Estimating lung ventilation directly from 4D CT Hounsfield unit values

Kipritidis, John; Hofman, Michael S; Siva, Shankar; Callahan, Jason; Roux, Pierre‐Yves Le; Woodruff, Henry C.; Counter, William B.; Keall, Paul J.

doi:10.1118/1.4937599

Cited by 48 publications

(74 citation statements)

References 31 publications

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“…Two unpublished methods evaluated both HU and volume changes simultaneously to correct for tissue compression (“Hybrid‐A”) or to determine the mechanical stress distribution of the lung as a surrogate for function (“Hybrid‐B”). Also considered were “attenuation‐type” ventilation metrics that do not use DIR, but rather model blood‐gas exchange in terms of time‐averaged 4DCT HU values . Some ventilation metrics incorporate a tissue density scaling factor, ρ , which has been shown to improve the modelling of radioaerosol deposition .…”

Section: Methodsmentioning

confidence: 99%

“…Also considered were "attenuation-type" ventilation metrics that do not use DIR, but rather model blood-gas exchange in terms of time-averaged 4DCT HU values. 17 Some ventilation metrics incorporate a tissue density scaling factor, q, which has been shown to improve the modelling of radioaerosol deposition. 16 Another point of difference is that some ventilation metrics report the "specific" breathinginduced ventilation (i.e., fractional air volume change per voxel, as in the original Guerrero equation 5 ) whereas others report the "absolute" air volume change at each voxel (i.e., in units equal or proportional to mL/voxel, for example, as used in the modified Guerrero equation.…”

Section: B4 Characterization Of Ctvi Algorithmsmentioning

confidence: 99%

“…The "Attenuation" metric was developed in Ref. [17] and is based on the assumption that physiological ventilation (i.e., blood-gas exchange) should relate to the regional product of tissue and air densities. The CTVI is calculated directly from 4DCT HU values which are time averaged over the phase bins / = 1,. .…”

Section: Non Dir-based Ventilation Metricmentioning

confidence: 99%

“…Comparisons with Xenon CT in mechanically ventilated sheep, and ex vivo imaging of fluorescent microspheres in mice have featured highly controlled experimental conditions and achieved strong cross‐modality correlations (e.g., with voxel‐level correlations exceeding ∼0.8 for small lung subvolumes). In contrast, clinical human studies using single photon emission computed tomography (SPECT) with technetium‐99m (

^{99 m} Tc

), positron emission tomography (PET) with gallium‐68 (

^{68} Ga

),, and hyperpolarized gas MRI with either helium‐3 (

^{3} He

) or xenon‐129 (

^{129} Xe

) have all shown variable cross‐modality correlations (mean Spearman correlations in the range 0.1–0.8), which has been variously attributed to poor image quality in the 4DCT dataset or the RefVI scan, time delays between intrapatient scans, or poor reproducibility of breathing patterns/maneuvers. A recent study by Eslick et al .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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 rS, and Dice similarity coefficients evaluated for low function lung (DSClow) and high function lung (DSChigh). 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 rS 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 rS, 0.52 (0.36–0.67) for DSClow, and 0.45 (0.28–0.62) for DSChigh. All other algorithms exhibited at least one negative rS 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 ce...

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Section: Methodsmentioning

confidence: 99%

Section: B4 Characterization Of Ctvi Algorithmsmentioning

confidence: 99%

Section: Non Dir-based Ventilation Metricmentioning

confidence: 99%

^{99 m} Tc

), positron emission tomography (PET) with gallium‐68 (

^{68} Ga

),, and hyperpolarized gas MRI with either helium‐3 (

^{3} He

) or xenon‐129 (

^{129} Xe

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

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“…The CT-based methods do not always appear to give robust voxel level functional information, which should be one of the advantages of using CT over lower resolution images [78]. Different image registration algorithms and parameter settings can significantly alter CT ventilation values [83 85] and alternatives to image registration have been explored [79,86]. 4D-CT artefacts [83,87], CT noise [88], gravity [89,90], and breathing manoeuvre [91 93] may also have an impact on CTbased measures, and reproducibility is moderate [92,94].…”

Section: 3mentioning

confidence: 99%

Functional Image-guided Radiotherapy Planning for Normal Lung Avoidance

et al. 2016

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ReuseThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can't change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. 1 AbstractFor patients with lung cancer undergoing curative intent radiotherapy, functional lung imaging can be incorporated into treatment planning to modify the dose distribution within non-target volume lung by differentiation of lung regions that are functionally defective or viable. This concept of functional image-guided lung avoidance treatment planning has been investigated with several imaging modalities, primarily SPECT but also hyperpolarised gas MRI, PET and CT-based measures of lung biomechanics. Here, we review the application of each of these modalities, review practical issues of lung avoidance implementation, including image registration and the role of both ventilation and perfusion imaging, and provide guidelines for reporting of future lung avoidance planning studies.

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