Cone‐beam computed tomography with a flat‐panel imager: Initial performance characterization

“Reg Refine” is a tool available in the MIM Maestro v6.4.5 platform (www.mimsoftware.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground‐truth models, the Pixel‐based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck. Image noise matched in both magnitude and texture to clinical CBCT scans was also added to each model to simulate the use case of CBCT–CT alignment. For lung, the results showed Reg Refine effective at improving registration accuracy when controlled by an expert user within the context of large lung deformation. CBCT noise was also shown to have no effect on DIR performance while using the MIM algorithm for this site. For head and neck, the results showed CBCT noise to have a large effect on the accuracy of registration, specifically for low‐contrast structures such as the brainstem and parotid glands. In these cases, the Reg Refine tool was able to improve the registration accuracy when controlled by an expert user. Several strategies for how to achieve these results have been outlined to assist other users and provide feedback for developers of similar tools.PACS number(s): 87.44.Qr, 87.57.nj, 87.57.c

Section: Methodsmentioning

confidence: 99%

Evaluation of the tool “Reg Refine” for user‐guided deformable image registration

Johnson

Padgett

Chen

et al. 2016

“…The integration of image‐guided radiotherapy tools, such as MV 14 , 15 and kV 16 , 17 on‐board imaging (OBI) mounted on linear accelerators, have increased efficiency and reproducibility of patient setup and target positioning. The kV OBI systems provide diagnostic quality images of a patient's internal anatomy with high positional accuracy and high contrast (18) .…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of patient setup uncertainty of stereotactic radiotherapy with the frameless 6D ExacTrac system using statistical modeling

Keeling

Hossain

Jin

et al. 2016

The purpose of this study is to evaluate patient setup accuracy and quantify individual and cumulative positioning uncertainties associated with different hardware and software components of the stereotactic radiotherapy (SRS/SRT) with the frameless 6D ExacTrac system. A statistical model is used to evaluate positioning uncertainties of the different components of SRS/SRT treatment with the Brainlab 6D ExacTrac system using the positioning shifts of 35 patients having cranial lesions. All these patients are immobilized with rigid head‐and‐neck masks, simulated with Brainlab localizer and planned with iPlan treatment planning system. Stereoscopic X‐ray images (XC) are acquired and registered to corresponding digitally reconstructed radiographs using bony‐anatomy matching to calculate 6D translational and rotational shifts. When the shifts are within tolerance (0.7 mm and 1°), treatment is initiated. Otherwise corrections are applied and additional X‐rays (XV) are acquired to verify that patient position is within tolerance. The uncertainties from the mask, localizer, IR ‐frame, X‐ray imaging, MV, and kV isocentricity are quantified individually. Mask uncertainty (translational: lateral, longitudinal, vertical; rotational: pitch, roll, yaw) is the largest and varies with patients in the range false(−2.07−3.71mm,−5.82−5.62mm,−5.84−3.61mm;−2.10−2.40∘,−2.23−2.60∘,and−2.7−3.00∘false) obtained from mean of XC shifts for each patient. Setup uncertainty in IR positioning (0.88, 2.12, 1.40 mm, and 0.64°, 0.83°, 0.96°) is extracted from standard deviation of XC. Systematic uncertainties of the frame (0.18, 0.25, −1.27mm, −0.32∘, 0.18°, and 0.47°) and localizer (−0.03, −0.01, 0.03 mm, and −0.03∘, 0.00°, −0.01∘) are extracted from means of all XV setups and mean of all XC distributions, respectively. Uncertainties in isocentricity of the MV radiotherapy machine are (0.27, 0.24, 0.34 mm) and kV imager (0.15, −0.4, 0.21 mm). A statistical model is developed to evaluate the individual and cumulative systematic and random positioning uncertainties induced by the different hardware and software components of the 6D ExacTrac system. The uncertainties from the mask, localizer, IR frame, X‐ray imaging, couch, MV linac, and kV imager isocentricity are quantified using statistical modeling.PACS number(s): 87.56.B‐, 87.59.B‐

“…It is most often integrated with other metrics to assess image quality for specific tasks, 6 , 7 , 8 , 9 and has been commonly utilized in the development, characterization, optimization, and comparison of many new imaging technologies such as computed radiography, (10) digital mammography, 11 , 12 storage phosphors for dental X‐ray, (13) and other devices in a preclinical 14 , 15 and clinical 16 , 17 environment.…”

Section: Introductionmentioning

confidence: 99%

Practical considerations for noise power spectra estimation for clinical CT scanners

Dolly

Chen

Anastasio

et al. 2016

Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose4 iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ∼0.15 mm−1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low‐frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task‐based image quality assessment.PACS number(s): 87.57.C‐, 87.57.Q‐