A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT

Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs

PURPOSE

Fast helical free-breathing CT (FHFBCT) scans are widely used for 5DCT and 5D Cone Beam imaging protocols. For quantitative analysis of lung physiology and function, it is important to segment the lung lobes in these scans. Since the 5DCT protocols use up to 25 FHFBCT scans, it is important that this segmentation task be automated. In this paper, we present a deep neural network (DNN) framework for segmenting the lung lobes in near real time.

METHODS

A total of 22 patient datasets (550 3D CT scans) were used for the study. Each of the lung lobes was manually segmented and considered ground-truth. A supervised and constrained generative adversarial network (CGAN) was employed for learning each set of lobe segmentations for each patient with 12 patients designated for training data. The resulting generator DNNs represented the lobe segmentations for each training dataset. A quorum-based algorithm was then implemented to test validation data consisting of 10 separate patient datasets (250 3D CTs). Each of the DNNs predicted their corresponding lobes for the validation data, and equal weights were given to the 12 generator CGANs. The quorum process worked by selecting the weighted average result of all 12 CGAN results for each lobe.

RESULTS

When evaluated against ground-truth segmentations, the quorum-based lobe segmentation was observed to have average structural similarity index, normalized cross-correlation coefficient, and dice coefficient values of 0.929, 0.806, and 0.814, respectively, compared to values of 0.911, 0.698, and 0.696, respectively, using a conventional strategy.

CONCLUSION

The proposed quorum-based approach computed segmentations with clinically acceptable accuracy in near real time using a multi-GPU-based computing setup. This method is scalable as more patient-specific CGANs can be added to the quorum over time.

Evaluation of four surface surrogates for modeling lung tumor positions over several fractions in radiotherapy

Patient breathing during lung cancer radiotherapy reduces the ability to keep a sharp dose gradient between tumor and normal tissues. To mitigate detrimental effects, accurate information about the tumor position is required. In this work, we evaluate the errors in modeled tumor positions over several fractions of a simple tumor motion model driven by a surface surrogate measure and its time derivative. The model is tested with respect to four different surface surrogates and a varying number of surrogate and image acquisitions used for model training. Fourteen patients were imaged 100 times with cine CT, at three sessions mimicking a planning session followed by two treatment fractions. Patient body contours were concurrently detected by a body surface laser scanning system BSLS from which four surface surrogates were extracted; thoracic point TP, abdominal point AP, the radial distance mean RDM, and a surface derived volume SDV. The motion model was trained on session 1 and evaluated on sessions 2 and 3 by comparing modeled tumor positions with measured positions from the cine images. The number of concurrent surrogate and image acquisitions used in the training set was varied, and its impact on the final result was evaluated. The use of AP as a surface surrogate yielded the smallest error in modeled tumor positions. The mean deviation between modeled and measured tumor positions was 1.9 mm. The corresponding deviations for using the other surrogates were 2.0 mm (RDM), 2.8 mm (SDV), and 3.0 mm (TP). To produce a motion model that accurately models the tumor position over several fractions requires at least 10 simultaneous surrogate and image acquisitions over 1–2 minutes.

A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy

Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.

Systematic feasibility analysis of performing elastography using reduced dose CT lung image pairs

PURPOSE

Elastography using computer tomography (CT) is a promising methodology that can provide patient-specific regional distributions of lung biomechanical properties. The purpose of this paper is to investigate the feasibility of performing elastography using simulated lower dose CT scans.

METHODS

A cohort of eight patient CT image pairs were acquired with a tube current-time product of 40 mAs for estimating baseline lung elastography results. Synthetic low mAs CT scans were generated from the baseline scans to simulate the additional noise that would be present in acquisitions at 30, 25, and 20 mAs, respectively. For the simulated low mAs scans, exhalation and inhalation datasets were registered using an in-house optical flow deformable image registration algorithm. The registered deformation vector fields (DVFs) were taken to be ground truth for the elastography process. A model-based elasticity estimation was performed for each of the reduced mAs datasets, in which the goal was to optimize the elasticity distribution that best represented their respective DVFs. The estimated elasticity and the DVF distributions of the reduced mAs scans were then compared with the baseline elasticity results for quantitative accuracy purposes.

RESULTS

The DVFs for the low mAs and baseline scans differed from each other by an average of 1.41 mm, which can be attributed to the noise added by the simulated reduction in mAs. However, the elastography results using the DVFs from the reduced mAs scans were similar from the baseline results, with an average elasticity difference of 0.65, 0.71, and 0.76 kPa, respectively. This illustrates that elastography can provide equivalent results using low-dose CT scans.

CONCLUSIONS

Elastography can be performed equivalently using CT image pairs acquired with as low as 20 mAs. This expands the potential applications of CT-based elastography.