Characterizing spatial differences between SPECT-ventilation and SPECT-perfusion in patients with lung cancer undergoing radiotherapy.

This study investigates agreement between ventilation and perfusion for lung cancer patients undergoing radiotherapy. Ventilation-perfusion scans of nineteen patients with stage III lung cancer from a prospective protocol were compared using voxel-wise Spearman correlation-coefficients. The presented results show in about 25% of patients, ventilation and perfusion exhibit lower agreement.

N-Phase Local Expansion Ratio for Characterizing Out-of-Phase Lung Ventilation.

Out-of-phase ventilation occurs when local regions of the lung reach their maximum or minimum volumes at breathing phases other than the global end inhalation or exhalation phases. This paper presents the N-phase local expansion ratio (LER N ) as a surrogate for lung ventilation. A common approach to estimate lung ventilation is to use image registration to align the end exhalation and inhalation 3DCT images and then analyze the resulting correspondence map. This 2-phase local expansion ratio (LER2) is limited because it ignores out-of-phase ventilation and thus may underestimate local lung ventilation. To overcome this limitation, LER N measures the maximum ratio of local expansion and contraction over the entire breathing cycle. Comparing LER2 to LER N provides a means for detecting and characterizing locations of the lung that experience out-of-phase ventilation. We present a novel in-phase/out-of-phase ventilation (IOV) function plot to visualize and measure the amount of high-function IOV that occurs during a breathing cycle. Treatment planning 4DCT scans collected during coached breathing from 32 human subjects with lung cancer were analyzed in this study. Results show that out-of-phase breathing occurred in all subjects and that the spatial distribution of out-of-phase ventilation varied from subject to subject. For the 32 subjects analyzed, 50% of the out-of-phase regions on average were mislabeled as low-function by LER2 (high-function threshold of 1.1, IOV threshold of 1.05). 4DCT and Xenon-enhanced CT of four sheep showed that LER8 is more accurate than LER2 for measuring lung ventilation.

Simulation of x-ray-induced acoustic imaging for absolute dosimetry: Accuracy of image reconstruction methods.

PURPOSE: Three-dimensional in-vivo dose verification is one of the standing challenges in radiation therapy. X-ray-induced acoustic tomography has recently been proposed as an imaging method for use in in-vivo dosimetry. The aim of this study was to investigate the accuracy of reconstructing three-dimensional (3D) absolute dose using x-ray-induced acoustic tomography. We performed this investigation using two different tomographic dose reconstruction techniques. METHODS: Two examples of 3D dose reconstruction techniques for x-ray acoustic imaging are investigated. Dose distributions are calculated for varying field sizes using a clinical treatment planning system. The induced acoustic pressure waves which are generated by the increase in temperature due to the absorption of pulsed MV x-rays are simulated using an advanced numerical modeling package for acoustic wave propagation in the time domain. Two imaging techniques, back projection and iterative time reversal, are used to reconstruct the 3D dose distribution in a water phantom with open fields. Image analysis is performed and reconstructed depth dose curves from x-ray acoustic imaging are compared to the depth dose curves calculated from the treatment planning system. Calculated field sizes from the reconstructed dose profiles by back projection and time reversal are compared to the planned field size to determine their accuracy. The iterative time reversal imaging technique is also used to reconstruct dose in an example clinical dose distribution. Image analysis of this clinical test case is performed using the gamma passing rate. In addition, gamma passing rates are used to validate the stopping criteria in the iterative time reversal method. RESULTS: Water phantom simulations showed that back projection does not adequately reconstruct the shape and intensity of the depth dose. When compared to the depth of maximum dose calculated by a treatment planning system, the maximum dose depth by back projection is shifted deeper by 55 and 75 mm for 4 × 4 cm and 10 × 10 cm field sizes, respectively. The reconstructed depth dose by iterative time reversal accurately agrees with the planned depth dose for a 4 × 4 cm field size and is shifted deeper by 12 mm for the 10 × 10 cm field size. When reconstructing field sizes, the back projection method leads to 18% and 35% larger sizes for the 4 × 4 cm and 10 × 10 cm fields, respectively, whereas the iterative time reversal method reconstructs both field sizes with < 2% error. For the clinical dose distribution, we were able to reconstruct the dose delivered by a 1 degree sub-arc with a good accuracy. The reconstructed and planned doses were compared using gamma analysis, with> 96% gamma passing rate at 3%/2 mm. CONCLUSIONS: Our results show that the 3D x-ray acoustic reconstructed dose by iterative time reversal is considerably more accurate than the dose reconstructed by back projection. Iterative time reversal imaging has a potential for use in 3D absolute dosimetry.

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images.

Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted toward normal fissure anatomy, yielding low sensitivity to weak, and abnormal fissures that are common in clinical data sets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when the global context is not considered. We propose a supervised discriminative learning framework for simultaneous feature extraction and classification. The proposed framework, called FissureNet, is a coarse-to-fine cascade of two convolutional neural networks. The coarse-to-fine strategy alleviates the challenges associated with training a network to segment a thin structure that represents a small fraction of the image voxels. FissureNet was evaluated on a cohort of 3706 subjects with inspiration and expiration 3DCT scans from the COPDGene clinical trial and a cohort of 20 subjects with 4DCT scans from a lung cancer clinical trial. On both data sets, FissureNet showed superior performance compared with a deep learning approach using the U-Net architecture and a Hessian-based fissure detection method in terms of area under the precision-recall curve (PR-AUC). The overall PR-AUC for FissureNet, U-Net, and Hessian on the COPDGene (lung cancer) data set was 0.980 (0.966), 0.963 (0.937), and 0.158 (0.182), respectively. On a subset of 30 COPDGene scans, FissureNet was compared with a recently proposed advanced fissure detection method called derivative of sticks (DoS) and showed superior performance with a PR-AUC of 0.991 compared with 0.668 for DoS.

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 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 centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

Quantifying ventilation change due to radiation therapy using 4DCT Jacobian calculations.

PURPOSE: Regional ventilation and its response to radiation dose can be estimated using four-dimensional computed tomography (4DCT) and image registration. This study investigated the impact of radiation therapy (RT) on ventilation and the dependence of radiation-induced ventilation change on pre-RT ventilation derived from 4DCT. METHODS AND MATERIALS: Three 4DCT scans were acquired from each of 12 subjects: two scans before RT and one scan 3 months after RT. The 4DCT datasets were used to generate the pre-RT and post-RT ventilation maps by registering the inhale phase image to the exhale phase image and computing the Jacobian determinant of the resulting transformation. The ventilation change between pre-RT and post-RT was calculated by taking a ratio of the post-RT Jacobian map to the pre-RT Jacobian map. The voxel-wise ventilation change between pre- and post-RT was investigated as a function of dose and pre-RT ventilation. RESULTS: Lung regions receiving over 20 Gy exhibited a significant decrease in function (3.3%, P < 0.01) compared to those receiving less than 20 Gy. When the voxels were stratified into high and low pre-RT function by thresholding the Jacobian map at 10% volume expansion (Jacobian = 1.1), high-function voxels exhibited 4.8% reduction in function for voxels receiving over 20 Gy, a significantly greater decline (P = 0.037) than the 2.4% reduction in function for low-function voxels. Ventilation decreased linearly with dose in both high-function and low-function regions. High-function regions showed a significantly larger decline in ventilation (P ≪ 0.001) as dose increased (1.4% ventilation reduction/10 Gy) compared to low-function regions (0.3% ventilation reduction/10 Gy). With further stratification of pre-RT ventilation, voxels exhibited increasing dose-dependent ventilation reduction with increasing pre-RT ventilation, with the largest pre-RT Jacobian bin (pre-RT Jacobian between 1.5 and 1.6) exhibiting a ventilation reduction of 4.8% per 10 Gy. CONCLUSIONS: Significant ventilation reductions were measured after radiation therapy treatments, and were dependent on the dose delivered to the tissue and the pre-RT ventilation of the tissue. For a fixed radiation dose, lung tissue with high pre-RT ventilation experienced larger decreases in post-RT ventilation than lung tissue with low pre-RT ventilation.