The prognostic performance of radiomic features for distant metastasis (DM) was evaluated by their concordance index (CI). Radiomic features were compared with conventional imaging metrics (e.g. diameter). All p‐values were corrected for multiple testing using the false discovery rate.

Results:

All patients received SBRT and 20.4% of patients developed DM. From each image type (FB or AIP), nineteen radiomic features were selected based on stability and variance. Both image types had five common and fourteen different radiomic features. One FB (CI=0.70) and five AIP (CI range=0.65–0.68) radiomic features were significantly prognostic for DM (p<0.05). None of the conventional features derived from FB images (range CI=0.60–0.61) were significant but all AIP conventional features were (range CI=0.64–0.66).

Conclusion:

Features extracted from different types of CT scans have varying prognostic performances. AIP images contain more prognostic radiomic features for DM than FB images. These methods can provide personalized medicine approaches at low cost, as FB and AIP data are readily available within a large number of radiation oncology departments.

R.M. had consulting interest with Amgen (ended in 2015).
SU‐F‐R‐53: CT‐Based Radiomics Analysis of Non‐Small Cell Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy
Authors: Huynh E , Coroller T , Narayan V , Agrawal V , Hou Y , Romano J , Franco I , Mak R , Aerts H
Published: July 2016

Purpose:

Stereotactic body radiation therapy (SBRT) is the standard of care for medically inoperable non‐small cell lung cancer (NSCLC) patients and has demonstrated excellent local control and survival. However, some patients still develop distant metastases and local recurrence, and therefore, there is a clinical need to identify patients at high‐risk of disease recurrence. The aim of the current study is to use a radiomics approach to identify imaging biomarkers, based on tumor phenotype, for clinical outcomes in SBRT patients.

Methods:

Radiomic features were extracted from free breathing computed tomography (CT) images of 113 Stage I‐II NSCLC patients treated with SBRT. Their association to and prognostic performance for distant metastasis (DM), locoregional recurrence (LRR) and survival was assessed and compared with conventional features (tumor volume and diameter) and clinical parameters (e.g. performance status, overall stage). The prognostic performance was evaluated using the concordance index (CI). Multivariate model performance was evaluated using cross validation. All p‐values were corrected for multiple testing using the false discovery rate.

Results:

Radiomic features were associated with DM (one feature), LRR (one feature) and survival (four features). Conventional features were only associated with survival and one clinical parameter was associated with LRR and survival. One radiomic feature was significantly prognostic for DM (CI=0.670, p<0.1 from random), while none of the conventional and clinical parameters were significant for DM. The multivariate radiomic model had a higher median CI (0.671) for DM than the conventional (0.618) and clinical models (0.617).

Conclusion:

Radiomic features have potential to be imaging biomarkers for clinical outcomes that conventional imaging metrics and clinical parameters cannot predict in SBRT patients, such as distant metastasis. Development of a radiomics biomarker that can identify patients at high‐risk of recurrence could facilitate personalization of their treatment regimen for an optimized clinical outcome.

R.M. had consulting interest with Amgen (ended in 2015).
SU‐F‐R‐54: CT‐Texture Based Early Tumor Treatment Response Assessment During Radiation Therapy Delivery: Small Cell Versus Non‐Small Cell Lung Cancers
Authors: Paul J , Gore E , Li X
Published: July 2016

Purpose:

Tumor treatment response may potentially be assessed during radiation therapy (RT) by analyzing changes in CT‐textures. We investigated the different early RT‐responses between small cell (SCLC) and non‐small cell lung cancer (NSCLC) as assessed by CT‐texture.

Methods:

Daily diagnostic‐quality CT acquired during routine CT‐guided RT using a CT‐on‐Rails for 13‐NSCLC and 5‐SCLC patients were analyzed. These patient had ages ranging from 45–78 and 38–63 years, respectively, for NSCLC and SCLC groups, and tumor‐stages ranging from T2‐T4, and were treated with either RT or chemotherapy and RT with 45–66Gy/ 20–34 fractions. Gross‐tumor volume (GTV) contour was generated on each daily CT by populating GTV contour from simulation to daily CTs with manual editing if necessary. CT‐texture parameters, such as Hounsfield Unit (HU) histogram, mean HU, skewness, kurtosis, entropy, and short‐run high‐gray level emphasis (SRHGLE), were calculated in GTV from each daily CT‐set using an in house software tool. Difference in changes of these texture parameters during RT between NSCLC and SCLC was analyzed and compared with GTV volume changes.

Results:

Radiation‐induced changes in CT‐texture were different between SCLC and NSCLC. Average changes from first to the last fractions for NSCLC and SCLC in GTV were 28±10(12–44) and 30±15(11–47) HU (mean HU reduction), 12.7% and 18.3% (entropy), 50% and 55% (SRHGLE), 19% and 22% (kurtosis), and 5.2% and 22% (skewness), respectively. Good correlation in kurtosis changes and GTV was seen (R2=0.8923) for SCLC, but not for NSCLC (R2=0.4748). SCLC had better correlations between GTV volume reduction and entropy (SCLC R2=0.847; NSCLC R2=0.6485), skewness (SCLC R2=0.935; NSCLC R2=0.7666), or SRHGLE (SCLC R2=0.9619; NSCLC R2=0.787).

Conclusion:

NSCLC and SCLC exhibited different early RT‐responses as assessed by CT‐texture changes during RT‐delivery. The observed larger changes in various CT‐texture parameters for SCLC indicate that SCLC may respond to RT more rapid than NSCLC.
SU‐F‐I‐07: CBCT Denoising Based On Adaptive Dictionary Learning Algorithms
Authors: Qin Shaohua , Yin Juan , Li Hongsheng , Chen Jinhu , Yin Yong , Li Dengwang
Published: July 2016

Purpose:

We proposed a new dictionary learning algorithm (AK‐SVD) based on K‐SVD. AK‐SVD can denoise the CBCT image, and did not need the noise information as prior knowledge.

Methods:

The AK‐SVD had two steps: signal sparse representation, and then dictionary optimization. The CBCT image was sparse, and there were limited big coefficients. The other coefficients were zero or near zero. In the sparse representation step of traditional K‐SVD, the noise variance was used as a threshold to select the big representation coefficients. This increased the complexity of the algorithm. The denoising result also was affected by the accuracy of the noise variance estimation, especially in non‐Gaussian noise. In AK‐SVD we used the average of the existing big coefficients as a threshold. The new found coefficient was compared with the threshold. If it was bigger than this threshold, it will be determined as the big coefficient, and be added to the set of existing big coefficients. The finding process continued. If it was smaller than this threshold, the finding process was end.This threshold was not related to the noise variance, and based on this method we improved the traditional K‐SVD.

Results:

In the synthetic experiments about designing dictionary from synthetic signals, the correct rate of dictionary learning by the AK‐SVD was similar with the ideal results of the K‐SVD where the noise variance was known. However, the AK‐SVD algorithm did not need to evaluate the noise variance, so it had lower computational complexity and wider adaptability. In the denoising experiment about the CBCT image corrupted by the non‐Gaussian noise, AK‐SVD has an advantage in terms of texture.

Conclusion:

The AK‐SVD can work well with the noise variance unknown, and it had lower computational complexity and wider adaptability than K‐SVD.

This work was jointly supported by National Natural Science Foundation of China (61471226), Natural Science Foundation for Distinguished Young Scholars of Shandong Province (JQ201516), China Postdoctoral Science Foundation (2015T80739, 2014M551949), and research funding from Jinan (201401221).
SU‐F‐I‐11: Software Development for 4D‐CBCT Research of Real‐Time‐Image Gated Spot Scanning Proton Therapy
Authors: Fujii T , Matsuura T , Takao S , Miyamoto N , Matsuzaki Y , Fujii Y , Umegaki K , Shimizu S , Shirato H
Published: July 2016

Purpose:

To acquire correct information for inside the body in patient positioning of Real‐time‐image Gated spot scanning Proton Therapy (RGPT), utilization of tomographic image at exhale phase of patient respiration obtained from 4‐dimensional Cone beam CT (4D‐CBCT) has been desired. We developed software named “Image Analysis Platform” for 4D‐CBCT researches which has technique to segment projection‐images based on 3D marker position in the body. The 3D marker position can be obtained by using two axes CBCT system at Hokkaido University Hospital Proton Therapy Center. Performance verification of the software was implemented.

Methods:

The software calculates 3D marker position retrospectively by using matching positions on pair projection‐images obtained by two axes fluoroscopy mode of CBCT system. Log data of 3D marker tracking are outputted after the tracking. By linking the Log data and gantry‐angle file of projection‐image, all projection‐images are equally segmented to spatial five‐phases according to marker 3D position of SI direction and saved to specified phase folder. Segmented projection‐images are used for CBCT reconstruction of each phase. As performance verification of the software, test of segmented projection‐images was implemented for sample CT phantom (Catphan) image acquired by two axes fluoroscopy mode of CBCT. Dummy marker was added on the images. Motion of the marker was modeled to move in 3D space. Motion type of marker is sin4 wave function has amplitude 10.0 mm/5.0 mm/0 mm, cycle 4 s/4 s/0 s for SI/AP/RL direction.

Results:

The marker was tracked within 0.58 mm accuracy in 3D for all images, and it was confirmed that all projection‐images were segmented and saved to each phase folder correctly.

Conclusion:

We developed software for 4D‐CBCT research which can segment projection‐image based on 3D marker position. It will be helpful to create high quality of 4D‐CBCT reconstruction image for RGPT.
SU‐F‐I‐13: Correction Factor Computations for the NIST Ritz Free Air Chamber for Medium‐Energy X Rays
Authors: Bergstrom P
Published: July 2016

Purpose:

The National Institute of Standards and Technology (NIST) uses 3 free‐air chambers to establish primary standards for radiation dosimetry at x‐ray energies. For medium‐energy × rays, the Ritz free‐air chamber is the main measurement device. In order to convert the charge or current collected by the chamber to the radiation quantities air kerma or air kerma rate, a number of correction factors specific to the chamber must be applied.

Methods:

We used the Monte Carlo codes EGSnrc and PENELOPE.

Results:

Among these correction factors are the diaphragm correction (which accounts for interactions of photons from the x‐ray source in the beam‐defining diaphragm of the chamber), the scatter correction (which accounts for the effects of photons scattered out of the primary beam), the electron‐loss correction (which accounts for electrons that only partially expend their energy in the collection region), the fluorescence correction (which accounts for ionization due to reabsorption ffluorescence photons and the bremsstrahlung correction (which accounts for the reabsorption of bremsstrahlung photons). We have computed monoenergetic corrections for the NIST Ritz chamber for the 1 cm, 3 cm and 7 cm collection plates.

Conclusion:

We find good agreement with other's results for the 7 cm plate. The data used to obtain these correction factors will be used to establish air kerma and it's uncertainty in the standard NIST x‐ray beams.
SU‐F‐I‐14: 3D Breast Digital Phantom for XACT Imaging
Authors: Tang S , Chen Y , Ahmad S , Yang K , Laaroussi R , Chen J , Samant P , Xiang L
Published: July 2016

Purpose:

The X‐ray induced acoustic computed tomography (XACT) is a new imaging modality which combines X‐ray contrast and high ultrasonic resolution in a single modality. Using XACT in breast imaging, a 3D breast volume can be imaged by only one pulsed X‐ray radiation, which could dramatically reduce the imaging dose for patients undergoing breast cancer screening and diagnosis. A 3D digital phantom that contains both X‐ray properties and acoustic properties of different tissue types is indeed needed for developing and optimizing the XACT system. The purpose of this study is to offer a realistic breast digital phantom as a valuable tool for improving breast XACT imaging techniques and potentially leading to better diagnostic outcomes.

Methods:

A series of breast CT images along the coronal plane from a patient who has breast calcifications are used as the source images. A HU value based segmentation algorithm is employed to identify breast tissues in five categories, namely the skin tissue, fat tissue, glandular tissue, chest bone and calcifications. For each pixel, the dose related parameters, such as material components and density, and acoustic related parameters, such as frequency‐dependent acoustic attenuation coefficient and bandwidth, are assigned based on tissue types. Meanwhile, other parameters which are used in sound propagation, including the sound speed, thermal expansion coefficient, and heat capacity are also assigned to each tissue.

Results:

A series of 2D tissue type image is acquired first and the 3D digital breast phantom is obtained by using commercial 3D reconstruction software. When giving specific settings including dose depositions and ultrasound center frequency, the X‐ray induced initial pressure rise can be calculated accordingly.

Conclusion:

The proposed 3D breast digital phantom represents a realistic breast anatomic structure and provides a valuable tool for developing and evaluating the system performance for XACT.
SU‐F‐I‐15: Evaluation of a New MR‐Compatible Respiratory Motion Device at 3T
Authors: Soliman A , Chugh B , Keller B , Sahgal A , Song W
Published: July 2016

Purpose:

Recent advances in MRI‐guided radiotherapy has inspired the development of MRI‐compatible motion devices that simulate patient periodic motion in the scanner, particularly respiratory motion. Most commercial devices rely on non MR‐safe ferromagnetic stepper motors which are not practical for regular QA testing. This work evaluates the motion performance of a new fully MRI compatible respiratory motion device at 3T.

Methods:

The QUASAR™ MRI‐compatible respiratory motion phantom has been recently developed by Modus QA Inc., London, ON, Canada. The prototype is constructed from diamagnetic materials with linear motion generated using MRI‐compatible piezoelectric motors that can be safely inserted in the scanner bore. The tumor was represented by a fillable sphere and is attached to the linear motion generator. The spherical tumor‐representative and its surroundings were filled with different concentrations of MnCl2 to produce realistic relaxation times. The motion was generated along the longitudinal (H/F) axis of the bore using sinusoidal reference waveform (amplitude = 15 mm, frequency 0.25 Hz). Imaging was then performed on 3T Philips Achieva using a 32‐channel cardiac coil. Fast 2D spoiled gradient‐echo was used with a spatial resolution of 1.8 × 1.8 mm2 and slice thickness of 4 mm. The motion waveform was then measured on the resultant image series by tracking the centroid of the sphere through the time series. This image‐derived measured motion was compared to the software‐generated reference waveform.

Results:

No visible distortions from the device were observed on the images. Excellent agreement between the measured and the reference waveforms were obtained. Negligible motion was observed in the lateral (R/L) direction.

Conclusion:

Our investigation demonstrates that this piezo‐electric motor design is effective at simulating periodic motion and is a potential candidate for MRI‐radiotherapy respiratory motion simulation. Future work should focus on evaluating non‐sinusoidal waveforms, fast 3D pulse sequences, and perform dosimetric QA.
SU‐F‐I‐17: White Matter Track Based Analysis at Limbic Abnormalities in Cognitive Impairment
Authors: Juh R , Suh T , Han J , Kim C , Oh C
Published: July 2016

Purpose:

Mild cognitive impairment with depression (MCID) is common and associated with disability and cognitive impairment, with high probability of relapse. Hypothesize that a sign of WM disintegration would be observed in MCID than MCI nondepression (MCIND), especially in frontal and limbic regions and patients with depression would show reduced GM density in the hippocampus, amygdala, anterior gyrus cingulate, and dorsolateral prefrontal cortex (DLPFC) and dorsomedial prefrontal cortex (DMPFC) the abnormalities of long association fiber tracts integrity are correlated with geriatric depression.

Methods:

Forty‐two subjects (20 nondepressed, 22 depressed) underwent DTI and cognitive assessment. Depression was initially assessed by means of the Korean version of the 30‐item Geriatric Depression Scale (K‐GDS). All patients scoring 19 or higher on the K‐GDS were screened as depressed. An automated tract‐based statistical analysis method was used to derive estimates of fractional anisotropy (FA) for each subject. Group effects and correlations with clinical features on DTI parameters were examined.

Results:

We found cross‐sectional differences in WM tract disintegration on posterior cingulum, splenium of corpus callosum, uncinate fasciculus, genu, thalamus, internal and external capsule of limbic in MCID. These results support changes in the structural integrity of neuronal cells in these specific important brain regions constituting a fronto‐limbic‐cerebellar network during depressive and in particular during the course of depression. The different parts of the frontal lobes have afferent and efferent connections with other neocortical, limbic, and subcortical regions and participate in the limbic‐cortico‐striatal‐pallidal‐thalamic circuits.

Conclusion:

Findings are suggestive of loss of integrity in WM fiber within frontal, temporal and limbic regions, increasing the evidence that implicates disruptions to the limbic‐orbitofrontal networks in the pathogenesis of MCID. These neuroanatomical circuits play an important role in the regulation and modulation of affect and emotion, and contribute to the pathogenesis of late‐life depression.
SU‐F‐I‐18: Altered Visual Cortex Responses by Wide‐View FMRI in Primary Open Angle Glaucoma Patients
Authors: Zhou W , Muir E , Chalfin S , Nagi K , Clarke G , Duong T
Published: July 2016

Purpose:

Primary open‐angle glaucoma (POAG) is a common ocular disorder with progressive retinal ganglion cell loss. This study explored the application of wide view fMRI to evaluate neural activity of the whole visual cortex and its relation with visual field functions in POAG patients.

Methods:

Nine subjects with diagnosed POAG (age: 59.9±10.9 y.o., 4 males) and nine age‐matched healthy volunteers (age: 58.0±4.6 y.o., 6 males) were scanned on a 3T Siemens scanner. For each subject, wide view (110°) visual stimulation of a rotating wedge was performed during fMRI acquisition. T1‐MPRAGE was used to acquire anatomical images as a reference. fMRI signal percent change was calculated for quadrant areas of the visual field for each subject. Group comparison was performed using non‐parametric Mann‐Whitney test with Bonferroni correction. In POAG patients, indices of visual field score and retina layer thickness from each quadrant were correlated with the corresponding fMRI percent changes by Spearman's method. P<0.05 was the threshold for statistical significance.

Results:

The fMRI signal percent changes of moderate (0.65±0.10, N=9) and advanced (0.44±0.05, N=2) POAG quadrant visual cortex showed significant decrease (moderate: P<0.05; severe: P<0.01) compared with normal (1.02±0.05, N=36). Slightly reduced fMRI signal in early POAG (0.97±0.07, N=25) was observed but not statistically significant. For POAG patients, the fMRI responses per quadrant activation were positively correlated with the clinical visual field scores (ρ=0.48, P<0.005); no correlation was found between fMRI response and retinal nerve layer thickness (ρ=0.01, P>0.05).

Conclusion:

fMRI revealed a functional decrease of the neural activity in the visual cortex in moderate and advanced POAG. The correlation of fMRI signal with visual field test results indicate fMRI could be used as an objective tool to quantify visual field defects for glaucoma patients.
SU‐F‐I‐21: Unfolding of Aliased Dynamic Acquisitions for the Acceleration of Breast Dynamic‐Contrast Enhanced MRI (DCE‐MRI)
Authors: Pineda F , Medved M , Fan X , Karczmar G
Published: July 2016

Purpose:

To determine the feasibility of increasing temporal resolution in breast DCE‐MRI by reducing the field‐of‐view (FOV) and unfolding the aliased images using un‐aliased subtraction images as guides.

Methods:

Experience with an ‘ultrafast’ protocol for breast DCE‐MRI (6–10s acquisitions) showed that the number of significantly enhancing voxels was very low (<7% of the FOV) in the first 30–45s after contrast injection. The sparse enhancement in these images suggests that if the FOV is reduced in the phase‐encoding direction (reducing acquisition time) and aliasing occurs, the likelihood of two significantly‐enhancing voxels overlapping in the aliased image is relatively low. Aliased images were simulated from the first 30s of fully‐sampled ultrafast acquisitions. Five cases with known enhancing lesions and moderate or marked parenchymal enhancement were selected (i.e. more enhancing voxels). FOV's were reduced by 40%. Subtractions were generated for all images, and filtered to determine significant enhancement. The locations that each aliased enhancing voxel could have come from were determined. If only one of the possible locations was enhanced at the later, fully sampled, time‐point, the aliased voxel was copied to that location. If both were enhanced overlap likely occurred and the voxel was copied to both locations in the ‘unfolded’ image.

Results:

On average 2.2% (range 0% – 9.81%) of the significantly enhancing voxels, above the chest wall, overlapped in the aliased images. This relatively low number means that it's possible to generate unfolded images that are visually similar to the full‐FOV images.

Conclusion:

These simulations show that it is possible to mostly recover early post‐contrast breast DCE‐MRI images from aliased acquisitions. While this method cannot separate the signal from each overlapping voxel in the aliased acquisitions, in‐progress work will address this by changing the aliasing factor for each time‐point, giving each aliased voxel a distinct ‘trajectory’.
SU‐F‐I‐24: Feasibility of Magnetic Susceptibility to Relative Electron Density Conversion Method for Radiation Therapy
Authors: Ito K , Kadoya N , Chiba M , Sato K , Nagasaka T , Yamanaka K , Dobashi S , Takeda K , Matsushita H , Jingu K
Published: July 2016

Purpose:

The aim of this study is to develop radiation treatment planning using magnetic susceptibility obtained from quantitative susceptibility mapping (QSM) via MR imaging. This study demonstrates the feasibility of a method for generating a substitute for a CT image from an MRI.

Methods:

The head of a healthy volunteer was scanned using a CT scanner and a 3.0 T MRI scanner. The CT imaging was performed with a slice thickness of 2.5 mm at 80 and 120 kV (dual‐energy scan). These CT images were converted to relative electron density (rED) using the CT‐rED conversion table generated by a previous dual‐energy CT scan. The CT‐rED conversion table was generated using the conversion of the energy‐subtracted CT number to rED via a single linear relationship. One T2 star‐weighted 3D gradient echo‐based sequence with four different echo times images was acquired using the MRI scanner. These T2 star‐weighted images were used to estimate the phase data. To estimate the local field map, a Laplacian unwrapping of the phase and background field removal algorithm were implemented to process phase data. To generate a magnetic susceptibility map from the local field map, we used morphology enabled dipole inversion method. The rED map was resampled to the same resolution as magnetic susceptibility, and the magnetic susceptibility‐rED conversion table was obtained via voxel‐by‐voxel mapping between the magnetic susceptibility and rED maps.

Results:

A correlation between magnetic susceptibility and rED is not observed through our method.

Conclusion:

Our results show that the correlation between magnetic susceptibility and rED is not observed. As the next step, we assume that the voxel of the magnetic susceptibility map comprises two materials, such as water (0 ppm) and bone (‐2.2 ppm) or water and marrow (0.81ppm). The elements of each voxel were estimated from the ratio of the two materials.
SU‐F‐I‐28: CTQA: Clinically Relevant Testing
Authors: Och J
Published: July 2016

Purpose:

to develop a CTQA program centered on Quantitative Image Quality and Patient Dose.

Methods:

Image Noise was selected as the Image Quality metric, defined as the standard deviation of an ROI, drawn within designated areas on clinical images of routine brain and abdomen scans. Patient Dose was defined as the dose to the particular “slice” used for the noise measurement. Baseline performance values were acquired for the scans, for each CT scanner in the system. For abdomens, the population was divided into four size categories. Upon completion of establishing baseline performance, quarterly sampling of each scanner population was initiated. Values outside the expected range are investigated, and, if indicated, interventions are made, and the results monitored.

Results:

In the two years since its inception, we have discovered numerous situations which required intervention, i.e. protocol adjustments to regain baseline performance. During the same period of time, no deficiencies were reported on any annual CT QA inspections.

Conclusion:

Monitoring Image Noise and Patient Dose for consistency are effective QA tools. They are easy to implement and imbue clinical relevance to the QA program.
SU‐F‐I‐33: Estimating Radiation Dose in Abdominal Fat Quantitative CT
Authors: Li X , Yang K , Liu B
Published: July 2016

Purpose:

To compare size‐specific dose estimate (SSDE) in abdominal fat quantitative CT with another dose estimate Dsize,L that also takes into account scan length.

Methods:

This study complied with the requirements of the Health Insurance Portability and Accountability Act. At our institution, abdominal fat CT is performed with scan length = 1 cm and CTDIvol = 4.66 mGy (referenced to body CTDI phantom). A previously developed CT simulation program was used to simulate single rotation axial scans of 6–55 cm diameter water cylinders, and dose integral of the longitudinal dose profile over the central 1 cm length was used to predict the dose at the center of one‐cm scan range. SSDE and Dsize,L were assessed for 182 consecutive abdominal fat CT examinations with mean water‐equivalent diameter (WED) of 27.8 cm ± 6.0 (range, 17.9 ‐ 42.2 cm). Patient age ranged from 18 to 75 years, and weight ranged from 39 to 163 kg.

Results:

Mean SSDE was 6.37 mGy ± 1.33 (range, 3.67–8.95 mGy); mean Dsize,L was 2.99 mGy ± 0.85 (range, 1.48 ‐ 4.88 mGy); and mean Dsize,L/SSDE ratio was 0.46 ± 0.04 (range, 0.40 ‐ 0.55).

Conclusion:

The conversion factors for size‐specific dose estimate in AAPM Report No. 204 were generated using 15 ‐ 30 cm scan lengths. One needs to be cautious in applying SSDE to small length CT scans. For abdominal fat CT, SSDE was 80–150% higher than the dose of 1 cm scan length.
SU‐F‐I‐35: Establishment of DRL and Dose Indicators for Optimization of Protectionin Pediatric CT in Saudi Arabia
Authors: Al‐Haj A , Lobriguito A
Published: July 2016

Purpose:

To establish the national dose reference level for pediatric CT procedures in Saudi Arabia. It also aims to determine dose indicators that can be used for optimization of protection.

Methods:

A retrospective study of patient demographic data, CT exposure parameters and dose indicators in DLP and CTDI volume for pediatric patients was undertaken. The CT procedures were chest, brain and abdomen. A total of 550 patients from 5 medical centers were included in the study. The patients were grouped into age groups of 0–1, 1–5, 5–10 and 10–15 years old. The dose reference levels for each procedure were determined using the 75 percentile of all the recorded DLP values. The effective dose for each age group was estimated from the mean DLP per procedure. A dose conversion coefficient in effective dose per DLP for each age group per procedure was derived. Normalization of the mAs and CTD1 volume was done and analyzed for evaluating dose optimization.

Results:

The DLP values vary from center to center for different age groups for the same procedure by a factor of about 2 to 4. Pooling all age categories, the DPL values are 42–400 mGy cm for chest, 500–1,900 mGy cm for brain and 40–850 mGy cm for abdomen. One of the factors that gave the high variation is the use of the adult protocol for some age groups especially for ages greater than 10. The effective dose of chest for 0 to 1 years old is higher than the 5 to 10 years by about 8percent.

Conclusion:

The use of the DRL values, effective dose and dose conversion coefficient for each procedure are best indicators for procedures that need optimization of protection. There is a need to formulate a national guideline on the use of DRL for pediatric CT procedures.
SU‐F‐I‐46: Optimizing Dose Reduction in Adult Head CT Protocols While Maintaining Image Quality in Postmortem Head Scans
Authors: Lipnharski I , Carranza C , Quails N , Correa N , Rajderkar D , Bennett J , Rill L , Arreola M
Published: July 2016

Purpose:

To optimize adult head CT protocol by reducing dose to an appropriate level while providing CT images of diagnostic quality.

Methods:

Five cadavers were scanned from the skull base to the vertex using a routine adult head CT protocol (120 kVp, 270 mA, 0.75 s rotation, 0.5 mm × 32 detectors, 70.8 mGy CTDIvol) followed by seven reduced‐dose protocols with varying combinations of reduced tube current, reduced rotation time, and increased detectors with CTDIvol ranging from 38.2 to 65.6 mGy. Organ doses were directly measured with 21 OSL dosimeters placed on the surface and implanted in the head by a neurosurgeon. Two neuroradiologists assessed grey‐white matter differentiation, fluid space, ventricular size, midline shift, brain mass, edema, ischemia, and skull fractures on a three point scale: (1) Unacceptable, (2) Borderline Acceptable, and (3) Acceptable.

Results:

For the standard scan, doses to the skin, lens of the eye, salivary glands, thyroid, and brain were 37.55 mGy, 49.65 mGy, 40.67 mGy, 4.63 mGy, and 27.33 mGy, respectively. Two cadavers had cerebral edema due to changing dynamics of postmortem effects, causing the grey‐white matter differentiation to appear less distinct. Two cadavers with preserved grey‐white matter received acceptable scores for all image quality features for the protocol with a CTDIvol of 57.3 mGy, allowing organ dose savings ranging from 34% to 45%. One cadaver allowed for greater dose reduction for the protocol with a CTDIvol of 42 mGy.

Conclusion:

Efforts to optimize scan protocol should consider both dose and clinical image quality. This is made possible with postmortem subjects, whose brains are similar to patients, allowing for an investigation of ideal scan parameters. Radiologists at our institution accepted scan protocols acquired with lower scan parameters, with CTDIvol values closer to the American College of Radiology's (ACR) Achievable Dose level of 57 mGy.