When is contrast needed for abdominal and pelvic CT?

Radetic, Mark; Devita, Robert; Haaga, John R.

doi:10.3949/ccjm.87a.19093

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…Local expert opinion through hospital protocols often guides this decision, as well as consideration for the patient’s ability to safely tolerate contrast by mouth. 28 Downsides to utilization include that administration of oral contrast may delay scanning due to increased time for administration and bowel transit. 22 For evaluation of general acute nontraumatic abdominal pain, oral contrast appears to add little to radiological diagnostic performance.…”

Section: Common Imaging Modalities Utilized In the Emergency Departmentmentioning

confidence: 99%

Abdominal Pain in the Emergency Department: How to Select the Correct Imaging for Diagnosis

Wolfe¹,

Halsey-Nichols²,

Ritter³

et al. 2022

OAEM

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Abdominal pain is a common presenting complaint in the emergency department, and utilization of diagnostic imaging is often a key tool in determining its etiology. Plain radiography has limited utility in this population. Computed tomography (CT) is the imaging modality of choice for undifferentiated abdominal pain. Ultrasound and magnetic resonance imaging may be helpful in specific scenarios, primarily in pediatrics and pregnancy, and offer the benefit of eliminating ionizing radiation risk of CT. Guidance for imaging selection is determined by location of pain, special patient considerations, and specific suspected etiologies. Expert guidance is offered by the American College of Radiology Appropriateness Criteria ® which outlines imaging options based on location of pain.

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Section: Common Imaging Modalities Utilized In the Emergency Departmentmentioning

confidence: 99%

Abdominal Pain in the Emergency Department: How to Select the Correct Imaging for Diagnosis

Wolfe¹,

Halsey-Nichols²,

Ritter³

et al. 2022

OAEM

View full text Add to dashboard Cite

show abstract

“…In computed tomography (CT) scans, contrast enhancement is critical to the successful diagnosis of lesions, especially in the abdomen. [1][2][3] Lesions of particular types 1 can only be examined after intravenous injection of contrast agent. Intravenous contrast phase classification can improve the data curation of CT scans and contribute to the refinement of training data for subsequent tasks.…”

Section: Introductionmentioning

confidence: 99%

Utilizing domain knowledge to improve intravenous contrast phase classification of CT scans

Liu,

Mukherjee

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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Intravenous contrast phase classification can improve the data curation of CT scans for medical AI applications. In our early work, a five-phase classification model based on ResNet was developed to serve this purpose. It can accurately predict CT phases if the testing data follows the data distribution of the training data, while its performance drops substantially on the out-of-distribution data. To address this issue, this work aims to incorporate the inherent domain knowledge related to different CT phases for their classification. We explore the intensity distributions of a few key organs in different CT phases. TotalSegmentator was used to segment these organs including pulmonary artery, heart atrium, heart ventricle, heart myocardium, aorta, portal vein, splenic vein, liver, kidney, inferior vena cava, iliac artery, iliac veins, and urinary bladder. The intensity information was then extracted as image features to train a random forest classification model. The classification models were trained on an in-house dataset of 252 CT scans. They were validated on a testing dataset with 213 outof-distribution CT scans. The proposed method achieved better accuracy of 62.44%, while it was 53.02% using ResNet. These results showed that embedding domain knowledge could improve CT phase classification to be robust to the out-of-distribution dataset.

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“…A prevailing limitation of these phantoms has been their static representations of the anatomy.Some phantoms such as the XCAT 9 provide patient dynamics such as the cardiac and respiratory motions. There is also a need to include the dynamics associated with contrast media; as such media are widely used in CT imaging 10,11 aiming to discern anatomy and physiology processes. 12,13 Inter-organ contrast concentration as a function of time has also been previously simulated for the XCAT population 14 using single and multi-compartment models.…”

Section: Introductionmentioning

confidence: 99%

Anatomically and physiologically informed computational model of hepatic contrast perfusion for virtual imaging trials

et al. 2022

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Purpose Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise‐impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter‐organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra‐organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra‐organ concentration within the XCAT phantom with a specific focus on the liver. Methods Intra‐organ contrast perfusion depends on the organ's vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual‐input‐single‐output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood–contrast mixture was compared to clinical data. Results The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time–concentration curves. Measurements of simulated CT ROIs were found to agree with clinically observed values of early arterial phase contrast enhancement of the parenchyma (∼5$ \sim 5$ HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement false(180$(180$ HU). Conclusions The computational methods presented here furthered the development of the XCAT phantoms allowing for multi‐timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications.

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When is contrast needed for abdominal and pelvic CT?

Cited by 11 publications

References 9 publications

Abdominal Pain in the Emergency Department: How to Select the Correct Imaging for Diagnosis

Abdominal Pain in the Emergency Department: How to Select the Correct Imaging for Diagnosis

Utilizing domain knowledge to improve intravenous contrast phase classification of CT scans

Anatomically and physiologically informed computational model of hepatic contrast perfusion for virtual imaging trials

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