Browsing by Author "Solomon, Justin"

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Open Access
Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard
(Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952P, 2021-02-15) Ria, Francesco; Smith, Taylor; Abadi, Ehsan; Solomon, Justin; Samei, Ehsan
Image quality estimation is crucial in modern CT with noise magnitude playing a key role. Several methods have been proposed to estimate noise surrogates in vivo. This study aimed to ascertain the accuracy of three different noise-magnitude estimation methods. We used ensemble noise as the ground truth. The most accurate approach to assess ensemble noise is to scan a patient repeatedly and assess the noise for each pixel across the ensemble of images. This process is ethically undoable on actual patients. In this study, we surmounted this impasse using Virtual Imaging Trials (VITs) that simulate clinical scenarios using computer-based simulations. XCAT phantoms were imaged 47 times using a scanner-specific simulator (DukeSim) and reconstructed with filtered back projection (FBP) and iterative (IR) algorithms. Noise magnitudes were calculated in lung (ROIn), soft tissues (GNI), and air surrounding the patient (AIRn), applying different HU thresholds and techniques. The results were compared with the ensemble noise magnitudes within soft tissue (En). For the FBP-reconstructed images, median En was 30.6 HU; median ROIn was 46.6 HU (+52%), median GNI was 40.1 HU (+31%), and median AIRn 25.1 HU (-18%). For the IR images, median En was 19.5 HU; median ROIn was 31.2 HU (+60%), median GNI was 25.1 HU (+29%), and median AIRn 18.8 HU (-4%). Compared to ensemble noise, GNI and ROIn overestimate the tissue noise, while AIRn underestimates it. Air noise was least representative of variations in tissue noise due to imaging condition. These differences may be applied as adjustment or calibration factors to better represent clinical results.
Open Access
Making CT Dose Monitoring Meaningful: Augmenting Dose with Imaging Quality
(Tomography) Alsaihati, Njood; Ria, Francesco; Solomon, Justin; Ding, Aiping; Frush, Donald; Samei, Ehsan
Due to the concerns about radiation dose associated with medical imaging, radiation dose monitoring systems (RDMSs) are now utilized by many radiology providers to collect, process, analyze, and manage radiation dose-related information. Currently, most commercially available RDMSs focus only on radiation dose information and do not track any metrics related to image quality. However, to enable comprehensive patient-based imaging optimization, it is equally important to monitor image quality as well. This article describes how RDMS design can be extended beyond radiation dose to simultaneously monitor image quality. A newly designed interface was evaluated by different groups of radiology professionals (radiologists, technologists, and physicists) on a Likert scale. The results show that the new design is effective in assessing both image quality and safety in clinical practices, with an overall average score of 7.8 out of 10.0 and scores ranging from 5.5 to 10.0. Radiologists rated the interface highest at 8.4 out of 10.0, followed by technologists at 7.6 out of 10.0, and medical physicists at 7.5 out of 10.0. This work demonstrates how the assessment of the radiation dose can be performed in conjunction with the image quality using customizable user interfaces based on the clinical needs associated with different radiology professions.
Open Access
Scientific Abstracts and Sessions
(Medical Physics, 2020-06) Ria, Francesco; Smith, Taylor; Abadi, Ehsan; Solomon, Justin; Samei, ehsan
Purpose Image quality estimation in CT is crucial for technology assessment, procedure optimization, and overall radiological benefit evaluation, with noise magnitude playing a key role. Over the years, several methods have been proposed to estimate noise surrogates in vivo. The most accurate approach is to assess ensemble noise by scanning a patient multiple times and sampling each pixel noise within the ensemble of images, an ethically undoable repeated imaging process. Such impasse can be surmounted using Virtual Imaging Trials (VITs) that use computer-based simulations to simulate clinically realistic scenarios. The purpose of this study was to compare two different noise magnitude estimation methods with the ensemble noise measured in a VIT population. Methods This study included a set of 47 XCAT-phantom repeated chest exams acquired virtually using a scanner-specific simulator (DukeSim) modeling a commercial scanner geometry, reconstructed with FBP and IR algorithms. Noise magnitudes were calculated in soft tissues (GNI) and air surrounding the patient (AIRn), applying [-300,100]HU and HU<-900 thresholds, respectively. Furthermore, for each pixel in GNI threshold, the ensemble noise magnitudes in soft tissues (En) were calculated across images. Noise magnitude from different methods were compared in terms of percentage difference with correspondent En median values. Results For FBP reconstructed images, median En was 30.6 HU; median GNI was 40.1 HU (+31%) and median AIRn was 25.1 HU (-18%). For IR images, median En was 19.5 HU; median GNI was 25.1 HU (+29%) and median AIRn was 18.8 HU (-4%). Conclusion Compared to ensemble noise, GNI overestimates the tissue noise by about 30%, while AIRn underestimates it by 4 to 18%, depending on the reconstruction used. These differences may be applied as adjustment or calibration factors to the related noise estimation methods to most closely represent clinical results. However, air noise cannot be assumed to represent tissue noise.
Open Access
Technical Note: Validation of TG 233 phantom methodology to characterize noise and dose in patient CT data.
(Med Phys, 2020-02-10) Ria, Francesco; Solomon, Justin; Wilson, Joshua M; Samei, Ehsan
PURPOSE: Phantoms are useful tools in diagnostic CT, but practical limitations reduce phantoms to being only a limited patient surrogate. Furthermore, a phantom with a single cross sectional area cannot be used to evaluate scanner performance in modern CT scanners that use dose reduction techniques such as Automated Tube Current Modulation (ATCM) and Iterative Reconstruction (IR) algorithms to adapt x-ray flux to patient size, reduce radiation dose, and achieve uniform image noise. A new multi-sized phantom (Mercury Phantom, MP) has been introduced, representing multiple diameters. This work aimed to ascertain if measurements from MP can predict radiation dose and image noise in clinical CT images to prospectively inform protocol design. METHODS: The adult MP design included four different physical diameters (18.5, 23.0, 30.0, 37.0 cm) representing a range of patient sizes. The study included 1457 examinations performed on two scanner models from two vendors, and two clinical protocols (abdominopelvic with and chest without contrast). Attenuating diameter, radiation dose, and noise magnitude (average pixel standard deviation in uniform image) was automatically estimated in patients and in the MP using a previously validated algorithm. An exponential fit of CTDIvol and noise as a function of size was applied to patients and MP data. Lastly, the fit equations from the phantom data were used to fit the patient data. In each patient distribution fit, the normalized root mean square error (nRMSE) values were calculated in the residuals' plots as a metric to indicate how well the phantom data can predict dose and noise in clinical operations as a function of size. RESULTS: For dose across patient size distributions, the difference between nRMSE from patient fit and MP model data prediction ranged between 0.6% and 2.0% (mean 1.2%). For noise across patient size distributions, the nRMSE difference ranged between 0.1% and 4.7% (mean 1.4%). CONCLUSION: The Mercury Phantom provided a close prediction of radiation dose and image noise in clinical patient images. By assessing dose and image quality in a phantom with multiple sizes, protocol parameters can be designed and optimized per patient size in a highly constrained setup to predict clinical scanner and ATCM system performance.