G.G. Zhang

Intrathoracic tumour motion estimation from CT imaging using the 3D optical flow method

The purpose of this work was to develop and validate an automated method for intrathoracic tumour motion estimation from breath-hold computed tomography (BH CT) imaging using the three-dimensional optical flow method (3D OFM). A modified 3D OFM algorithm provided 3D displacement vectors for each voxel which were used to map tumour voxels on expiration BH CT onto inspiration BH CT images. A thoracic phantom and simulated expiration/inspiration BH CT pairs were used for validation. The 3D OFM was applied to the measured inspiration and expiration BH CT images from one lung cancer and one oesophageal cancer patient. The resulting displacements were plotted in histogram format and analysed to provide insight regarding the tumour motion. The phantom tumour displacement was measured as 1.20 and 2.40 cm with full-width at tenth maximum (FWTM) for the distribution of displacement estimates of 0.008 and 0.006 cm, respectively. The maximum error of any single voxels motion estimate was 1.1 mm along the z-dimension or approximately one-third of the z-dimension voxel size. The simulated BH CT pairs revealed an rms error of less than 0.25 mm. The displacement of the oesophageal tumours was nonuniform and up to 1.4 cm, this was a new finding. A lung tumour maximum displacement of 2.4 cm was found in the case evaluated. In conclusion, 3D OFM provided an accurate estimation of intrathoracic tumour motion, with estimated errors less than the voxel dimension in a simulated motion phantom study. Surprisingly, oesophageal tumour motion was large and nonuniform, with greatest motion occurring at the gastro-oesophageal junction.

Evaluating Effects of Radiation Therapy Treatment on 4DCT-Calculated Lung Ventilation.

Deglutition-Induced Real-Time Directional Displacements in Head-and-Neck Cancer Patients — Dynamic Volume Shuttle Imaging Analysis V. Shankar , C. Haritha , V.B. Patel , J.D. Prajapathi , P.K.Mathews , G.J. Sunith , P. Shinde , L.N. Chaudhari , M. Meshram , J. Joseph , et al., M.S.Patel Cancer Center, Karamsad, Gujarat, India, Dept of Radiodiagnosis, Shree Krishna Hospital, Karamsad, Gujarat, India, GE Healthcare, Mumbai, India

SU-D-110-02: Evaluation of the Differences between Locoregional Lung Ventilation Estimation Methods Using a Single Deformable Image Registration Algorithm

Purpose: Three methods of calculating ventilation from 4D CTimage sets have been explored by several research groups. This study is to investigate the differences of these three local ventilation calculations. Methods: Optical flow (OF) deformable image registration of the normal end expiration and inspiration phases of 4D‐CT images was used to correlate the voxels between the two phases. The OF was validated using a 4D pixel‐ based and point‐validated breathing thorax model, consisting of a 4D‐CT image data set along with associated landmarks. Ventilation derived from 4D‐CTs from 20 esophageal patients were retrospectively analyzed. Differences between the ventilation images generated by three methods, the Jacobian, the DeltaV, and the HU, were examined on a voxel‐to‐voxel basis. The Jacobian method uses the first derivative of the deformation field to approximate the change in volume of voxels. The DeltaV method directly calculates the volume change. The HU method uses the change in Houndsfield Units (HUs) of corresponding voxels to calculate ventilation. Results: The target registration error (TRE) for the deformable image registration was an average of 1.6±0.68 mm and maximum of 3.1 mm. Average difference between the DeltaV and the Jacobian ventilation as a percentage of the maximum ventilation value was 0.51±0.3% (range 0.33% to 1.32%). Average difference between the DeltaV and HU ventilation was 2.4±4.5 % (range 0.4% to 19.2 %). A small number of voxels show significant differences. We speculate that the larger differences were due to some image registration variances. Regions of highest and lowest ventilation matched well for all methods. Conclusions: Highs and lows in ventilation were more pronounced in the DeltaV method compared to the Jacobian. In general the differences between the two ventilation methods were small. However, the differences between the DeltaV and the HU methods were considerably larger.

A Method to Determine the Optimal Number of Bins in 4D PET

of axillary and extra-axillary metastases identified by FDG PET/CT in patients scheduled for neoadjuvant chemotherapy, and how often this information could change post-operative radiation planning. Materials/Methods: We performed a retrospective analysis of 38 patients with breast cancer scheduled for neoadjuvant chemotherapy between January 2011 and July 2012. 10 patients were clinical stage II, 26 clinical stage III, 2 clinical stage IV. All patients had a FDG PET/CT within 1 month of diagnosis. 28/32 patients had pathologic confirmation of ipsilateral axillary lymph nodes. We identified the incidence of positive axillary lymph nodes and extra axillary metastases, correlated this with stage, and identified how often this could change radiation planning. Results: Axillary lymph nodes were positive in 32/38 patients (84.2%); 5/ 10 (50%) stage II, 25/26 (96.2%) stage III, 2/2 (100%) stage IV. 28/32 (87.5%) of patients with PET positive axillary lymph nodes had pathologic confirmation. 16/38 patients had extra-axillary metastases. These were identified in 14/26 stage III patients (53.8%) and 2/2 stage IV patients (100%). Sites of extra-axillary PET positive metastases were: subpectoral 11/38 (28.9%), internal mammary chain (IMC) 6/38 (15.8%), supraclavicular 2/38 (5.3%), subclavian 1/38 (2.6%), mediastinal lymph node 1/38 (2.6%), and pulmonary nodule 1/38 (2.6%). One patient with positive IMC nodes did not have positive axillary nodes. In all other cases (15/16) patients with extra axillary metastases had axillary metastases. Metastases to subpectoral, IMC, supraclavicular, and subclavian lymph nodes could potentially require modification of post-operative radiation therapy fields. (Total 20/38, 52.6%) Conclusions: FDG PET/CT detected positive axillary lymph nodes in 84.2% of breast cancer patients scheduled for neoadjuvant chemotherapy; in 50% of Stage II patients, 96.2% of stage III patients and 100% of Stage IV patients. Extra-axillary metastases were identified in 42.1% of patients, 53.8% of stage III patients and 100 % of stage IV patients. In 52.6 % of patients, non-axillary regional metastases were identified that could potentially change radiation treatment plans. In clinical stage III and limited stage IV disease, FDG/PET CT could contribute to modified radiation treatment planning.

Effects of Noise in 4D CT on Deformable Image Registration and Derived Ventilation Data

Quantum noise is common in CT images and is a persistent problem in accurate ventilation imaging using 4D-CT and deformable image registration (DIR). This study focuses on the effects of noise in 4D-CT on DIR and thereby derived ventilation data. A total of six sets of 4D-CT data with landmarks delineated in different phases, called point-validated pixel-based breathing thorax models (POPI), were used in this study. The DIR algorithms, including diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B-spline, were used to register the inspiration phase to the expiration phase. The DIR deformation matrices (DIRDM) were used to map the landmarks. Target registration errors (TRE) were calculated as the distance errors between the delineated and the mapped landmarks. Noise of Gaussian distribution with different standard deviations (SD), from 0 to 200 Hounsfield Units (HU) in amplitude, was added to the POPI models to simulate different levels

A Comparison of Ventilation Methods Using 4D-CT and Different Deformable Image Registration Algorithms

Ventilation imaging using 4D-CT is a convenient and cost effective functional imaging methodology which might be of value in radiotherapy treatment planning to spare functional lung volumes. To calculate ventilation imaging from 4DCT we must use deformable image registration (DIR). This study investigates the dependence of calculated ventilation on DIR methods. DIR of the normal end expiration and inspiration phases of the 4D-CT images was used to correlate the voxels between the two respiratory phases. Three different DIR algorithms, Optical Flow (OF), Diffeomorphic Demons (DD) and Diffeomorphic Morphons (DM), were retrospectively applied to 10 esophagus and 10 lung cancer cases with 4D-CT image sets that encompassed the entire lung volume. The three ventilation extraction methods were used based on either the Jacobian, the change in volume of the voxel derived from the volume change of a voxel (ΔV) or directly calculated from the Hounsfield Unit (HU). The images were compared using the Dice similarity coefficient comparison. Dependence of ventilation images on the DIR was greater for the Δ V and the Jacobian methods than for the HU method. The Dice similarity coefficient for 20% of low ventilation volume for Δ V was 0.33 ± 0.03 between OF and DM, 0.44 ± 0.05 between OF and DD and 0.51 ± 0.04 between DM and DD. The similarity comparisons for Jacobian was 0.32 ± 0.03, 0.44 ± 0.05 and 0.51 ± 0.04 respectively, and for HU 0.53 ± 0.03, 0.56 ± 0.03 and 0.76 ± 0.04 respectively. Ventilation calculation from 4D-CT demonstrates some degree of dependency on the DIR algorithm employed.

SU‐E‐J‐66: Effects of Noise in 4D‐CT On Deformable Image Registration and Derived Ventilation Data

PURPOSE Deformable image registration (DIR) and 4D-CT have been proposed to generate ventilation images. Quantum noise is common in CT images. This study focuses on the effects of noise in 4D-CT on DIR and the derived ventilation data. METHODS Diffeomorphic morphons (DM), diffeomorphic demons (DD), optical flow and B-Spline were used to register the end-inspiration phase to the end-expiration phase of 6 4D-CT sets with landmarks delineated on different phases, called point-validated pixel-based breathing thorax models (POPI). Landmarks at expiration were mapped to inspiration using DIR deformation matrices (DIRDM) for each POPI model. Target registration errors (TRE) were calculated as the distances between the delineated and the mapped landmarks. Gaussian noise with different standard deviations (SD) of the amplitude was added to the POPI models to simulate different levels of quantum noise. Ventilation estimations were performed by calculating the volume change geometrically, based on the DIRDM. Ventilation images with different CT noise levels were compared using Dice similarity coefficient (DSC). RESULTS The root mean square (RMS) values of the landmark TRE over the 6 POPI models for the 4 DIR algorithms were stable when the noise level was below SD=150 Hounsfield Units (HU), and increased with the noise level. The most accurate DIR was DD with mean RMS of 1.5±0.5 and 1.8+-0.5 mm at the added noise SD=0 and 200 HU respectively. The DSC values between the ventilation images with and without added noise decreased with the noise level. The most consistent DIR was DM with mean DSC=0.89+- 0.01 and 0.66+-0.02 for the top 50% ventilation volumes with the added noise of 0 and 30, 0 and 200 HU respectively. CONCLUSION While the landmark TRE was stable with noise level for low noise, the differences between ventilation images increased, indicating that 4D-CT based ventilation imaging is sensitive to image noise. This work was partially supported by a grant from the Varian Medical Systems, Inc.