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

Quantifying error introduced by iterative closest point image registration

OBJECTIVES

The aim of this paper was to quantify the analysis error introduced by iterative closest point (ICP) image registration. We also investigated whether a subsequent subtraction process can reduce process error.

METHODS

We tested metrology and two 3D inspection software using calibration standards at 0.39 μm, and 2.64 μm and mathematically perfect defects (softgauges) at 2 and 20 μm, on free form surfaces of increasing complexity and area, both with and without registration. Errors were calculated in percentage relative to the size of the defect being measured. Data were analysed in GraphPad Prism 9, normal and two-way ANOVA with post-hoc Tukey's was applied. Significance was inferred at p < 0.05.

RESULTS

Using ICP registration introduced errors from 0 % to 15.63 % of the defect size depending on the surface complexity and size of the defect. Significant differences were observed in analysis measurements between metrology and 3D inspection software and within different 3D inspection software, however, one did not show clear superiority over another. Even in the absence of registration, defects at 0.39 μm, and 2.64 μm produced substantial measurement error (13.39-77.50 % of defect size) when using 3D inspection software. Adding an additional data subtraction process reduced registration error to negligible levels (<1 % independent of surface complexity or area).

CONCLUSIONS

Commercial 3D inspection software introduces error during direct measurements below 3 μm. When using an ICP registration, errors over 15 % of the defect size can be introduced regardless of the accuracy of adjacent registration surfaces. Analysis output between software are not consistently repeatable or comparable and do not utilise ISO standards. Subtracting the datasets and analysing the residual difference reduced error to negligible levels.

CLINICAL SIGNIFICANCE

This paper quantifies the significant errors and inconsistencies introduced during the registration process even when 3D datasets are true and precise. This may impact on research diagnostics and clinical performance. An additional data processing step of scan subtraction can reduce this error but increases computational complexity.

Area-Detector Computed Tomography for Pulmonary Functional Imaging

An area-detector CT (ADCT) has a 320-detector row and can obtain isotropic volume data without helical scanning within an area of nearly 160 mm. The actual-perfusion CT data within this area can, thus, be obtained by means of continuous dynamic scanning for the qualitative or quantitative evaluation of regional perfusion within nodules, lymph nodes, or tumors. Moreover, this system can obtain CT data with not only helical but also step-and-shoot or wide-volume scanning for body CT imaging. ADCT also has the potential to use dual-energy CT and subtraction CT to enable contrast-enhanced visualization by means of not only iodine but also xenon or krypton for functional evaluations. Therefore, systems using ADCT may be able to function as a pulmonary functional imaging tool. This review is intended to help the reader understand, with study results published during the last a few decades, the basic or clinical evidence about (1) newly applied reconstruction methods for radiation dose reduction for functional ADCT, (2) morphology-based pulmonary functional imaging, (3) pulmonary perfusion evaluation, (4) ventilation assessment, and (5) biomechanical evaluation.

Pulmonary nodule enhancement in subtraction CT and dual-energy CT: A comparison study

OBJECTIVE

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 dual-energy 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.

Imaging of pulmonary perfusion using subtraction CT angiography is feasible in clinical practice

Abstract

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.