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

CT material decomposition with contrast agents: Single or multiple spectral photon-counting CT scans? A simulation study

PURPOSE

With the widespread introduction of dual energy computed tomography (DECT), applications utilizing the spectral information to perform material decomposition became available. Among these, a popular application is to decompose contrast-enhanced CT images into virtual non-contrast (VNC) or virtual non-iodine images and into iodine maps. In 2021, photon-counting CT (PCCT) was introduced, which is another spectral CT modality. It allows for scans with more than two different detected spectra. With these systems, it becomes possible to distinguish more than two materials. It is frequently proposed to administer more than one contrast agent, perform a single PCCT scan, and then calculate the VNC images and the contrast agent maps. This may not be optimal because the patient is injected with a material, only to have it computationally extracted again immediately afterwards by spectral CT. It may be better to do an unenhanced scan followed by one or more contrast-enhanced scans. The main argument for the spectral material decomposition is patient motion, which poses a significant challenge for approaches involving two or more temporally separated scans. In this work, we assume that we can correct for patient motion and thus are free to scan the patient more than once. Our goal is then to quantify the penalty for performing a single contrast-enhanced scan rather than a clever series of unenhanced and enhanced scans. In particular, we consider the impact on patient dose and image quality.

METHODS

We simulate CT scans of three differently sized phantoms containing various contrast agents. We do this for a variety of tube voltage settings, a variety of patient-specific prefilter (PSP) thicknesses and a variety of threshold settings of the photon-counting detector with up to four energy bins. The reconstructed bin images give the expectation values of soft tissue and of the contrast agents. Error propagation of projection noise into the images yields the image noise. Dose is quantified using the total CT dose index (CTDI) value of the scans. When combining multiple scans, we further consider all possible tube current (or dose) ratios between the scans. Material decomposition is done image-based in a statistical optimal way. Error propagation into the material-specific images yields the signal-to-noise ratio at unit dose (SNRD). The winning scan strategy is the one with the highest total SNRD, which is related to the SNRD of the material that has the lowest signal-to-noise ratio (SNR) among the materials to decompose into. We consider scan strategies with up to three scans and up to three materials (water W, contrast agent X and contrast agent Y).

RESULTS

In all cases, those scan strategies yield the best performance that combine differently enhanced scans, for example, W+WX, W+WXY, WX+WXY, W+WX+WY, with W denoting an unenhanced scan and WX, WY and WXY denoting X-, Y-, and X-Y-enhanced scans, respectively. The dose efficiency of scans with a single enhancement scheme, such as WX or WXY, is far lower. The dose penalty to pay for these single enhancement strategies is about two or greater. Our findings also apply to scans with a single energy bin and thus also to CT systems with conventional, energy-integrating detectors, that is, conventional DECT. Dual source CT (DSCT) scans are preferable over single source CT scans, also because one can use a PSP on the high Kilovolt spectrum to better separate the detected spectra. For the strategies and tasks considered here, it does not make sense to simultaneously scan with two different types of contrast agents. Iodine outperforms other high Z elements in nearly all cases.

CONCLUSIONS

Given the significant dose penalty when performing only one contrast-enhanced scan rather than a series of unenhanced and enhanced scans, one should consider avoiding the single-scan strategies. This requires to invest in the development of accurate registration algorithms that can compensate for patient and contrast agent motion between separate scans.

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.