First-pass CT perfusion in small peripheral lung cancers: effect of the temporal interval between scan acquisitions on the radiation dose and quantitative vascular parameters

Quantitative Dual-Energy Computed Tomography Predicts Regional Perfusion Heterogeneity in a Model of Acute Lung Injury

Objective

The aims of this study were to investigate the ability of contrast-enhanced dual-energy computed tomography (DECT) for assessing regional perfusion in a model of acute lung injury, using dynamic first-pass perfusion CT (DynCT) as the criterion standard and to evaluate if changes in lung perfusion caused by prone ventilation are similarly demonstrated by DECT and DynCT.

Methods

This was an institutional review board–approved study, compliant with guidelines for humane care of laboratory animals. A ventilator-induced lung injury protocol was applied to 6 landrace pigs. Perfused blood volume (PBV) and pulmonary blood flow (PBF) were respectively quantified by DECT and DynCT, in supine and prone positions. The lungs were segmented in equally sized regions of interest, namely, dorsal, middle, and ventral. Perfused blood volume and PBF values were normalized by lung density. Regional air fraction (AF) was assessed by triple-material decomposition DECT. Per-animal correlation between PBV and PBF was assessed with Pearson R. Regional differences in PBV, PBF, and AF were evaluated with 1-way analysis of variance and post hoc linear trend analysis (α = 5%).

Results

Mean correlation coefficient between PBV and PBF was 0.70 (range, 0.55–0.98). Higher PBV and PBF values were observed in dorsal versus ventral regions. Dorsal-to-ventral linear trend slopes were −10.24 mL/100 g per zone for PBV (P < 0.001) and −223.0 mL/100 g per minute per zone for PBF (P < 0.001). Prone ventilation also revealed higher PBV and PBF in dorsal versus ventral regions. Dorsal-to-ventral linear trend slopes were −16.16 mL/100 g per zone for PBV (P < 0.001) and −108.2 mL/100 g per minute per zone for PBF (P < 0.001). By contrast, AF was lower in dorsal versus ventral regions in supine position, with dorsal-to-ventral linear trend slope of +5.77%/zone (P < 0.05). Prone ventilation was associated with homogenization of AF distribution among different regions (P = 0.74).

Conclusions

Dual-energy computed tomography PBV is correlated with DynCT-PBF in a model of acute lung injury, and able to demonstrate regional differences in pulmonary perfusion. Perfusion was higher in the dorsal regions, irrespectively to decubitus, with more homogeneous lung aeration in prone position.

Intra-observer and inter-observer agreements for the measurement of dual-input whole tumor computed tomography perfusion in patients with lung cancer: Influences of the size and inner-air density of tumors

Background

This study was conducted to assess intra‐observer and inter‐observer agreements for the measurement of dual‐input whole tumor computed tomography perfusion (DCTP) in patients with lung cancer.

Methods

A total of 88 patients who had undergone DCTP, which had proved a diagnosis of primary lung cancer, were divided into two groups: (i) nodules (diameter ≤3 cm) and masses (diameter >3 cm) by size, and (ii) tumors with and without air density. Pulmonary flow, bronchial flow, and pulmonary index were measured in each group. Intra‐observer and inter‐observer agreements for measurement were assessed using intraclass correlation coefficient, within‐subject coefficient of variation, and Bland–Altman analysis.

Results

In all lung cancers, the reproducibility coefficient for intra‐observer agreement (range 26.1–38.3%) was superior to inter‐observer agreement (range 38.1–81.2%). Further analysis revealed lower agreements for nodules compared to masses. Additionally, inner‐air density reduced both agreements for lung cancer.

Conclusion

The intra‐observer agreement for measuring lung cancer DCTP was satisfied, while the inter‐observer agreement was limited. The effects of tumoral size and inner‐air density to agreements, especially between two observers, should be emphasized. In future, an automatic computer‐aided segment of perfusion value of the tumor should be developed.

Multislice Analysis of Blood Flow Values in CT Perfusion Studies of Lung Cancer

Objectives. Tumour heterogeneity represents a key issue in CT perfusion (CTp), where all studies are usually based on global mean or median values of perfusion maps, often computed on whole tumour. We sought to determine whether, and to what extent, such global values can be representative of tumour heterogeneity, with respect to single slices, and could be used for therapy assessment. Materials and Methods. Twelve patients with one primary non-small cell lung cancer lesion were enrolled in this study, for a total amount of 26 CTp examinations and 118 slices. Mean and median blood flow (BF) values, calculated voxel-based, were computed on each slice and the whole tumour. To measure functional heterogeneity, entropy was calculated on BF values as well. Results. Most of the slices were not represented by the global BF values computed on the whole tumour. In addition, there are a number of lesions having equivalent global BF values, but they are composed of slices having very different heterogeneity distributions, that is, entropy values. Conclusions. Global mean/median BF values of the single slices separately should be considered for clinical assessment, only if interpreted through entropy computed on BF values. The numerical equivalence between global BF values of different lesions may correspond to different clinical status, thus inducing possible errors in choice of therapy when considering global values only.

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

PURPOSE

A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data.

METHODS

The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts' model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference.

RESULTS

The patients' AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s.

CONCLUSIONS

The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

Effect on perfusion values of sampling interval of computed tomographic perfusion acquisitions in neuroendocrine liver metastases and normal liver

OBJECTIVE

This study aimed to assess the effects of sampling interval (SI) of computed tomographic (CT) perfusion acquisitions on CT perfusion values in normal liver and liver metastases from neuroendocrine tumors.

METHODS

Computed tomographic perfusion in 16 patients with neuroendocrine liver metastases was analyzed using distributed-parameter modeling to yield tissue blood flow, blood volume, mean transit time, permeability, and hepatic arterial fraction for tumor and normal liver. Computed tomographic perfusion values for the reference SI of 0.5 s (SI0.5) were compared with those of SI data sets of 1 second, 2 seconds, 3 seconds, and 4 seconds using mixed-effects model analyses.

RESULTS

Increases in SI beyond 1 second were associated with significant and increasing departures of CT perfusion parameters from the reference values at SI0.5 (P ≤ 0.0009). Computed tomographic perfusion values deviated from the reference with increasing uncertainty with increasing SIs. Findings for normal liver were concordant.

CONCLUSIONS

Increasing SIs beyond 1 second yield significantly different CT perfusion parameter values compared with the reference values at SI0.5.

Effects of guided random sampling of TCCs on blood flow values in CT perfusion studies of lung tumors

RATIONALE AND OBJECTIVES

Tissue perfusion is commonly used to evaluate lung tumor lesions through dynamic contrast-enhanced computed tomography (DCE-CT). The aim of this study was to improve the reliability of the blood flow (BF) maps by means of a guided sampling of the tissue time-concentration curves (TCCs).

MATERIALS AND METHODS

Fourteen selected CT perfusion (CTp) examinations from different patients with lung lesions were considered, according to different degrees of motion compensation. For each examination, two regions of interest (ROIs) referring to the target lesion and the arterial input were manually segmented. To obtain the perfusion parameters, we computed the maximum slope of the Hill equation, describing the pharmacokinetics of the contrast agent, and the TCC was fitted for each voxel. A guided iterative approach based on the Random Sample Consensus method was used to detect and exclude samples arising from motion artifacts through the assessment of the confidence level of each single temporal sample of the TCC compared to the model. Removing these samples permits to refine the model fitting, thus exploiting more reliable data. Goodness-of-fit measures of the fitted TCCs to the original data (eg, root mean square error and correlation distance) were used to assess the reliability of the BF values, so as to preserve the functional structure of the resulting perfusion map. We devised a quantitative index, the local coefficient of variation (lCV), to measure the spatial coherence of perfusion maps, from local to regional and global resolution. The effectiveness of the algorithm was tested under three different degrees of motion yielded by as many alignment procedures.

RESULTS

At pixel level, the proposed approach improved the reliability of BF values, quantitatively assessed through the correlation index. At ROI level, a comparative analysis emphasized how our approach "replaced" the noisy pixels, providing smoother parametric maps while preserving the main functional structure. Moreover, the implemented algorithm provides a more meaningful effect in correspondence of a higher motion degree. This was confirmed both quantitatively, using the lCV, and qualitatively, through visual inspection by expert radiologists.

CONCLUSIONS

Perfusion maps achieved with the proposed approach can now be used as a valid tool supporting radiologists in DCE-CTp studies. This represents a step forward to clinical utilization of these studies for staging, prognosis, and monitoring values of therapeutic regimens.