Browsing by Author "Bosmans, Hilde"

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    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.
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    High sensitivity dedicated dual-breast PET/MR imaging: concept and preliminary simulations
    (Proceedings of SPIE, 2020-05-22) Tornai, Martin; Samanta, Suranjana; Majewski, Stanislaw; Williams, Mark; Turkington, Timothy; Register, Alan; Jiang, Jianyong; Dolinsky, Sergei; O'Sullivan, Joseph; Tai, Yuan-Chuan
    This paper presents a new high-sensitivity PET geometry for high fidelity MRI-compatible PET breast imaging which can scan both breasts simultaneously and have: high sensitivity and resolution; compatibility with MR-breast imaged volume; complete visualization of both breasts, mediastinum and axilla; and a modular design. Whereas contemporary dedicated x-ray and molecular breast imaging devices only scan one breast at a time, this approach relies on an unconventional PET geometry, and is able to provide a PET field of view (FOV) larger than that from dedicated breast MRI. The system geometry is evaluated with GATE Monte Carlo simulations of intrinsic system parameters. Various sized lesions (4-6mm) having [6:1 to 4:1] lesion:background radioactivity ratios mimicking different biological uptake are simulated, strategically located throughout a volumetric anthropomorphic torso. Dedicated breast PET (dbPET) imaging is compared with contemporary clinical PET. The dbPET system sensitivity is >6X greater than for contemporary whole-body PET. The novel, non-conventional system geometry allows for simultaneous dual-breast imaging, along with full medial and axillary imaging. Iteratively reconstructed full-volumetric images illustrate sharper visualization of 4mm lower uptake [4:1] lesions throughout the FOV compared with clinical PET. Image overlap between dedicated breast PET and MRI FOVs is excellent. Simulation results indicate clear superiority over conventional, high-sensitivity whole-body PET systems, as well as improved sensitivity over single-breast dbPET systems. This proposed system potentially facilitates both early detection and diagnosis, especially by increasing specificity of MRI, as well as visualizing tissue heterogeneity, monitoring therapeutic efficacy, and detecting breast cancer recurrence throughout the entire mediastinum.
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    Patient-informed modelling of hepatic contrast dynamics in contrast-enhanced CT imaging
    (Medical Imaging 2020: Physics of Medical Imaging, 2020-03-16) Setiawan, Hananiel; Ria, Francesco; Abadi, Ehsan; Fu, Wanyi; Smith, Taylor; Samei, Ehsan
    PURPOSE Iodinated contrast agents are commonly used in CT imaging to enhance tissue contrast. Consistency in contrast enhancement (CE) is critical in radiological diagnosis. Contrast material circulation in individual patients is affected by factors such as patient body habitus and anatomy leading to significant variability in organ contrast enhancement, image quality, and dose. Toward the goal of improving CE consistency in clinical populations, in this work we developed a contrast dynamics model to predict CT HU enhancement of liver parenchyma in abdominopelvic CE CT scans. METHOD AND MATERIALS This study included 700 adult abdominopelvic contrast CT exams performed in 2014-2018 using two scanner models from two vendors. Each CT image was segmented using a deep learning-based segmentation algorithm and the hepatic parenchyma HU values were acquired from the segmentations. A two-layer neural network-based algorithm was used to identify the relationship between patient attributes (height, weight, BMI, age, sex), scan parameters (slice thickness, scanner model), contrast injection protocols (bolus volume, injection-to-scan wait time), and the liver HU CE. We randomly selected 60% studies for training, 10% validation, and 30% for testing the accuracy. The training output was the extracted HU values. The goodness-of-fit of the model was evaluated in terms of R^2, Adjusted R^2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) between the model prediction and ground truth. In addition, the generalizability of the model was evaluated by comparing the R^2 in the training data (leave-one-out validation) and the testing data. RESULTS This preliminary model has an 0.51 R^2, 0.40 adjusted R^2, 10.0 HU MAE, 159.1 HU MSE, 0.6±12.8 HU Mean Error, and 2.5 HU Median Error on test data. For training data, the model has 0.59 R^2, 0.56 Adjusted R^2, and 0.5 predicted R^2. The close R^2 between testing and training data results indicate a reasonable generalizability. CONCLUSION Results showed considerable predictability of liver CE from patient attributes, scanning parameters, and contrast administration protocol. We envision to expand the model to include other major organs toward a comprehensive predictive model. CLINICAL RELEVANCE/APPLICATION A contrast dynamics model can be an essential tool to personalize contrast-enhanced CT protocol and to improve the consistency of contrast enhancement across different patients in diagnostics imaging.
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    Proceedings Virtual Imaging Trials in Medicine 2024.
    (ArXiv, 2024-05-08) Abadi, Ehsan; Badano, Aldo; Bakic, Predrag; Bliznakova, Kristina; Bosmans, Hilde; Carton, Ann-Katherine; Frangi, Alejandro; Glick, Stephen; Kinahan, Paul; Lo, Joseph; Maidment, Andrew; Ria, Francesco; Samei, Ehsan; Sechopoulos, Ioannis; Segars, Paul; Tanaka, Rie; Vancoillie, Liesbeth
    This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday.