Dynamic ventilation imaging from four-dimensional computed tomography

Prospective Trial of Functional Lung Avoidance Radiation Therapy for Lung Cancer: Quality of Life Report

PURPOSE

A novel form of lung function imaging has been developed that uses 4-dimensional computed tomography (4DCT) data to generate lung ventilation images (4DCT-ventilation). Functional avoidance uses 4DCT-ventilation to reduce doses to functional lung with the aim of reducing pulmonary side effects. A phase 2, multicenter 4DCT-ventilation functional avoidance clinical trial was completed. The purpose of this work was to quantify changes in patient-reported outcomes (PROs) for patients treated with functional avoidance and determine which metrics are predictive of PRO changes.

MATERIALS AND METHODS

Patients with locally advanced lung cancer receiving curative-intent radiation therapy were accrued. Each patient had a 4DCT-ventilation image generated using 4DCT data and image processing. PRO instruments included the Functional Assessment of Cancer Therapy-Lung (FACT-L) questionnaire administered pretreatment; at the end of treatment; and at 3, 6, and 12 months posttreatment. Using the FACT-Trial Outcome Index and the FACT-Lung Cancer Subscale results, the percentage of clinically meaningful declines (CMDs) were determined. A linear mixed-effects model was used to determine which patient, clinical, dose, and dose-function metrics were predictive of PRO decline.

RESULTS

Of the 59 patients who completed baseline PRO surveys. 83% had non-small cell lung cancer, with 75% having stage 3 disease. The median dose was 60 Gy in 30 fractions. CMD FACT-Trial Outcome Index decline was 46.3%, 38.5%, and 26.8%, at 3, 6, and 12 months, respectively. CMD FACT-Lung Cancer Subscale decline was 33.3%, 33.3%, and 29.3%, at 3, 6, and 12 months, respectively. Although an increase in most dose and dose-function parameters was associated with a modest decline in PROs, none of the results were significant (all P > .053).

CONCLUSIONS

The current work presents an innovative combination of use of functional avoidance and PRO assessment and is the first report of PROs for patients treated with prospective 4DCT-ventilation functional avoidance. Approximately 30% of patients had clinically significant decline in PROs at 12 months posttreatment. The study provides additional data on outcomes with 4DCT-ventilation functional avoidance.

Advances in CT-based lung function imaging for thoracic radiotherapy

The objective of this review is to examine the potential benefits and challenges of CT-based lung function imaging in radiotherapy over recent decades. This includes reviewing background information, defining related concepts, classifying and reviewing existing studies, and proposing directions for further investigation. The lung function imaging techniques reviewed herein encompass CT-based methods, specifically utilizing phase-resolved four-dimensional CT (4D-CT) or end-inspiratory and end-expiratory CT scans, to delineate distinct functional regions within the lungs. These methods extract crucial functional parameters, including lung volume and ventilation distribution, pivotal for assessing and characterizing the functional capacity of the lungs. CT-based lung ventilation imaging offers numerous advantages, notably in the realm of thoracic radiotherapy. By utilizing routine CT scans, additional radiation exposure and financial burdens on patients can be avoided. This imaging technique also enables the identification of different functional areas of the lung, which is crucial for minimizing radiation exposure to healthy lung tissue and predicting and detecting lung injury during treatment. In conclusion, CT-based lung function imaging holds significant promise for improving the effectiveness and safety of thoracic radiotherapy. Nevertheless, challenges persist, necessitating further research to address limitations and optimize clinical utilization. Overall, this review highlights the importance of CT-based lung function imaging as a valuable tool in radiotherapy planning and lung injury monitoring.

Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography

BACKGROUND

Computed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL.

METHODS

From the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50-64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT).

RESULTS

The CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively.

CONCLUSION

We present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

Comparison of Free-Breathing 3D Phase-Resolved Functional Lung (PREFUL) MRI With Dynamic 19 F Ventilation MRI in Patients With Obstructive Lung Disease and Healthy Volunteers

BACKGROUND

Non-contrast-enhanced 1 H magnetic resonance imaging (MRI) with full lung coverage shows promise for assessment of regional lung ventilation but a comparison with direct ventilation measurement using 19 F MRI is lacking.

PURPOSE

To compare ventilation parameters calculated using 3D phase-resolved functional lung (PREFUL) MRI with 19 F MRI.

STUDY TYPE

Prospective.

POPULATION

Fifteen patients with asthma, 14 patients with chronic obstructive lung disease, and 13 healthy volunteers.

FIELD STRENGTH/SEQUENCE

A 3D gradient-echo pulse sequence with golden-angle increment and stack-of-stars encoding at 1.5 T.

ASSESSMENT

All participants underwent 3D PREFUL MRI and 19 F MRI. For 3D PREFUL, static regional ventilation (RVent) and dynamic flow-volume cross-correlation metric (FVL-CM) were calculated. For both parameters, ventilation defect percentage (VDP) values and ventilation defect (VD) maps (including a combination of both parameters [VDPCombined ]) were determined. For 19 F MRI, images from eight consecutive breaths under volume-controlled inhalation of perfluoropropane were acquired. Time-to-fill (TTF) and wash-in (WI) parameters were extracted. For all 19 F parameters, a VD map was generated and the corresponding VDP values were calculated.

STATISTICAL TESTS

For all parameters, the relationship between the two techniques was assessed using a Spearman correlation (r). Differences between VDP values were compared using Bland-Altman analysis. For regional comparison of VD maps, spatial overlap and Sørensen-Dice coefficients were computed.

RESULTS

3D PREFUL VDP values were significantly correlated to VDP measures by 19 F (r range: 0.59-0.70). For VDPRVent , no significant bias was observed with VDP of the third and fourth breath (bias range = -6.8:7.7%, P range = 0.25:0.30). For VDPFVL-CM , no significant bias was found with VDP values of fourth-eighth breaths (bias range = -2.0:12.5%, P range = 0.12:0.75). The overall spatial overlap of all VD maps increased with each breath, ranging from 61% to 81%, stabilizing at the fourth breath.

DATA CONCLUSION

3D PREFUL MRI parameters showed moderate to strong correlation with 19 F MRI. Depending on the 3D PREFUL VD map, the best regional agreement was found to 19 F VD maps of third-fifth breath.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Robust imaging approach for precise prediction of postoperative lung function in lung cancer patients prior to curative operation

BACKGROUND

To create a combined variable integrating both ventilation and perfusion as measured by preoperative dual-energy computed tomography (DECT), compare the results with predicted postoperative (PPO) lung function as estimated using conventional methods, and assess agreement with actual postoperative lung function.

METHODS

A total of 33 patients with lung cancer who underwent curative surgery after DECT and perfusion scan were selected. Ventilation and perfusion values were generated from DECT data. In the "combined variable method," these two variables and clinical variables were linearly regressed to estimate PPO lung function. Six PPO lung function parameters (segment counting, perfusion scan, volume analysis, ventilation map, perfusion map, and combined variable) were compared with actual postoperative lung function using an intraclass correlation coefficient (ICC).

RESULTS

The segment counting method produced the highest ICC for forced vital capacity (FVC) at 0.93 (p < 0.05), while the segment counting and perfusion map methods produced the highest ICC for forced expiratory volume in 1 second (FEV1 ; both 0.89, p < 0.05). The highest ICC value when using the combined variable method was for FEV1 /FVC (0.75, p < 0.05) and diffusing capacity of the lung for carbon monoxide (DLco; 0.80, p < 0.05) when using the perfusion map method. Overall, the perfusion map and ventilation map provided the best performance, followed by volume analysis, segment counting, perfusion scan, and the combined variable.

CONCLUSIONS

Use of DECT image processing to predict postoperative lung function produced better agreement with actual postoperative lung function than conventional methods. The combined variable method produced ICC values of 0.8 or greater for FVC and FEV1 .

Systematic Evaluation of the Impact of Lung Segmentation Methods on 4-Dimensional Computed Tomography Ventilation Imaging Using a Large Patient Database

PURPOSE

A novel form of lung functional imaging applied for functional avoidance radiation therapy has been developed that uses 4-dimensional computed tomography (4DCT) data and image processing techniques to calculate lung ventilation (4DCT-ventilation). Lung segmentation is a common step to define a region of interest for 4DCT-ventilation generation. The purpose of this study was to quantitatively evaluate the sensitivity of 4DCT-ventilation imaging using different lung segmentation methods.

METHODS AND MATERIALS

The 4DCT data of 350 patients from 2 institutions were used. Lung contours were generated using 3 methods: (1) reference segmentations that removed airways and pulmonary vasculature manually (Lung-Manual), (2) standard lung contours used for planning (Lung-RadOnc), and (3) artificial intelligence (AI)-based contours that removed the airways and pulmonary vasculature (Lung-AI). The AI model was based on a residual 3-dimensional U-Net and was trained using the Lung-Manual contours of 279 patients. We compared the Lung-RadOnc or Lung-AI with Lung-Manual contours for the entire 4DCT-ventilation functional avoidance process including lung segmentation (surface Dice similarity coefficient [Surface DSC]), 4DCT-ventilation generation (correlation), and subanalysis of 10 patients on a dosimetric endpoint (percentage of high functional volume of lung receiving ≥20 Gy [fV20{%}]).

RESULTS

Surface DSC comparing Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI contours was 0.40 ± 0.06 and 0.86 ± 0.04, respectively. The correlation between 4DCT-ventilation images generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI were 0.48 ± 0.14 and 0.85 ± 0.14, respectively. The difference in fV20[%] between 4DCT-ventilation generated with Lung-Manual/Lung-RadOnc and Lung-Manual/Lung-AI was 2.5% ± 4.1% and 0.3% ± 0.5%, respectively.

CONCLUSIONS

Our work showed that using standard planning lung contours can result in significantly variable 4DCT-ventilation images. The study demonstrated that AI-based segmentations generate lung contours and 4DCT-ventilation images that are similar to those generated using manual methods. The significance of the study is that it characterizes the lung segmentation sensitivity of the 4DCT-ventilation process and develops methods that can facilitate the integration of this novel imaging in busy clinics.

Evaluation of variables predicting PFT changes for lung cancer patients treated on a prospective 4DCT-ventilation functional avoidance clinical trial

PURPOSE

Functional avoidance radiotherapy uses functional imaging to reduce pulmonary toxicity by designing radiotherapy plans that reduce doses to functional regions of the lung. A phase-II, multi-center, prospective study of 4DCT-ventilation functional avoidance was completed. Pre and post-treatment pulmonary function tests (PFTs) were acquired and assessed pulmonary function change. This study aims to evaluate which clinical, dose and dose-function factors predict PFT changes for patients treated with 4DCT-ventilation functional avoidance radiotherapy.

MATERIALS AND METHODS

56 patients with locally advanced lung cancer receiving radiotherapy were accrued. PFTs were obtained at baseline and three months following radiotherapy and included forced expiratory volume in 1-second (FEV1), forced vital capacity (FVC), and FEV1/FVC. The ability of patient, clinical, dose (lung and heart), and dose-function metrics (metrics that combine dose and 4DCT-ventilation-based function) to predict PFT changes were evaluated using univariate and multivariate linear regression.

RESULTS

Univariate analysis showed that only dose-function metrics and the presence of chronic obstructive pulmonary disease (COPD) were significant (p<0.05) in predicting FEV1 decline. Multivariate analysis identified a combination of clinical (immunotherapy status, presence of thoracic comorbidities, smoking status, and age), along with lung dose, heart dose, and dose-function metrics in predicting FEV1 and FEV1/FVC changes.

CONCLUSION

The current work evaluated factors predicting PFT changes for patients treated in a prospective functional avoidance radiotherapy study. The data revealed that lung dose- function metrics could predict PFT changes, validating the significance of reducing the dose to the functional lung to mitigate the decline in pulmonary function and providing guidance for future clinical trials.

Four-Dimensional Computed Tomography Ventilation Image-Guided Lung Functional Avoidance Radiation Therapy: A Single-Arm Prospective Pilot Clinical Trial

PURPOSE

The primary objective of this prospective pilot trial was to assess the safety and feasibility of lung functional avoidance radiation therapy (RT) with 4-dimensional (4D) computed tomography (CT) ventilation imaging.

METHODS AND MATERIALS

Patients with primary lung cancer or metastatic disease to the lungs to receive conventionally fractionated RT (CFRT) or stereotactic body RT (SBRT) were eligible. Standard-of-care 4D-CT scans were used to generate ventilation images through image processing/analysis. Each patient required a standard intensity modulated RT plan and ventilation image guided functional avoidance plan. The primary endpoint was the safety of functional avoidance RT, defined as the rate of grade ≥3 adverse events (AEs) that occurred ≤12 months after treatment. Protocol treatment was considered safe if the rates of grade ≥3 pneumonitis and esophagitis were <13% and <21%, respectively for CFRT, and if the rate of any grade ≥3 AEs was <28% for SBRT. Feasibility of functional avoidance RT was assessed by comparison of dose metrics between the 2 plans using the Wilcoxon signed-rank test.

RESULTS

Between May 2015 and November 2019, 34 patients with non-small cell lung cancer were enrolled, and 33 patients were evaluable (n = 24 for CFRT; n = 9 for SBRT). Median follow-up was 14.7 months. For CFRT, the rates of grade ≥3 pneumonitis and esophagitis were 4.2% (95% confidence interval, 0.1%-21.1%) and 12.5% (2.7%-32.4%). For SBRT, no patients developed grade ≥3 AEs. Compared with the standard plans, the functional avoidance plans significantly (P < .01) reduced the lung dose-function metrics without compromising target coverage or adherence to standard organs at risk constraints.

CONCLUSIONS

This study, representing one of the first prospective investigations on lung functional avoidance RT, demonstrated that the 4D-CT ventilation image guided functional avoidance RT that significantly reduced dose to ventilated lung regions could be safely administered, adding to the growing body of evidence for its clinical utility.

Assessment of Regional Lung Ventilation with Positron Emission Tomography Using the Radiofluorinated Gas [18F]SF6: Application to an Animal Model of Impaired Ventilation

PURPOSE

Clinical ventilation studies are primarily performed with computerized tomography (CT) and more often with single-photon emission Computerized tomography (SPECT) using radiolabelled aerosols, both presenting certain limitations. Here, we investigate the use of the radiofluorinated gas [¹⁸F]SF₆ as a positron emission tomography (PET) ventilation marker in an animal model of impaired lung ventilation.

PROCEDURES

Sprague-Dawley rats (n = 15) were randomly assigned to spontaneous ventilation (sham group), endotracheal administration of phosphate-buffered saline (PBS group), or endotracheal administration of lipopolysaccharide (LPS group). PET-[¹⁸F]SF₆ images (10-min acquisition) were acquired at t = 48 h after LPS or PBS administration under mechanical ventilation. CT images were acquired after each PET session. Volumes of interest were manually delineated in the lungs on CT images, and voxel-by-voxel analysis was carried out on PET images to obtain the corresponding histograms. After the imaging sessions, lungs were harvested to conduct histological analysis.

RESULTS

Ventilation studies in sham animals showed uniform distribution of [¹⁸F]SF₆ and fast elimination of the radioactivity after discontinuation of the administration. For PBS- and LPS-treated rats, ventilation defects were observed on PET images in some animals, identified as regions with low presence of the radiolabelled gas. Hypoventilated areas co-localized with regions with higher x-ray attenuation than healthy lungs on the CT images, suggesting the presence of oedema and, in some cases, atelectasis. Histograms obtained from PET images showed quasi-Gaussian distributions for control animals, while PBS- and LPS-treated animals demonstrated the presence of hypoventilated voxels. Deviation of the histograms from Gaussian distribution correlated with histological score was obtained by ex vivo histological analysis.

CONCLUSIONS

[¹⁸F]SF₆ is an appropriate marker of regional lung ventilation and may find application in the early diagnose of acute lung disease.

Micro-CT-derived ventilation biomarkers for the longitudinal assessment of pathology and response to therapy in a mouse model of lung fibrosis

Experimental in-vivo animal models are key tools to investigate the pathogenesis of lung disease and to discover new therapeutics. Histopathological and biochemical investigations of explanted lung tissue are currently considered the gold standard, but they provide space-localized information and are not amenable to longitudinal studies in individual animals. Here, we present an imaging procedure that uses micro-CT to extract morpho-functional indicators of lung pathology in a murine model of lung fibrosis. We quantified the decrease of lung ventilation and measured the antifibrotic effect of Nintedanib. A robust structure-function relationship was revealed by cumulative data correlating micro-CT with histomorphometric endpoints. The results highlight the potential of in-vivo micro-CT biomarkers as novel tools to monitor the progression of inflammatory and fibrotic lung disease and to shed light on the mechanism of action of candidate drugs. Our platform is also expected to streamline translation from preclinical studies to human patients.

Characterizing Pulmonary Function Test Changes for Patients With Lung Cancer Treated on a 2-Institution, 4-Dimensional Computed Tomography-Ventilation Functional Avoidance Prospective Clinical Trial

Purpose

Four-dimensional computed tomography (4DCT)-ventilation-based functional avoidance uses 4DCT images to generate plans that avoid functional regions of the lung with the goal of reducing pulmonary toxic effects. A phase 2, multicenter, prospective study was completed to evaluate 4DCT-ventilation functional avoidance radiation therapy. The purpose of this study was to report the results for pretreatment to posttreatment pulmonary function test (PFT) changes for patients treated with functional avoidance radiation therapy.

Methods and Materials

Patients with locally advanced lung cancer receiving chemoradiation were accrued. Functional avoidance plans based on 4DCT-ventilation images were generated. PFTs were obtained at baseline and 3 months after chemoradiation. Differences for PFT metrics are reported, including diffusing capacity for carbon monoxide (DLCO), forced expiratory volume in 1 second (FEV1), and forced vital capacity (FVC). PFT metrics were compared for patients who did and did not experience grade 2 or higher pneumonitis.

Results

Fifty-six patients enrolled on the study had baseline and posttreatment PFTs evaluable for analysis. The mean change in DLCO, FEV1, and FVC was -11.6% ± 14.2%, -5.6% ± 16.9%, and -9.0% ± 20.1%, respectively. The mean change in DLCO was -15.4% ± 14.4% for patients with grade 2 or higher radiation pneumonitis and -10.8% ± 14.1% for patients with grade <2 radiation pneumonitis (P = .37). The mean change in FEV1 was -14.3% ± 22.1% for patients with grade 2 or higher radiation pneumonitis and -3.9% ± 15.4% for patients with grade <2 radiation pneumonitis (P = .09).

Conclusions

The current work is the first to quantitatively characterize PFT changes for patients with lung cancer treated on a prospective functional avoidance radiation therapy study. In comparison with patients treated with standard thoracic radiation planning, the data qualitatively show that functional avoidance resulted in less of a decline in DLCO and FEV1. The presented data can help elucidate the potential pulmonary function improvement with functional avoidance radiation therapy.

Is individual perfusion dose-response different than ventilation dose-response for lung cancer patients treated with radiotherapy?

OBJECTIVE

Current ventilation and perfusion dose-response studies focus on single-modalities (ventilation or perfusion) and perform pulmonary-toxicity assessment related to radiotherapy on a population-based basis. This study aims at quantitative and clinical evaluation of intrapatient differences between ventilation and perfusion dose-responses among lung cancer patients treated with radiotherapy.

METHODS

20 patients enrolled on a prospective functional avoidance protocol underwent single photon emission computed tomography-CT ventilation and perfusion scans pre- and post-radiotherapy. Relative changes in pre- to post-treatment ventilation and perfusion in lung regions receiving ≥20 Gy were calculated. In addition, the slopes of the linear fit to the relative ventilation and perfusion changes in regions receiving 0-60 Gy were calculated. A radiologist read and assigned a functional defect score to pre- and post-treatment ventilation/perfusion scans.

RESULTS

25% of patients had a difference >35% between ventilation and perfusion pre- to post-treatment changes and 20-30% of patients had opposite directions for ventilation and perfusion pre- to post-treatment changes. Using a semi-quantitative scale, radiologist assessment showed that 20% of patients had different pre- to post-treatment ventilation changes when compared to pre- to post-treatment perfusion changes.

CONCLUSION

Our data showed that ventilation dose-response can differ from perfusion dose-response for 20-30% of patients. Therefore, when performing thoracic dose-response in cancer patients, it is insufficient to look at ventilation or perfusion alone; but rather both modes of functional imaging may be needed when predicting for clinical outcomes.

ADVANCES IN KNOWLEDGE

The significance of this study can be highlighted by the differences between the intrapatient dose-response assessments of this analysis compared to existing population-based dose-response analyses. Elucidating intrapatient ventilation and perfusion dose-response differences may be valuable in predicting pulmonary toxicity in lung cancer patients post-radiotherapy.

Xenon-Enhanced Ventilation Computed Tomography for Functional Lung Avoidance Radiation Therapy in Patients With Lung Cancer

PURPOSE

This phase 2 trial aimed to determine whether xenon-enhanced ventilation computed tomography (XeCT)-guided functional-lung-avoidance radiation therapy could reduce the radiation pneumonitis (RP) rate in patients with lung cancer undergoing definitive chemoradiation therapy.

METHODS AND MATERIALS

Functional lung ventilation was measured via pulmonary function testing (PFT) and XeCT. A standard plan (SP) without reference to XeCT and a functional-lung-avoidance plan (fAP) optimized for lowering the radiation dose to the functional lung at the guidance of XeCT were designed. Dosimetric parameters and predicted RP risks modeled by biological evaluation were compared between the 2 plans in a treatment planning system (TPS). All patients received the approved fAP. The primary endpoint was the rate of grade ≥2 RP, and the secondary endpoints were the survival outcomes. The study hypothesis was that fAP could reduce the rate of grade ≥2 RP to 12% compared with a 30% historical rate.

RESULTS

Thirty-six patients were evaluated. Xenon-enhanced total functional lung volumes positively correlated with PFT ventilation parameters (forced vital capacity, P = .012; forced expiratory volume in 1 second, P = .035), whereas they were not correlated with the diffusion capacity parameter. We observed a 17% rate of grade ≥2 RP (6 of 36 patients), which was significantly different (P = .040) compared with the historical control. Compared with the SP, the fAP significantly spared the total ventilated lung, leading to a reduction in predicted grade ≥2 RP (P = .001) by TPS biological evaluation. The median follow-up was 15.2 months. The 1-year local control (LC), disseminated failure-free survival (DFFS), and overall survival (OS) rates were 88%, 66%, and 91%, respectively. The median LC and OS were not reached, and the median DFFS was 24.0 months (95% confidence interval, 15.7-32.3 months).

CONCLUSIONS

This report of XeCT-guided functional-lung-avoidance radiation therapy provided evidence showing its feasibility in clinical practice. Its benefit should be assessed in a broader multicenter trial setting.

Quantification of lung function on CT images based on pulmonary radiomic filtering

PURPOSE

To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT).

METHODS

The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial-encoded image information. Fifty-three radiomic features were extracted within the kernel, resulting in a fourth-order tensor object. As such, each voxel coordinate of the original lung was represented as a 53-dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA-SPECT) based on the Spearman correlation (ρ) analysis.

RESULTS

The radiomic feature maps GLRLM-based Run-Length Non-Uniformity and GLCOM-based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively.

CONCLUSIONS

The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis-generating data for future studies.

Assessment of a computed tomography-based radiomics approach for assessing lung function in lung cancer patients

PURPOSE

We aimed to assess radiomics approaches for estimating three pulmonary function test (PFT) results (forced expiratory volume in one second [FEV1], forced vital capacity [FVC], and the ratio of FEV1 to FVC [FEV₁/FVC]) using data extracted from chest computed tomography (CT) images.

METHODS

This retrospective study included 85 lung cancer patients (mean age, 75 years ±8; 69 men) who underwent stereotactic body radiotherapy between 2012 and 2020. Their pretreatment chest breath-hold CT and PFT data before radiotherapy were obtained. A total of 107 radiomics features (Shape: 14, Intensity: 18, Texture: 75) were extracted using two methods: extraction of the lung tissue (<-250 HU) (APPROACH 1), and extraction of small blood vessels and lung tissue (APPROACH 2). The PFT results were estimated using the least absolute shrinkage and selection operator regression. Pearson's correlation coefficients (r) were determined for all PFT results, and the area under the curve (AUC) was calculated for FEV₁/FVC (<70 %). Finally, we compared our approaches with the conventional formula (Conventional).

RESULTS

For the estimated FEV₁/FVC, the Pearson's r were 0.21 (P =.06), 0.69 (P <.01), and 0.73 (P <.01) for Conventional, APPROACH 1, and APPROACH 2, respectively; the AUCs for FEV₁/FVC (<70 %) were 0.67 (95 % confidence interval [CI]: 0.55, 0.79), 0.82 (CI: 0.72, 0.91; P =.047) and 0.86 (CI: 0.78, 0.94; P =.01), respectively.

CONCLUSIONS

The radiomics approach performed better than the conventional equation and may be useful for assessing lung function based on CT images.

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model

Purpose

The current algorithms for measuring ventilation images from 4D cone-beam computed tomography (CBCT) are affected by the accuracy of deformable image registration (DIR). This study proposes a new deep learning (DL) method that does not rely on DIR to derive ventilation images from 4D-CBCT (CBCT-VI), which was validated with the gold-standard single-photon emission-computed tomography ventilation image (SPECT-VI).

Materials and Methods

This study consists of 4D-CBCT and 99mTc-Technegas SPECT/CT scans of 28 esophagus or lung cancer patients. The scans were rigidly registered for each patient. Using these data, CBCT-VI was derived using a deep learning-based model. Two types of model input data are studied, namely, (a) 10 phases of 4D-CBCT and (b) two phases of peak-exhalation and peak-inhalation of 4D-CBCT. A sevenfold cross-validation was applied to train and evaluate the model. The DIR-dependent methods (density-change-based and Jacobian-based methods) were used to measure the CBCT-VIs for comparison. The correlation was calculated between each CBCT-VI and SPECT-VI using voxel-wise Spearman's correlation. The ventilation images were divided into high, medium, and low functional lung regions. The similarity of different functional lung regions between SPECT-VI and each CBCT-VI was evaluated using the dice similarity coefficient (DSC). One-factor ANONA model was used for statistical analysis of the averaged DSC for the different methods of generating ventilation images.

Results

The correlation values were 0.02 ± 0.10, 0.02 ± 0.09, and 0.65 ± 0.13/0.65 ± 0.15, and the averaged DSC values were 0.34 ± 0.04, 0.34 ± 0.03, and 0.59 ± 0.08/0.58 ± 0.09 for the density change, Jacobian, and deep learning methods, respectively. The strongest correlation and the highest similarity with SPECT-VI were observed for the deep learning method compared to the density change and Jacobian methods.

Conclusion

The results showed that the deep learning method improved the accuracy of correlation and similarity significantly, and the derived CBCT-VIs have the potential to monitor the lung function dynamic changes during radiotherapy.

Results of a Multi-Institutional Phase 2 Clinical Trial for 4DCT-Ventilation Functional Avoidance Thoracic Radiation Therapy

PURPOSE

Radiation pneumonitis remains a major limitation in the radiation therapy treatment of patients with lung cancer. Functional avoidance radiation therapy uses functional imaging to reduce pulmonary toxic effects by designing radiation therapy plans that reduce doses to functional regions of the lung. Lung functional imaging has been developed that uses 4-dimensional computed tomography (4DCT) imaging to calculate 4DCT-based lung ventilation (4DCT-ventilation). A phase 2 multicenter study was initiated to evaluate 4DCT-ventilation functional avoidance radiation therapy. The study hypothesis was that functional avoidance radiation therapy could reduce the rate of grade ≥2 radiation pneumonitis to 12% compared with a 25% historical rate, with the trial being positive if ≤16.4% of patients experienced grade ≥2 pneumonitis.

METHODS AND MATERIALS

Lung cancer patients receiving curative-intent radiation therapy (prescription doses of 45-75 Gy) and chemotherapy were accrued. Patient 4DCT scans were used to generate 4DCT-ventilation images. The 4DCT-ventilation images were used to generate functional avoidance plans that reduced doses to functional portions of the lung while delivering the prescribed tumor dose. Pneumonitis was evaluated by a clinician at 3, 6, and 12 months after radiation therapy.

RESULTS

Sixty-seven evaluable patients were accrued between April 2015 and December 2019. The median prescription dose was 60 Gy (range, 45-66 Gy) delivered in 30 fractions (range, 15-33 fractions). The average reduction in the functional volume of lung receiving ≥20 Gy with functional avoidance was 3.5% (range, 0%-12.8%). The median follow-up was 312 days. The rate of grade ≥2 radiation pneumonitis was 10 of 67 patients (14.9%; 95% upper CI, 24.0%), meeting the phase 2 criteria.

CONCLUSIONS

4DCT-ventilation offers an imaging modality that is convenient and provides functional imaging without an extra procedure necessary. This first report of a multicenter study of 4DCT-ventilation functional avoidance radiation therapy provided data showing that the trial met phase 2 criteria and that evaluation in a phase 3 study is warranted.

Predicting radiation pneumonitis with fuzzy clustering neural network using 4DCT ventilation image based dosimetric parameters

Background

To develop a fuzzy clustering neural network to predict radiation-induced pneumonitis (RP) using four-dimensional computed tomography (4DCT) ventilation image (VI) based dosimetric parameters for thoracic cancer patients.

Methods

The VI were retrospectively calculated from pre-treatment 4DCT data using a deformable image registration (DIR) and an improved VI algorithm. Similar to dose-volume histogram (DVH) of intensity modulated radiotherapy (IMRT), dose-function histogram (DFH) was derived from dose distribution and VI. Then, the dose-function metrics were calculated from DFH. For comparison, the dose-volume metrics were calculated from DVH. Correspondingly, two sets of feature vectors were formed from the dose-volume metrics and the dose-function metrics, respectively. For the feature vectors of each set, they were first pre-processed by principal component analysis (PCA) to reduce feature dimensions. Then, they were grouped to few clusters determined by fuzzy c-means (FCM) algorithm. Next, the neural network was trained to correlate the dosimetric parameters with RP based on the feature vectors of each cluster. Finally, the occurrence of RP was predicted by the neural network on the test data.

Results

Through PCA analysis, the top 5 principal components were selected. Their contribution is more than 98%, which is adequate to represent the original feature space of input data. Based on the clustering validity indexes, the optimal number of clusters is 4 and used for subsequent fuzzy clustering of the input data. After network training, the areas under the curve (AUC) of the prediction model is 0.77 using VI-based dosimetric parameters and 0.67 using structure-based dosimetric parameters.

Conclusions

Compared to the structure-based dosimetric features, the VI-based dosimetric features are more relevant to lung function and presented higher prediction accuracy of RP. The fuzzy clustering neural network improved the prediction accuracy of RP compared to the conventional neural network. The combination of VI-based dose-function metrics and fuzzy clustering neural network provides an effective predictive model for assessing lung toxicity risk after radiotherapy.

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning

Purpose.To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patients undergoing radiotherapy for lung cancer.Methods. A clinical data set of 58 pre- and post-radiotherapy^99mTc-labeled MAA-SPECT perfusion studies (32 patients) each with contemporaneous 4DCT studies was collected. Using the inhale and exhale phases of the 4DCT, a 3D-residual network was trained to create synthetic perfusion images utilizing the MAA-SPECT as ground truth. The training process was repeated for a 50-imaging study, five-fold validation with twenty model instances trained per fold. The highest performing model instance from each fold was selected for inference upon the eight-study test set. A manual lung segmentation was used to compute correlation metrics constrained to the voxels within the lungs. From the pre-treatment test cases (N = 5), 50th percentile contours of well-perfused lung were generated from both the clinical and synthetic perfusion images and the agreement was quantified.Results. Across the hold-out test set, our deep learning model predicted perfusion with a Spearman correlation coefficient of 0.70 (IQR: 0.61-0.76) and a Pearson correlation coefficient of 0.66 (IQR: 0.49-0.73). The agreement of the functional avoidance contour pairs was Dice of 0.803 (IQR: 0.750-0.810) and average surface distance of 5.92 mm (IQR: 5.68-7.55).Conclusion. We demonstrate that from 4DCT alone, a deep learning model can generate synthetic perfusion images with potential application in functional avoidance treatment planning.

Ventilation measurements using fast-helical free-breathing computed tomography

PURPOSE

To examine the use of multiple fast-helical free breathing computed tomography (FHFBCT) scans for ventilation measurement.

METHODS

Ten patients were scanned 25 times in alternating directions using a FHFBCT protocol. Simultaneously, an abdominal pneumatic bellows was used as a real-time breathing surrogate. Regions-of-interest (ROIs) were selected from the upper right lungs of each patient for analysis. The ROIs were first registered using a published registration technique (pTV). A subsequent follow-up registration employed an objective function with two terms, a ventilation-adjusted Hounsfield Unit difference and a conservation-of-mass term labeled ΔΓ that denoted the difference between the deformation Jacobian and the tissue density ratio. The ventilations were calculated voxel-by-voxel as the slope of a first-order fit of the Jacobian as a function of the breathing amplitude.

RESULTS

The ventilations of the 10 patients showed different patterns and magnitudes. The average ventilation calculated from the deformation vector fields (DVFs) of the pTV and secondary registration was nearly identical, but the standard deviation of the voxel-to-voxel differences was approximately 0.1. The mean of the 90^th percentile values of ΔΓ was reduced from 0.153 to 0.079 between the pTV and secondary registration, implying first that the secondary registration improved the conservation-of-mass criterion by almost 50% and that on average the correspondence between the Jacobian and density ratios as demonstrated by ΔΓ was less than 0.1. This improvement occurred in spite of the average of the 90th percentile changes in the DVF magnitudes being only 0.58 mm.

CONCLUSIONS

This work introduces the use of multiple free-breathing CT scans for free-breathing ventilation measurements. The approach has some benefits over the traditional use of 4-dimensional CT (4DCT) or breath-hold scans. The benefit over 4DCT is that FHFBCT does not have sorting artifacts. The benefits over breath-hold scans include the relatively small motion induced by quiet respiration versus deep-inspiration breath hold and the potential for characterizing dynamic breathing processes that disappear during breath hold.

Radiation-induced Hounsfield unit change correlates with dynamic CT perfusion better than 4DCT-based ventilation measures in a novel-swine model

To analyze radiation induced changes in Hounsfield units and determine their correlation with changes in perfusion and ventilation. Additionally, to compare the post-RT changes in human subjects to those measured in a swine model used to quantify perfusion changes and validate their use as a preclinical model. A cohort of 5 Wisconsin Miniature Swine (WMS) were studied. Additionally, 19 human subjects were recruited as part of an IRB approved clinical trial studying functional avoidance radiation therapy for lung cancer and were treated with SBRT. Imaging (a contrast enhanced dynamic perfusion CT in the swine and 4DCT in the humans) was performed prior to and post-RT. Jacobian elasticity maps were calculated on all 4DCT images. Contours were created from the isodose lines to discretize analysis into 10 Gy dose bins. B-spline deformable image registration allowed for voxel-by-voxel comparative analysis in these contours between timepoints. The WMS underwent a research course of 60 Gy in 5 fractions delivered locally to a target in the lung using an MRI-LINAC system. In the WMS subjects, the dose-bin contours were copied onto the contralateral lung, which received < 5 Gy for comparison. Changes in HU and changes in Jacobian were analyzed in these contours. Statistically significant (p < 0.05) changes in the mean HU value post-RT compared to pre-RT were observed in both the human and WMS groups at all timepoints analyzed. The HU increased linearly with dose for both groups. Strong linear correlation was observed between the changes seen in the swine and humans (Pearson coefficient > 0.97, p < 0.05) at all timepoints. Changes seen in the swine closely modeled the changes seen in the humans at 12 months post RT (slope = 0.95). Jacobian analysis showed between 30 and 60% of voxels were damaged post-RT. Perfusion analysis in the swine showed a statistically significant (p < 0.05) reduction in contrast inside the vasculature 3 months post-RT compared to pre-RT. The increases in contrast outside the vasculature was strongly correlated (Pearson Correlation 0.88) with the reduction in HU inside the vasculature but were not correlated with the changes in Jacobians. Radiation induces changes in pulmonary anatomy at 3 months post-RT, with a strong linear correlation with dose. The change in HU seen in the non-vessel lung parenchyma suggests this metric is a potential biomarker for change in perfusion. Finally, this work suggests that the WMS swine model is a promising pre-clinical model for analyzing radiation-induced changes in humans and poses several benefits over conventional swine models.

Early prediction of lung lesion progression in COVID-19 patients with extended CT ventilation imaging

PURPOSE

In the prediction of COVID-19 disease progression, a clear illustration and early determination of an area that will be affected by pneumonia remain great challenges. In this study, we aimed to predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness by using chest CT.

METHODS

COVID-19 patients who underwent three chest CT scans in the progressive phase were retrospectively enrolled. An extended CT ventilation imaging (CTVI) method was proposed in this work that was adapted to use two chest CT scans acquired on different days, and then lung ventilation maps were generated. The prediction maps were obtained according to the fractional ventilation values, which were related to pulmonary regional function and tissue property changes. The third CT scan was used to validate whether the prediction maps could be used to distinguish healthy regions and potential lesions.

RESULTS

A total of 30 patients (mean age ± SD, 43 ± 10 years, 19 females, and 2-12 days between the second and third CT scans) were included in this study. The predicted lesion locations and sizes were almost the same as the true ones visualized in third CT scan. Quantitatively, the predicted lesion volumes and true lesion volumes showed both a good Pearson correlation (R² = 0.80; P < 0.001) and good consistency in the Bland-Altman plot (mean bias = 0.04 cm³). Regarding the enlargements of the existing lesions, prediction results also exhibited a good Pearson correlation (R² = 0.76; P < 0.001) with true lesion enlargements.

CONCLUSION

The present findings demonstrated that the extended CTVI method could accurately predict and visualize the progression of lung lesions in COVID-19 patients in the early stage of illness, which is helpful for physicians to predetermine the severity of COVID-19 pneumonia and make effective treatment plans in advance.

Combined Assessment of Pulmonary Ventilation and Perfusion with Single-Energy Computed Tomography and Image Processing

RATIONALE AND OBJECTIVES

To establish a proof-of-principle for combined assessment of pulmonary ventilation and perfusion using single-energy computed tomography (CT) and image processing/analysis (denoted as single-energy CT ventilation/perfusion imaging).

MATERIALS AND METHODS

Breath-hold CT scans were acquired at end-expiration and end-inspiration before injection of iodinated contrast agents, and repeated at end-inspiration after contrast injection for 17 canines (8 normal and 9 diseased lung subjects). Ventilation images were calculated with deformable image registration to map the end-expiratory and end-inspiratory CT images and quantitative analysis for regional volume changes as surrogates for ventilation. Perfusion images were calculated by subtracting the end-inspiratory precontrast CT from the deformably registered end-inspiratory postcontrast CT, yielding a map of regional Hounsfield unit enhancement as a surrogate for perfusion. Ventilation-perfusion matching, spatial heterogeneity, and gravitationally directed gradients were compared between two groups using a Wilcoxon rank-sum test.

RESULTS

The normal group had significantly higher Dice similarity coefficients for spatial overlap of segmented functional volumes between ventilation and perfusion (median 0.40 vs. 0.33, p = 0.05), suggesting stronger ventilation-perfusion matching. The normal group also had greater Spearman's correlation coefficients based on 16 regions of interest (median 0.58 vs. 0.40, p = 0.09). The coefficients of variation were comparable (median, ventilation 0.71 vs. 0.91, p = 0.60; perfusion 0.63 vs. 0.75, p = 0.27). The linear regression slopes of gravitationally directed gradient were also comparable for ventilation (median, ventilation -0.26 vs. -0.18, p = 0.19; perfusion -0.17 vs. -0.06, p = 0.11).

CONCLUSION

These findings provide proof-of-principle for single-energy CT ventilation/perfusion imaging.

Quantifying pulmonary perfusion from noncontrast computed tomography

Purpose

Computed tomography (CT)‐derived ventilation methods compute respiratory induced volume changes as a surrogate for pulmonary ventilation. Currently, there are no known methods to derive perfusion information from noncontrast CT. We introduce a novel CT‐Perfusion (CT‐P) method for computing the magnitude mass changes apparent on dynamic noncontrast CT as a surrogate for pulmonary perfusion.

Methods

CT‐Perfusion is based on a mass conservation model which describes the unknown mass change as a linear combination of spatially corresponding inhale and exhale HU estimated voxel densities. CT‐P requires a deformable image registration (DIR) between the inhale/exhale lung CT pair, a preprocessing lung volume segmentation, and an estimate for the Jacobian of the DIR transformation. Given this information, the CT‐P image, which provides the magnitude mass change for each voxel within the lung volume, is formulated as the solution to a constrained linear least squares problem defined by a series of subregional mean magnitude mass change measurements. Similar to previous robust CT‐ventilation methods, the amount of uncertainty in a subregional sample mean measurement is related to measurement resolution and can be characterized with respect to a tolerance parameter τ. Spatial Spearman correlation between single photon emission CT perfusion (SPECT‐P) and the proposed CT‐P method was assessed in two patient cohorts via a parameter sweep of τ. The first cohort was comprised of 15 patients diagnosed with pulmonary embolism (PE) who had SPECT‐P and 4DCT imaging acquired within 24 h of PE diagnosis. The second cohort was comprised of 15 nonsmall cell lung cancer patients who had SPECT‐P and 4DCT images acquired prior to radiotherapy. For each test case, CT‐P images were computed for 30 different uncertainty parameter values, uniformly sampled from the range [0.01, 0.125], and the Spearman correlation between the SPECT‐P and the resulting CT‐P images were computed.

Results

The median correlations between CT‐P and SPECT‐P taken over all 30 test cases ranged between 0.49 and 0.57 across the parameter sweep. For the optimal tolerance τ = 0.0385, the CT‐P and SPECT‐P correlations across all 30 test cases ranged between 0.02 and 0.82. A one‐sample sign test was applied separately to the PE and lung cancer cohorts. A low Spearmen correlation of 15% was set as the null median value and two‐sided alternative was tested. The PE patients showed a median correlation of 0.57 (IQR = 0.305). One‐sample sign test was statistically significant with 96.5 % confidence interval: 0.20–0.63, P < 0.00001. Lung cancer patients had a median correlation of 0.57(IQR = 0.230). Again, a one‐sample sign test for median was statistically significant with 96.5 percent confidence interval: 0.45–0.71, P < 0.00001.

Conclusion

CT‐Perfusion is the first mechanistic model designed to quantify magnitude blood mass changes on noncontrast dynamic CT as a surrogate for pulmonary perfusion. While the reported correlations with SPECT‐P are promising, further investigation is required to determine the optimal CT acquisition protocol and numerical method implementation for CT‐P imaging.

A prospective study to validate pulmonary blood mass changes on non-contrast 4DCT in pulmonary embolism patients

PURPOSE

Limited diagnostic options exist for patients with suspected pulmonary embolism (PE) who cannot undergo CT-angiogram (CTA). CT-ventilation methods recover respiratory motion-induced lung volume changes as a surrogate for ventilation. We recently demonstrated that pulmonary blood mass change, induced by tidal respiratory motion, is a potential surrogate for pulmonary perfusion. In this study, we examine blood mass and volume change in patients with PE and parenchymal lung abnormalities (PLA).

METHODS

A cross-sectional analysis was conducted on a prospective, cohort-study with 129 consecutive PE suspected patients. Patients received 4DCT within 48 h of CTA and were classified as having PLA and/or PE. Global volume change (VC) and percent global pulmonary blood mass change (PBM) were calculated for each patient. Associations with disease type were evaluated using quantile regression.

RESULTS

68 of 129 patients were PE positive on CTA. Median change in PBM for PE-positive patients (0.056; 95% CI: 0.045, 0.068; IQR: 0.051) was smaller than that of PE-negative patients (0.077; 95% CI: 0.064, 0.089; IQR: 0.056), with an estimated difference of 0.021 (95% CI: 0.003, 0.038; p = 0.0190). PLA was detected in 57 (44.2%) patients. Median VC for PLA-positive patients (1.26; 95% CI: 1.22, 1.30; IQR: 0.15) showed no significant difference from PLA-negative VC (1.25; 95% CI: 1.21, 1.28; IQR: 0.15).

CONCLUSIONS

We demonstrate that pulmonary blood mass change is significantly lower in PE-positive patients compared to PE-negative patients, indicating that PBM derived from dynamic non-contrast CT is a potentially useful surrogate for pulmonary perfusion.

Technical Note: On the spatial correlation between robust CT-ventilation methods and SPECT ventilation

Purpose

The computed tomography (CT)‐derived ventilation imaging methodology employs deformable image registration (DIR) to recover respiratory motion‐induced volume changes from an inhale/exhale CT image pair, as a surrogate for ventilation. The Integrated Jacobian Formulation (IJF) and Mass Conserving Volume Change (MCVC) numerical methods for volume change estimation represent two classes of ventilation methods, namely transformation based and intensity (Hounsfield Unit) based, respectively. Both the IJF and MCVC methods utilize subregional volume change measurements that satisfy a specified uncertainty tolerance. In previous publications, the ventilation images resulting from this numerical strategy demonstrated robustness to DIR variations. However, the reduced measurement uncertainty comes at the expense of measurement resolution. The purpose of this study was to examine the spatial correlation between robust CT‐ventilation images and single photon emission CT‐ventilation (SPECT‐V).

Methods

Previously described implementations of IJF and MCVC require the solution of a large scale, constrained linear least squares problem defined by a series of robust subregional volume change measurements. We introduce a simpler parameterized implementation that reduces the number of unknowns while increasing the number of data points in the resulting least squares problem. A parameter sweep of the measurement uncertainty tolerance, τ, was conducted using the 4DCT and SPECT‐V images acquired for 15 non‐small cell lung cancer patients prior to radiotherapy. For each test case, MCVC and IJF CT‐ventilation images were created for 30 different uncertainty parameter values, uniformly sampled from the range 0.01,0.25. Voxel‐wise Spearman correlation between the SPECT‐V and the resulting CT‐ventilation images was computed.

Results

The median correlations between MCVC and SPECT‐V ranged from 0.20 to 0.48 across the parameter sweep, while the median correlations for IJF and SPECT‐V ranged between 0.79 and 0.82. For the optimal IJF tolerance τ=0.07, the IJF and SPECT‐V correlations across all 15 test cases ranged between 0.12 and 0.90. For the optimal MCVC tolerance τ=0.03, the MCVC and SPECT‐V correlations across all 15 test cases ranged between −0.06 and 0.84.

Conclusion

The reported correlations indicate that robust methods generate ventilation images that are spatially consistent with SPECT‐V, with the transformation‐based IJF method yielding higher correlations than those previously reported in the literature. For both methods, overall correlations were found to marginally vary for τ∈[0.03,0.15], indicating that the clinical utility of both methods is robust to both uncertainty tolerance and DIR solution.

Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom

Breathing motion is challenging for radiotherapy planning and delivery. This requires advanced four-dimensional (4D) imaging and motion mitigation strategies and associated validation tools with known deformations. Numerical phantoms such as the XCAT provide reproducible and realistic data for simulation-based validation. However, the XCAT generates partially inconsistent and non-invertible deformations where tumours remain rigid and structures can move through each other. We address these limitations by post-processing the XCAT deformation vector fields (DVF) to generate a breathing phantom with realistic motion and quantifiable deformation. An open-source post-processing framework was developed that corrects and inverts the XCAT-DVFs while preserving sliding motion between organs. Those post-processed DVFs are used to warp the first XCAT-generated image to consecutive time points providing a 4D phantom with a tumour that moves consistently with the anatomy, the ability to scale lung density as well as consistent and invertible DVFs. For a regularly breathing case, the inverse consistency of the DVFs was verified and the tumour motion was compared to the original XCAT. The generated phantom and DVFs were used to validate a motion-including dose reconstruction (MIDR) method using isocenter shifts to emulate rigid motion. Differences between the reconstructed doses with and without lung density scaling were evaluated. The post-processing framework produced DVFs with a maximum [Formula: see text]-percentile inverse-consistency error of 0.02 mm. The generated phantom preserved the dominant sliding motion between the chest wall and inner organs. The tumour of the original XCAT phantom preserved its trajectory while deforming consistently with the underlying tissue. The MIDR was compared to the ground truth dose reconstruction illustrating its limitations. MIDR with and without lung density scaling resulted in small dose differences up to 1 Gy (prescription 54 Gy). The proposed open-source post-processing framework overcomes important limitations of the original XCAT phantom and makes it applicable to a wider range of validation applications within radiotherapy.

Evaluation of four-dimensional cone beam computed tomography ventilation images acquired with two different linear accelerators at various gantry speeds using a deformable lung phantom

We evaluated four-dimensional cone beam computed tomography (4D-CBCT) ventilation images (VI_CBCT) acquired with two different linear accelerator systems at various gantry speeds using a deformable lung phantom. The 4D-CT and 4D-CBCT scans were performed using a computed tomography (CT) scanner, an X-ray volume imaging system (Elekta XVI) mounted in Versa HD, and an On-Board Imager (OBI) system mounted in TrueBeam. Intensity-based deformable image registration (DIR) was performed between peak-exhale and peak-inhale images. VI_CBCT- and 4D-CT-based ventilation images (VI_CT) were derived by DIR using two metrics: one based on the Jacobian determinant and one on changes in the Hounsfield unit (HU). Three different DIR regularization values (λ) were used for VI_CBCT. Correlations between the VI_CBCT and VI_CT values were evaluated using voxel-wise Spearman's rank correlation coefficient (r). In case of both metrics, the Jacobian-based VI_CBCT with a gantry speed of 0.6 deg/sec in Versa HD showed the highest correlation for all the gantry speeds (e.g., λ = 0.05 and r = 0.68). Thus, the r value of the Jacobian-based VI_CBCT was greater or equal to that of the HU-based VI_CBCT. In addition, the ventilation accuracy of VI_CBCT increased at low gantry speeds. Thus, the image quality of VI_CBCT was affected by the change in gantry speed in both the imaging systems. Additionally, DIR regularization considerably influenced VI_CBCT in both the imaging systems. Our results have the potential to assist in designing CBCT protocols, incorporating VI_CBCT imaging into the functional avoidance planning process.

Ventilation heterogeneity in smokers: role of unequal lung expansion and peripheral lung structure

Smoking-induced ventilation heterogeneity measured at the mouth via established washout indices [lung clearance index (LCI) and alveolar mixing efficiency (AME)] potentially results from unequal expansion, which can be quantified by computer tomography (CT), and structural changes down to the lung periphery, characterized by CT parametric response mapping indices [percentage of lung affected by functional small airway disease (PRM^fSAD) and emphysema (PRM^Emph)]. By combining CT imaging and nitrogen (N₂) washout tests in smokers, we specifically examined the roles of unequal lung expansion and peripheral structure. We first extracted three-dimensional maps of local lung expansion from registered inspiratory/expiratory CT images in 50 smokers (GOLD 0-IV) to compute for each smoker the theoretical N₂ washout concentration curve solely attributable to unequal local expansion. By a head-on comparison with washout N₂ concentrations measured at the mouth in the same smokers supine, we observed that 1) LCI increased from 4.8 ± 0.2 (SD) to 6.6 ± 0.8 (SD) due to unequal lung expansion alone and further increased to 9.0 ± 1.5 (SD) independent of local expansion and 2) AME decreased (from 100% by definition) to 95 ± 2 (SD)% due to unequal expansion alone and further decreased to 75 ± 7(SD)% independent of local expansion. In a multiple regression between the washout indices and CT-derived PRM^fSAD and PRM^Emph, LCI was related to PRM^fSAD (r = +0.58; P < 0.001), whereas AME was related to both PRM^fSAD (r_partial = -0.44; P = 0.002) and PRM^Emph (r_partial = -0.31; P = 0.033), in line with AME being dominated by alterations in peripheral structure. We conclude that smokers showing an increased LCI without corresponding AME decrease are predominantly affected by unequal lung expansion, whereas an AME decrease with a commensurate LCI increase indicates a smoking-induced alteration of peripheral structure.NEW & NOTEWORTHY A head-on comparison between imaging and multiple breath washout in supine smokers shows that computer tomography-measured unequal local lung expansion accounts for 50% or less of smoking-induced increase in ventilation heterogeneity. The contributions from unequal lung expansion and peripheral structure to the two main washout indices also explain their respective association with parametric response mapping indices.

The impact of 4DCT-ventilation imaging-guided proton therapy on stereotactic body radiotherapy for lung cancer

Functional lung avoidance during radiotherapy can help reduce pulmonary toxicity. This study assessed the potential impact of four-dimensional computed tomography (4DCT)-ventilation imaging-guided proton radiotherapy (PT) on stereotactic body radiotherapy (SBRT) by comparing it with three-dimensional conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT), which employ photon beams. Thirteen lung cancer patients who received SBRT with 3D-CRT were included in the study. 4DCT ventilation was calculated using the patients' 4DCT data, deformable image registration, and a density-change-based algorithm. Three functional treatment plans sparing the functional lung regions were developed for each patient using 3D-CRT, VMAT, and PT. The prescribed doses and dose constraints were based on the Radiation Therapy Oncology Group 0618 protocol. We evaluated the region of interest (ROI) and functional map-based dose-function metrics for 4DCT ventilation and the irradiated dose. Using 3D-CRT, VMAT, and PT, the percentages of the functional lung regions that received ≥ 5 Gy (fV5) were 26.0%, 21.9%, and 10.7%, respectively; the fV10 were 14.4%, 11.4%, and 9.0%, respectively; and fV20 were 6.5%, 6.4%, and 6.6%, respectively, and the functional mean lung doses (fMLD) were 5.6 Gy, 5.2 Gy, and 3.8 Gy, respectively. These results indicated that PT resulted in a significant reduction in fMLD, fV5, and fV10, but not fV20. The use of PT reduced the radiation to highly functional lung regions compared with those for 3D-CRT and VMAT while meeting all dose constraints.

Variations Between Dose-Ventilation and Dose-Perfusion Metrics in Radiation Therapy Planning for Lung Cancer

Purpose

Currently, several active clinical trials of functional lung avoidance radiation therapy using different imaging modalities for ventilation or perfusion are underway. Patients with lung cancer often show ventilation-perfusion mismatch, whereas the significance of dose-function metric remains unclear. The aim of the present study was to compare dose-ventilation metrics with dose-perfusion metrics for radiation therapy plan evaluation.

Methods and Materials

Pretreatment 4-dimensional computed tomography and ^99mTc-macroaggregated albumin single-photon emission computed tomography perfusion images of 60 patients with lung cancer treated with radiation therapy were analyzed. Ventilation images were created using the deformable image registration of 4-dimensional computed tomography image sets and image analysis for regional volume changes as a surrogate for ventilation. Ventilation and perfusion images were converted into percentile distribution images. Analyses included Pearson’s correlation coefficient and comparison of agreements between the following dose-ventilation and dose-perfusion metrics: functional mean lung dose and functional percent lung function receiving 5, 10, 20, 30, and 40 Gy (fV₅, fV₁₀, fV₂₀, fV₃₀, and fV₄₀, respectively).

Results

Overall, the dose-ventilation metrics were greater than the dose-perfusion metrics (ie, fV₂₀, 26.3% ± 9.9% vs 23.9% ± 9.8%). Correlations between the dose-ventilation and dose-perfusion metrics were strong (range, r = 0.94-0.97), whereas the agreements widely varied among patients, with differences as large as 6.6 Gy for functional mean lung dose and 11.1% for fV₂₀. Paired t test indicated that the dose-ventilation and dose-perfusion metrics were significantly different.

Conclusions

Strong correlations were present between the dose-ventilation and dose-perfusion metrics. However, the agreement between the dose-ventilation and dose-perfusion metrics widely varied among patients, suggesting that ventilation-based radiation therapy plan evaluation may not be comparable to that based on perfusion. Future studies should elucidate the correlation of dose-function metrics with clinical pulmonary toxicity metrics.

A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation

PURPOSE

The purpose of this study is to develop a deep learning (DL) method for producing four-dimensional computed tomography (4DCT) ventilation imaging and to evaluate the accuracy of the DL-based ventilation imaging against single-photon emission-computed tomography (SPECT) ventilation imaging (SPECT-VI). The performance of the DL-based method is assessed by comparing with density change- and Jacobian-based (HU and JAC) methods.

MATERIALS AND METHODS

Fifty patients with esophagus or lung cancer who underwent thoracic radiotherapy were enrolled in this study. For each patient, 4DCT scans paired with 99mTc-Technegas SPECT/CT were acquired before the first radiotherapy treatment. 4DCT and SPECT/CT were first rigidly registered using MIMvista and converted to data matrix using MATLAB, and then transferred to a DL model based on U-net for correlating 4DCT features and SPECT-VI. Two forms of 4DCT dataset [(a) ten phases and (b) two phases of peak-exhalation and peak-inhalation] as input are studied. Tenfold cross-validation procedure was used to evaluate the performance of the DL model. For comparative evaluation, HU and JAC methodologies are used to calculate specific ventilation imaging based on 4DCT (CTVI) for each patient. The voxel-wise Spearman's correlation was evaluated over the whole lung between each of CTVI and corresponding SPECT-VI. The SPECT-VI and produced CTVIs were segmented into high, median, and low functional lung (HFL, MFL, and LFL) regions. The spatial overlap of corresponding HFL, MFL, and LFL for each CTVI against SPECT-VI was also evaluated using the dice similarity coefficient (DSC). The averaged DSC of functional lung regions was calculated and statistically analyzed with a one-factor ANONA model among different methods.

RESULTS

The voxel-wise Spearman r_s values were (0.22 ± 0.31), (-0.09 ± 0.18), and (0.73 ± 0.16)/(0.71 ± 0.17) for the CTVI_HU , CTVI_JAC , and CTVI_DL(1) /CTVI_DL(2) . These results showed the DL method yielded the strongest correlation with SPECT-VI. Using the DSC as the spatial overlap metric, we found that the CTVI_HU , CTVI_JAC , and CTVI_DL(1) /CTVI_DL(2) methods achieved averaged DSC values for all patients to be (0.45 ± 0.08), (0.33 ± 0.04), and (0.73 ± 0.09)/(0.71 ± 0.09), respectively. The results demonstrated that the DL method yielded the highest similarity with SPECT-VI with the prominently significant difference (P < 10^-7 ).

CONCLUSIONS

This study developed a DL method for producing CTVI and performed a validation against SPECT-VI. The results demonstrated that DL method can derive CTVI with greatly improved accuracy in comparison to HU and JAC methods. The produced ventilation images can be more accurate and useful for lung functional avoidance radiotherapy and treatment response modeling.

4DCT Ventilation Map Construction Using Biomechanics-base Image Registration and Enhanced Air Segmentation

Current lung radiation therapy (RT) treatment planning algorithms used in most centers assume homogeneous lung function. However, co-existing pulmonary dysfunctions present in many non-small cell lung cancer (NSCLC) patients, particularly smokers, cause regional variations in both perfusion and ventilation, leading to inhomogeneous lung function. An adaptive RT treatment planning that deliberately avoids highly functional lung regions can potentially reduce pulmonary toxicity and morbidity. The ventilation component of lung function can be measured using a variety of techniques. Recently, 4DCT ventilation imaging has emerged as a cost-effective and accessible method. Current 4DCT ventilation calculation methods, including the intensity-based and Jacobian models, suffer from inaccurate estimations of air volume distribution and unreliability of intensity-based image registration algorithms. In this study, we propose a novel method that utilizes a biomechanical model-based registration along with an accurate air segmentation algorithm to calculate 4DCT ventilation maps. The results show a successful development of ventilation maps using the proposed method.

Comparison of regional lung perfusion response on longitudinal MAA SPECT/CT in lung cancer patients treated with and without functional tissue-avoidance radiation therapy

OBJECTIVE

The effect of functional lung avoidance planning on radiation dose-dependent changes in regional lung perfusion is unknown. We characterized dose-perfusion response on longitudinal perfusion single photon emission computed tomography (SPECT)/CT in two cohorts of lung cancer patients treated with and without functional lung avoidance techniques.

METHODS

The study included 28 primary lung cancer patients: 20 from interventional (NCT02773238) (FLARE-RT) and eight from observational (NCT01982123) (LUNG-RT) clinical trials. FLARE-RT treatment plans included perfused lung dose constraints while LUNG-RT plans adhered to clinical standards. Pre- and 3 month post-treatment macro-aggregated albumin (MAA) SPECT/CT scans were rigidly co-registered to planning four-dimensional CT scans. Tumour-subtracted lung dose was converted to EQD2 and sorted into 5 Gy bins. Mean dose and percent change between pre/post-RT MAA-SPECT uptake (%ΔPERF), normalized to total tumour-subtracted lung uptake, were calculated in each binned dose region. Perfusion frequency histograms of pre/post-RT MAA-SPECT were analyzed. Dose-response data were parameterized by sigmoid logistic functions to estimate maximum perfusion increase (%ΔPERFmaxincrease), maximum perfusion decrease (%ΔPERFmaxdecrease), dose midpoint (Dmid), and dose-response slope (k).

RESULTS

Differences in MAA perfusion frequency distribution shape between time points were observed in 11/20 (55%) FLARE-RT and 2/8 (25%) LUNG-RT patients (p < 0.05). FLARE-RT dose response was characterized by >10% perfusion increase in the 0-5 Gy dose bin for 8/20 patients (%ΔPERFmaxincrease = 10-40%), which was not observed in any LUNG-RT patients (p = 0.03). The dose midpoint Dmid at which relative perfusion declined by 50% trended higher in FLARE-RT compared to LUNG-RT cohorts (35 GyEQD2 vs 21 GyEQD2, p = 0.09), while the dose-response slope k was similar between FLARE-RT and LUNG-RT cohorts (3.1-3.2, p = 0.86).

CONCLUSION

Functional lung avoidance planning may promote increased post-treatment perfusion in low dose regions for select patients, though inter-patient variability remains high in unbalanced cohorts. These preliminary findings form testable hypotheses that warrant subsequent validation in larger cohorts within randomized or case-matched control investigations.

ADVANCES IN KNOWLEDGE

This novel preliminary study reports differences in dose-response relationships between patients receiving functional lung avoidance radiation therapy (FLARE-RT) and those receiving conventionally planned radiation therapy (LUNG-RT). Following further validation and testing of these effects in larger patient populations, individualized estimation of regional lung perfusion dose-response may help refine future risk-adaptive strategies to minimize lung function deficits and toxicity incidence.

Robust HU-based CT ventilation from an integrated mass conservation formulation

Computed tomography (CT) ventilation algorithms estimate volume changes induced by respiratory motion. Existing Hounsfield Unit (HU) methods approximate volume change from the measured HU variations between spatially corresponding voxel locations within a temporally resolved CT image pair, assuming that volume changes are caused solely by changes in air content. Numerical implementations require a deformable image registration to determine the inhale/exhale spatial correspondence, a preprocessing lung volume segmentation, a preprocessing high-intensity vessel segmentation, and a post-processing smoothing applied to the raw volume change estimates obtained for each lung tissue voxel.

PURPOSE

We introduce the novel mass-conserving volume change (MCVC) method for estimating voxel volume changes from the HU values within an inhale/exhale CT image pair. MCVC is based on subregional volume change estimates that possess quantitatively characterized and controllable levels of uncertainty. MCVC is therefore robust to small variations in DIR solutions and the resulting ventilation images are overall more reproducible. In contrast to existing HU methods, MCVC does not require a preprocessing lung vessel segmentation or pre/post-processing Gaussian smoothing.

METHODS

Subregional volume change estimates are defined in terms of mean density ratios. As such, the corresponding uncertainty is characterized using Gaussian statistics and standard error analysis of the sample density means. A numerical solution is obtained from the MCVC formulation by solving a constrained linear least squares problem defined by a series of subregional volume change estimates. Reproducibility of the MCVC method with respect to DIR solution was assessed using expert-determined landmark point pairs and inhale/exhale phases from 10 four-dimensional CTs (4DCTs) available on www.dir-lab.com. MCVC was also compared to the robust Integrated Jacobian Formulation (IJF), a transformation-based ventilation method.

RESULTS

The ten Dir-Lab 4DCT cases were registered twice with the same DIR algorithm, but using different degrees of freedom (DIR 1 and DIR 2). Standard HU ventilation (HUV) and MCVC ventilation images were computed for both solutions. The average spatial errors (300 landmarks per case) for DIR 1 ranged between 0.74 and 1.50 mm, whereas for DIR 2 they ranged between 0.68 and 1.18 mm. Despite the differences in spatial errors between the two DIR solutions, the average Pearson correlation between the corresponding HUV images was 0.94 (0.03), while for the MCVC images it was 1.00 (0.00). The average correlation between MCVC and IJF ventilation over the ten test cases was 0.81 (0.14), whereas for HUV and IJF it was 0.56 (1.11).

CONCLUSION

While HUV is robust to DIR solution, its implementation depends on heuristic Gaussian smoothing and vessel segmentation. MCVC is based on subregional volume change measurements with quantifiable and controllable levels of uncertainty. The subregional approach eliminates the need for Gaussian smoothing and lung vasculature segmentation. Numerical experiments are consistent with the underlying mathematical theory and indicate that MCVC ventilation is more reproducible with respect to DIR algorithm than standard HU methods. MCVC results are also more consistent with the robust IJF method, which suggests that incorporating robustness leads to more consistent results across both DIRs and ventilation algorithms.

MRI and CT lung biomarkers: Towards an in vivo understanding of lung biomechanics

BACKGROUND

The biomechanical properties of the lung are necessarily dependent on its structure and function, both of which are complex and change over time and space. This makes in vivo evaluation of lung biomechanics and a deep understanding of lung biomarkers, very challenging. In patients and animal models of lung disease, in vivo evaluations of lung structure and function are typically made at the mouth and include spirometry, multiple-breath gas washout tests and the forced oscillation technique. These techniques, and the biomarkers they provide, incorporate the properties of the whole organ system including the parenchyma, large and small airways, mouth, diaphragm and intercostal muscles. Unfortunately, these well-established measurements mask regional differences, limiting their ability to probe the lung's gross and micro-biomechanical properties which vary widely throughout the organ and its subcompartments. Pulmonary imaging has the advantage in providing regional, non-invasive measurements of healthy and diseased lung, in vivo. Here we summarize well-established and emerging lung imaging tools and biomarkers and how they may be used to generate lung biomechanical measurements.

METHODS

We review well-established and emerging lung anatomical, microstructural and functional imaging biomarkers generated using synchrotron x-ray tomographic-microscopy (SRXTM), micro-x-ray computed-tomography (micro-CT), clinical CT as well as magnetic resonance imaging (MRI).

FINDINGS

Pulmonary imaging provides measurements of lung structure, function and biomechanics with high spatial and temporal resolution. Imaging biomarkers that reflect the biomechanical properties of the lung are now being validated to provide a deeper understanding of the lung that cannot be achieved using measurements made at the mouth.

CT-based ventilation imaging in radiation oncology

A form of lung function imaging is emerging that uses phase-resolved four-dimensional CT (4DCT or breath-hold CT) images along with image processing techniques to generate lung function maps that provide a surrogate of lung ventilation. CT-based ventilation (referred to as CT-ventilation) research has gained momentum in Radiation Oncology because many lung cancer patients undergo four-dimensional CT simulation as part of the standard treatment planning process. Therefore, generating CT-ventilation images provides functional information without burdening the patient with an extra imaging procedure. CT-ventilation has progressed from an image processing calculation methodology, to validation efforts, to retrospective demonstration of clinical utility in Radiation Oncology. In particular, CT-ventilation has been proposed for two main clinical applications: functional avoidance radiation therapy and thoracic dose-response assessment. The idea of functional avoidance radiation therapy is to preferentially spare functional portions of the lung (as measured by CT-ventilation) during radiation therapy with the hypothesis that reducing dose to functional portions of the lung will lead to reduced rates of radiation-related thoracic toxicity. The idea of imaging-based dose-response assessment is to evaluate pre- to post-treatment CT-ventilation-based imaging changes. The hypothesis is that early, imaging-change-based response can be an early predictor of subsequent thoracic toxicity. Based on the retrospective evidence, the clinical applications of CT-ventilation have progressed from the retrospective setting to on-going prospective clinical trials. This review will cover basic CT-ventilation calculation methodologies, validation efforts, presentation of clinical applications, summarize on-going clinical trials, review potential uncertainties and shortcomings of CT-ventilation, and discuss future directions of CT-ventilation research.

Robust CT ventilation from the integral formulation of the Jacobian

Computed tomography (CT) derived ventilation algorithms estimate the apparent voxel volume changes within an inhale/exhale CT image pair. Transformation‐based methods compute these estimates solely from the spatial transformation acquired by applying a deformable image registration (DIR) algorithm to the image pair. However, approaches based on finite difference approximations of the transformation's Jacobian have been shown to be numerically unstable. As a result, transformation‐based CT ventilation is poorly reproducible with respect to both DIR algorithm and CT acquisition method.

Purpose

We introduce a novel Integrated Jacobian Formulation (IJF) method for estimating voxel volume changes under a DIR‐recovered spatial transformation. The method is based on computing volume estimates of DIR mapped subregions using the hit‐or‐miss sampling algorithm for integral approximation. The novel approach allows for regional volume change estimates that (a) respect the resolution of the digital grid and (b) are based on approximations with quantitatively characterized and controllable levels of uncertainty. As such, the IJF method is designed to be robust to variations in DIR solutions and thus overall more reproducible.

Methods

Numerically, Jacobian estimates are recovered by solving a simple constrained linear least squares problem that guarantees the recovered global volume change is equal to the global volume change obtained from the inhale and exhale lung segmentation masks. Reproducibility of the IJF method with respect to DIR solution was assessed using the expert‐determined landmark point pairs and inhale/exhale phases from 10 four‐dimensional computed tomographies (4DCTs) available on http://www.dir-lab.com. Reproducibility with respect to CT acquisition was assessed on the 4DCT and 4D cone beam CT (4DCBCT) images acquired for five lung cancer patients prior to radiotherapy.

Results

The ten Dir‐Lab 4DCT cases were registered twice with the same DIR algorithm, but with different smoothing parameter. Finite difference Jacobian (FDJ) and IFJ images were computed for both solutions. The average spatial errors (300 landmarks per case) for the two DIR solution methods were 0.98 (1.10) and 1.02 (1.11). The average Pearson correlation between the FDJ images computed from the two DIR solutions was 0.83 (0.03), while for the IJF images it was 1.00 (0.00). For intermodality assessment, the IJF and FDJ images were computed from the 4DCT and 4DCBCT of five patients. The average Pearson correlation of the spatially aligned FDJ images was 0.27 (0.11), while it was 0.77 (0.13) for the IFJ method.

Conclusion

The mathematical theory underpinning the IJF method allows for the generation of ventilation images that are (a) computed with respect to DIR spatial accuracy on the digital voxel grid and (b) based on DIR‐measured subregional volume change estimates acquired with quantifiable and controllable levels of uncertainty. Analyses of the experiments are consistent with the mathematical theory and indicate that IJF ventilation imaging has a higher reproducibility with respect to both DIR algorithm and CT acquisition method, in comparison to the standard finite difference approach.

Technical Note: Deriving ventilation imaging from 4DCT by deep convolutional neural network

PURPOSE

Ventilation images can be derived from four-dimensional computed tomography (4DCT) by analyzing the change in HU values and deformable vector fields between different respiration phases of computed tomography (CT). As deformable image registration (DIR) is involved, accuracy of 4DCT-derived ventilation image is sensitive to the choice of DIR algorithms. To overcome the uncertainty associated with DIR, we develop a method based on deep convolutional neural network (CNN) to derive ventilation images directly from the 4DCT without explicit image registration.

METHODS

A total of 82 sets of 4DCT and ventilation images from patients with lung cancer were used in this study. In the proposed CNN architecture, the CT two-channel input data consist of CT at the end of exhale and the end of inhale phases. The first convolutional layer has 32 different kernels of size 5 × 5 × 5, followed by another eight convolutional layers each of which is equipped with an activation layer (ReLU). The loss function is the mean-squared-error (MSE) to measure the intensity difference between the predicted and reference ventilation images.

RESULTS

The predicted images were comparable to the label images of the test data. The similarity index, correlation coefficient, and Gamma index passing rate averaged over the tenfold cross validation were 0.880 ± 0.035, 0.874 ± 0.024, and 0.806 ± 0.014, respectively.

CONCLUSIONS

The results demonstrate that deep CNN can generate ventilation imaging from 4DCT without explicit deformable image registration, reducing the associated uncertainty.

The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging

PURPOSE

CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities.

METHODS

The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient r S , and Dice similarity coefficients evaluated for low function lung ( DSC low ) and high function lung ( DSC high ).

RESULTS

A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The r S and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for r S , 0.52 (0.36-0.67) for DSC low , and 0.45 (0.28-0.62) for DSC high . All other algorithms exhibited at least one negative r S value, and/or one DSC value less than 0.5.

CONCLUSIONS

The VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

An initial investigation of hyperpolarized gas tagging magnetic resonance imaging in evaluating deformable image registration-based lung ventilation

BACKGROUND

Deformable image registration (DIR)-based lung ventilation mapping is attractive due to its simplicity, and also challenging due to its susceptibility to errors and uncertainties. In this study, we explored the use of 3D Hyperpolarized (HP) gas tagging MRI to evaluate DIR-based lung ventilation.

METHOD AND MATERIAL

Three healthy volunteers included in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton MRI (p-MRI) using balanced steady-state free precession pulse sequence at end of inhalation and end of exhalation. We first obtained the reference displacement vector fields (DVFs) from the t-MRIs by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR-based DVFs from the p-MRIs by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVFs and the DIR-based DVFs using the Jacobian method and then compared using cross correlation and mutual information.

RESULTS

The DIR-based lung ventilations calculated using p-MRI varied considerably from the reference lung ventilations based on t-MRI among all three subjects. The lung ventilations generated using Velocity AI were preferable for the better spatial homogeneity and accuracy compared to the ones using MIM, with higher average cross correlation (0.328 vs 0.262) and larger average mutual information (0.528 vs 0.323).

CONCLUSION

We demonstrated that different DIR algorithms resulted in different lung ventilation maps due to underlining differences in the DVFs. HP gas tagging MRI provides a unique platform for evaluating DIR-based lung ventilation.

Interim Analysis of a Two-Institution, Prospective Clinical Trial of 4DCT-Ventilation-based Functional Avoidance Radiation Therapy

PURPOSE

Functional imaging has been proposed that uses 4DCT images to calculate 4DCT-based lung ventilation (4DCT-ventilation). We have started a 2-institution, phase 2 prospective trial evaluating the feasibility, safety, and preliminary efficacy of 4DCT-ventilation functional avoidance. The trial hypothesis is that the rate of grade ≥2 radiation pneumonitis could be reduced to 12% with functional avoidance, compared with a 25% rate of pneumonitis with a historical control. The trial employed a Simon 2-stage design with a planned futility analysis after 17 evaluable patients. The purpose of this work is to present the trial design and implementation, dosimetric data, and clinical results for the planned futility analysis.

METHODS AND MATERIALS

Eligible patients were patients with lung cancer who were prescribed doses of 45 to 75 Gy. For each patient, the 4DCT data were used to generate a 4DCT-ventilation image using the Hounsfield unit technique along with a compressible flow-based image registration algorithm. Two intensity modulated radiation therapy treatment plans were generated: (1) a standard lung plan and (2) a functional avoidance treatment plan that aimed to reduce dose to functional lung while meeting target and normal tissue constraints. Patients were treated with the functional avoidance plan and evaluated for thoracic toxicity (presented as rate and 95% confidence intervals [CI]) with a 1-year follow-up.

RESULTS

The V20 to functional lung was 21.6% ± 9.5% (mean ± standard deviation) with functional avoidance, representing a decrease of 3.2% (P < .01) relative to standard, nonfunctional treatment plans. The rates of grade ≥2 and grade ≥3 radiation pneumonitis were 17.6% (95% CI, 3.8%-43.4%) and 5.9% (95% CI, 0.1%-28.7%), respectively.

CONCLUSIONS

Dosimetrically, functional avoidance achieved reduction in doses to functional lung while meeting target and organ at risk constraints. On the basis of Simon's 2-stage design and the 17.6% grade ≥2 pneumonitis rate, the trial met its futility criteria and has continued accrual.