Computed tomography lung iodine contrast mapping by image registration and subtraction

Goatman, Keith A; Plakas, Costas; Schuijf, Joanne D.; Beveridge, Erin; Prokop, Mathias

doi:10.1117/12.2043551

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…implement in clinical practice. It digitally subtracts a precontrast CT scan from a contrast material-enhanced CT scan after motion correction (18,19). This approach results in a contrast-to-noise ratio higher than that with dual-energy CT due to the use of the entire difference between iodineperfused and precontrast injection tissue attenuation, rather than the spectral decomposition of water (tissue) from iodine, as in dual-energy CT (20,21).…”

Section: Key Pointsmentioning

confidence: 99%

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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Section: Key Pointsmentioning

confidence: 99%

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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“…This study demonstrated that the newer Canon registration algorithm shows an, albeit small, improvement in performance compared to the current commercially available algorithm, especially in the paracardiac region. Algorithms A and B were tested in previous studies with different methods and smaller diaphragm motion, 15,16 with similar results. However, in those works, the authors evaluated the residual error in each direction individually instead of its absolute magnitude, and only reported the mean and standard deviation in estimating the movement of 100 landmarks rather than evaluating all voxels included in the lungs, as done in this work.…”

Section: Discussionmentioning

confidence: 93%

“…Registration between the simulated precontrast and contrast‐enhanced CT images was tested for three different algorithms. Algorithm A, CMRE NRR v1.0, is a commercially available registration‐ and motion‐correction algorithm used in clinical practice (SURE Subtraction Lung, version 6.0, Canon Medical Systems, Otawara, Japan) . This algorithm is mainly developed to image pulmonary perfusion by visualizing the iodine enhancement in the lungs, which can show perfusion defects caused by pulmonary embolism.…”

Section: Methodsmentioning

confidence: 99%

“…Algorithm A, CMRE NRR v1.0, is a commercially available registration-and motion-correction algorithm used in clinical practice (SURE Subtraction Lung, version 6.0, Canon Medical Systems, Otawara, Japan). 15 This algorithm is mainly developed to image pulmonary perfusion by visualizing the iodine enhancement in the lungs, which can show perfusion defects caused by pulmonary embolism. Algorithm B is CMRE NRR v2.0 of the same SURE Subtraction Lung algorithm, which is a prototype and hence not yet commercially available.…”

Section: E Image Registration Algorithmsmentioning

confidence: 99%

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

et al. 2019

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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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“…Lung segmentation is applied to ensure that only the region of the lung is included in the registration process. 5 The output deformation fields are then used to dictate the voxel movement of the airway trees among all phases.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Automated continuous quantitative measurement of proximal airways on dynamic ventilation CT: initial experience using an ex vivo porcine lung phantom

Yamashiro

Tsubakimoto

Nagatani

et al. 2015

COPD

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BackgroundThe purpose of this study was to evaluate the feasibility of continuous quantitative measurement of the proximal airways, using dynamic ventilation computed tomography (CT) and our research software.MethodsA porcine lung that was removed during meat processing was ventilated inside a chest phantom by a negative pressure cylinder (eight times per minute). This chest phantom with imitated respiratory movement was scanned by a 320-row area-detector CT scanner for approximately 9 seconds as dynamic ventilatory scanning. Obtained volume data were reconstructed every 0.35 seconds (total 8.4 seconds with 24 frames) as three-dimensional images and stored in our research software. The software automatically traced a designated airway point in all frames and measured the cross-sectional luminal area and wall area percent (WA%). The cross-sectional luminal area and WA% of the trachea and right main bronchus (RMB) were measured for this study. Two radiologists evaluated the traceability of all measurable airway points of the trachea and RMB using a three-point scale.ResultsIt was judged that the software satisfactorily traced airway points throughout the dynamic ventilation CT (mean score, 2.64 at the trachea and 2.84 at the RMB). From the maximum inspiratory frame to the maximum expiratory frame, the cross-sectional luminal area of the trachea decreased 17.7% and that of the RMB 29.0%, whereas the WA% of the trachea increased 6.6% and that of the RMB 11.1%.ConclusionIt is feasible to measure airway dimensions automatically at designated points on dynamic ventilation CT using research software. This technique can be applied to various airway and obstructive diseases.

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Computed tomography lung iodine contrast mapping by image registration and subtraction

Cited by 4 publications

References 12 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

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

Automated continuous quantitative measurement of proximal airways on dynamic ventilation CT: initial experience using an ex vivo porcine lung phantom

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