Image Quality of Iodine Maps for Pulmonary Embolism: A Comparison of Subtraction CT and Dual-Energy CT

Grob, Dagmar; Smit, Ewoud J.; Oostveen, Luuk J.; Snoeren, Miranda; Prokop, Mathias; Schaefer-Prokop, Cornelia; Sechopoulos, Ioannis; Brink, Monique

doi:10.2214/ajr.18.20786

Cited by 9 publications

(14 citation statements)

References 18 publications

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“…A detailed description of the sample size calculation is included in Appendix E1 (online). A subset of the resulting images was used in our previous study, in which we compared the image quality of the subtraction CT and dual-energy CT iodine maps (22). This involved a subjective image quality comparison using the first 60 participants included in the present study and the objective comparison of iodine enhancement in 29 participants with acute PE.…”

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Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study

et al. 2019

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ulmonary embolism (PE) represents a prevalent acute cardiovascular condition that has considerable morbidity and mortality and requires prompt diagnosis and treatment (1). Since 2007, multidetector CT pulmonary angiography has been the standard technique used to detect PE (2), achieving sensitivity and specificity (3-5) higher than 90% with state-of-the-art equipment (6). A missed PE carries a high potential risk for a future venous thromboembolism. On the other hand, false-positive results and subsequent anticoagulation treatment can result in complications (7). The potential for overdiagnosis of PE is as harmful as underdiagnosis (8).Iodine maps depict abnormalities that correspond to loss of blood flow caused by an acute (or chronic) PE (9-12). Iodine maps improve sensitivity in the detection of emboli, especially small emboli at a subsegmental level or in more distal vessels (13,14) and support prognosis determination and monitoring of anticoagulation therapy effectiveness (15).The most common technique used to generate these maps is dual-energy CT (16,17). However, this requires dedicated hardware. On the other hand, subtraction CT requires motion correction software but no additional hardware, making it easier to adopt and less costly to

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Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study

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“…Via Dual-energy CT with associated iodine maps for perfusion defect detection, the expert radiologists easily picked up false negative AI reports. In future updates, the AI tool should implement those iodine maps as well, to decrease false negative results [6,[24][25][26]. Even though AI solution may potentially identify PE and thus assist radiologists, ultimately, we have to weigh the significance of such findings, which is for PE in most cases location-bound.…”

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confidence: 99%

“…McDonald et al, calculated in their study on the influence of technological advancements of crosssectional imaging on the radiology workflow, that a radiologist analyses an average of one image every three seconds [5]. This timeintensive encumbrance on the practicing radiologist, can accrue an increase in false negative results and misdiagnosis [6][7][8]. Real-time double reading by a peer is often done, which has been proved to aid in lowering the prevalence of misdiagnosis, however it is very labor-intensive.…”

Section: Introductionmentioning

confidence: 99%

Performance of an artificial intelligence tool with real-time clinical workflow integration – Detection of intracranial hemorrhage and pulmonary embolism

Buls¹,

Watté²,

Nieboer

et al. 2021

Physica Medica

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“…One option is to perform subtraction CT (Fig. ), which has been implemented in clinical practice to depict perfusion defects caused by pulmonary embolism . This technique generates an iodine map by subtracting a precontrast CT image from a contrast‐enhanced CT image.…”

Section: Introductionmentioning

confidence: 99%

“…1), which has been implemented in clinical practice to depict perfusion defects caused by pulmonary embolism. 5,6 This technique generates an iodine map by subtracting a precontrast CT image from a contrast-enhanced CT image. After removal of vessels, filtering, and adding an appropriate color scale, this iodine map reflects iodine distribution in the pulmonary parenchyma.…”

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Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study

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Purpose The purpose of this study was to assess, using an anthropomorphic digital phantom, the accuracy of algorithms in registering precontrast and contrast‐enhanced computed tomography (CT) chest images for generation of iodine maps of the pulmonary parenchyma via temporal subtraction. Materials and methods The XCAT phantom, with enhanced airway and pulmonary vessel structures, was used to simulate precontrast and contrast‐enhanced chest images at various inspiration levels and added CT simulation for realistic system noise. Differences in diaphragm position were varied between 0 and 20 mm, with the maximum chosen to exceed the 95th percentile found in a dataset of 100 clinical subtraction CTs. In addition, the influence of whole body movement, degree of iodine enhancement, beam hardening artifacts, presence of nodules and perfusion defects in the pulmonary parenchyma, and variation in noise on the registration were also investigated. Registration was performed using three lung registration algorithms — a commercial (algorithm A) and a prototype (algorithm B) version from Canon Medical Systems and an algorithm from the MEVIS Fraunhofer institute (algorithm C). For each algorithm, we calculated the voxel‐by‐voxel difference between the true deformation and the algorithm‐estimated deformation in the lungs. Results The median absolute residual error for all three algorithms was smaller than the voxel size (1.0 × 1.0 × 1.0 mm3) for up to an 8 mm diaphragm difference, which is the average difference in diaphragm levels found clinically, and increased with increasing difference in diaphragm position. At 20 mm diaphragm displacement, the median absolute residual error after registration was 0.85 mm (interquartile range, 0.51–1.47 mm) for algorithm A, 0.82 mm (0.50–1.40 mm) for algorithm B, and 0.91 mm (0.54–1.52 mm) for algorithm C. The largest errors were seen in the paracardiac regions and close to the diaphragm. The impact of all other evaluated conditions on the residual error varied, resulting in an increase in the median residual error lower than 0.1 mm for all algorithms, except in the case of whole body displacements for algorithm B, and with increased noise for algorithm C. Conclusion Motion correction software can compensate for respiratory and cardiac motion with a median residual error below 1 mm, which was smaller than the voxel size, with small differences among the tested registration algorithms for different conditions. Perfusion defects above 50 mm will be visible with the commercially available subtraction CT software, even in poorly registered areas, where the median residual error in that area was 7.7 mm.

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Image Quality of Iodine Maps for Pulmonary Embolism: A Comparison of Subtraction CT and Dual-Energy CT

Cited by 9 publications

References 18 publications

Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study

Iodine Maps from Subtraction CT or Dual-Energy CT to Detect Pulmonary Emboli with CT Angiography: A Multiple-Observer Study

Performance of an artificial intelligence tool with real-time clinical workflow integration – Detection of intracranial hemorrhage and pulmonary embolism

Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study

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