No abstract

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

Subtraction computed tomography (SCT) is a technique that uses software-based motion correction between an unenhanced and an enhanced CT scan for obtaining the iodine distribution in the pulmonary parenchyma. This technique has been implemented in clinical practice for the evaluation of lung perfusion in CT pulmonary angiography (CTPA) in patients with suspicion of acute and chronic pulmonary embolism, with acceptable radiation dose. This paper discusses the technical principles, clinical interpretation, benefits and limitations of arterial subtraction CTPA. Key Points • SCT uses motion correction and image subtraction between an unenhanced and an enhanced CT scan to obtain iodine distribution in the pulmonary parenchyma. • SCT could have an added value in detection of pulmonary embolism. • SCT requires only software implementation, making it potentially more widely available for patient care than dual-energy CT.

No abstract

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.

To compare nodule enhancement on subtraction CT iodine maps to that on dual-energy CT iodine maps using CT datasets acquired simultaneously. Methods: A previously-acquired set of lung subtraction and dual-energy CT maps consisting of thirty patients with 95 solid pulmonary nodules (≥4 mm diameter) was used. Nodules were annotated and segmented on CT angiography, and mean nodule enhancement in the iodine maps calculated. Three radiologists scored nodule visibility with both techniques on a 4-point scale.Results: Mean nodule enhancement was higher (p < 0.001) at subtraction CT (34.9 ± 12.9 HU) than at dualenergy CT (25.4 ± 21.0 HU). Nodule enhancement at subtraction CT was judged more often to be "highly visible" for each observers (p < 0.001) with an area under the curve of 0.81. Conclusions: Subtraction CT is able to depict iodine enhancement in pulmonary nodules better than dual-energy CT.

No abstract

No abstract