Jinghao Zhou

Intrafractional Target Motions and Uncertainties of Treatment Setup Reference Systems in Accelerated Partial Breast Irradiation

PURPOSE This study investigated the magnitude of intrafractional motion and level of accuracy of various setup strategies in accelerated partial breast irradiation (APBI) using three-dimensional conformal external beam radiotherapy. METHODS AND MATERIALS At lumpectomy, gold fiducial markers were strategically sutured to the surrounding walls of the cavity. Weekly fluoroscopy imaging was conducted at treatment to investigate the respiration-induced target motions. Daily pre- and post-RT kV imaging was performed, and images were matched to digitally reconstructed radiographs based on bony anatomy and fiducial markers, respectively, to determine the intrafractional motion magnitudes over the course of treatment. The positioning differences of the laser tattoo- and the bony anatomy-based setups compared with those of the marker-based setup (benchmark) were also determined. The study included 21 patients. RESULTS Although lung exhibited significant motion, the average marker motion amplitude on the fluoroscopic image was about 1 mm. Over a typical treatment time period, average intrafractional motion magnitude was 4.2 mm and 2.6 mm based on the marker and bony anatomy matching, respectively. The bony anatomy- and laser tattoo-based interfractional setup errors, with respect to the fiducial marker-based setup, were 7.1 and 9.0 mm, respectively. CONCLUSIONS Respiration has limited effects on the target motion during APBI. Bony anatomy-based treatment setup improves the accuracy relative to that of the laser tattoo-based setup approach. Since fiducial markers are sutured directly to the surgical cavity, the marker-based approach can further improve the interfractional setup accuracy. On average, a seroma cavity exhibits intrafractional motion of more than 4 mm, a magnitude that is larger than that which is otherwise derived based on bony anatomy matching. A seroma-specific marker-based approach has the potential to improve treatment accuracy by taking the true inter- and intrafractional motions into consideration.

Automatic detection and segmentation of ground glass opacity nodules

Ground Glass Opacity (GGO) is defined as hazy increased attenuation within a lung that is not associated with obscured underlying vessels. Since pure (nonsolid) or mixed (partially solid) GGO at the thin-section CT are more likely to be malignant than those with solid opacity, early detection and treatment of GGO can improve a prognosis of lung cancer. However, due to indistinct boundaries and inter- or intra-observer variation, consistent manual detection and segmentation of GGO have proved to be problematic. In this paper, we propose a novel method for automatic detection and segmentation of GGO from chest CT images. For GGO detection, we develop a classifier by boosting k-NN whose distance measure is the Euclidean distance between the nonparametric density estimates of two examples. The detected GGO region is then automatically segmented by analyzing the texture likelihood map of the region. We applied our method to clinical chest CT volumes containing 10 GGO nodules. The proposed method detected all of the 10 nodules with only one false positive nodule. We also present the statistical validation of the proposed classifier for GGO detection as well as very promising results for automatic GGO segmentation. The proposed method provides a new powerful tool for automatic detection as well as accurate and reproducible segmentation of GGO.

VASCULAR STRUCTURE SEGMENTATION AND BIFURCATION DETECTION

Delineation and reconstruction of vascular structures in medical images are critical for the diagnosis of various vascular diseases and related surgical procedures. In this paper, we present a novel method for vascular structure segmentation with fully automatic detection of bifurcation points. First, we perform a preselection of tubular objects and trace the vessels based on the eigenanalysis of the Hessian matrix. This provides us the estimated direction of vessels as well as the cross-sectional planes orthogonal to the vessels. Then, we apply AdaBoost learning method with specially designed filters on cross-sectional planes to automatically detect the bifurcation points of the vessels. Our method has over 97% success rate for detecting bifurcation points. We present very promising results of our method applied to the reconstruction of pulmonary vessels from clinical chest CT. Our method allows for fully automatic detection of bifurcation points as well as segmentation of vessels

An Automatic Method for Ground Glass Opacity Nodule Detection and Segmentation from CT Studies

Ground glass opacity (GGO) is defined as hazy increased attenuation within a lung that is not associated with obscured underlying vessels. Since pure (non-solid) or mixed (partially solid) GGO at the thin-section CT are more likely to be malignant than those with solid opacity, early detection and treatment of GGO can improve a prognosis of lung cancer. However, due to indistinct boundaries and inter-or intra-observer variation, consistent manual detection and segmentation of GGO have proved to be problematic. In this paper, we propose a novel method for automatic detection and segmentation of GGO from chest CT images. For GGO detection, we develop a classifier by boosting k-nearest neighbor (k-NN), whose distance measure is the Euclidean distance between the nonparametric density estimates of two regions. The detected GGO region is then automatically segmented by analyzing the 3D texture likelihood map of the region. We applied our method to clinical chest CT volumes containing 10 GGO nodules. The proposed method detected all of the 10 nodules with only one false positive nodule. We also present the statistical validation of the proposed classifier for automatic GGO detection as well as very promising results for automatic GGO segmentation. The proposed method provides a new powerful tool for automatic detection as well as accurate and reproducible segmentation of GGO

Determination of optimal fiducial marker across image-guided radiation therapy (IGRT) modalities: visibility and artifact analysis of gold, carbon, and polymer fiducial markers

The purpose of this study was to evaluate the visibility and artifact created by gold, carbon, and polymer fiducial markers in a simple phantom across computed tomography (CT), kilovoltage (kV), and megavoltage (MV) linear accelerator imaging and MV tomotherapy imaging. Three types of fiducial markers (gold, carbon, and polymer) were investigated for their visibility and artifacts in images acquired with various modalities and with different imaging parameters (kV, mAs, slice thickness). The imaging modalities include kV CT, 2D linac‐based kilovoltage and megavoltage X‐ray imaging systems, kV cone‐beam CT, and normal and fine tomotherapy imaging. The images were acquired on a phantom constructed using Superflab bolus in which markers of each type were inserted into the center layer. The visibility and artifacts produced by each marker were assessed qualitatively and quantitatively. All tested markers could be identified clearly on the acquired CT and linac‐based kV images; gold markers demonstrated the highest contrast. On the CT images, gold markers produced a significant artifact, while no artifacts were observed with polymer markers. Only gold markers were visible when using linac‐based MV and tomotherapy imaging. For linac‐based kV images, the contrast increased with kV and mAs values for all the markers, with the gold being the most pronounced. On CT images, the contrast increased with kV for the gold markers, while decreasing for the polymer and carbon marker. With the bolus phantom used, we found that when kV imaging‐based treatment verification equipment is available, polymer and carbon markers may be the preferred choice for target localization and patient treatment positioning verification due to less image artifacts. If MV imaging will be the sole modality for positioning verification, it may be necessary to use gold markers despite the artifacts they create on the simulation CT images. PACS number: 87

A Comparison of Helical Intensity-Modulated Radiotherapy, Intensity-Modulated Radiotherapy, and 3D-Conformal Radiation Therapy for Pancreatic Cancer

We assessed dosimetric differences in pancreatic cancer radiotherapy via helical intensity-modulated radiotherapy (HIMRT), linac-based IMRT, and 3D-conformal radiation therapy (3D-CRT) with regard to successful plan acceptance and dose to critical organs. Dosimetric analysis was performed in 16 pancreatic cases that were planned to 54 Gy; both post-pancreaticoduodenectomy (n = 8) and unresected (n = 8) cases were compared. Without volume modification, plans met constraints 75% of the time with HIMRT and IMRT and 13% with 3D-CRT. There was no statistically significantly improvement with HIMRT over conventional IMRT in reducing liver V35, stomach V45, or bowel V45. HIMRT offers improved planning target volume (PTV) dose homogeneity compared with IMRT, averaging a lower maximum dose and higher volume receiving the prescription dose (D100). HIMRT showed an increased mean dose over IMRT to bowel and liver. Both HIMRT and IMRT offer a statistically significant improvement over 3D-CRT in lowering dose to liver, stomach, and bowel. The results were similar for both unresected and resected patients. In pancreatic cancer, HIMRT offers improved dose homogeneity over conventional IMRT and several significant benefits to 3D-CRT. Factors to consider before incorporating IMRT into pancreatic cancer therapy are respiratory motion, dose inhomogeneity, and mean dose.

Vessel boundary extraction using ridge scan-conversion deformable model

Delineation and reconstruction of curvilinear structures in medical images are critical for the diagnosis of various vascular diseases and related surgical procedures. In this paper, we present a novel method for vascular structure segmentation and reconstruction, including the automatic detection of bifurcation points. First, we perform a preselection of tubular structures. Second, we trace the vessels based on the eigenanalysis of the Hessian matrix. This provides us the estimated direction of vessels as well as the cross-sectional planes orthogonal to the vessels. A ScanConversion method is then applied to cross-sectional planes to automatically detect the bifurcation points of the vessels. This method has a 96.59% success rate for detecting bifurcation correctly. Finally, vessels are delineated and reconstructed using deformable models. Our method is efficient and allows for completely automatic delineation and reconstruction of vessels as well as automatic detection of bifurcation points

A 3D global-to-local deformable mesh model based registration and anatomy-constrained segmentation method for image guided prostate radiotherapy

PURPOSE In the external beam radiation treatment of prostate cancers, successful implementation of adaptive radiotherapy and conformal radiation dose delivery is highly dependent on precise and expeditious segmentation-and registration of the prostate volume between the simulation and the treatment images. The purpose of this study is to develop a novel, fast, and accurate segmentation and registration method to increase the computational efficiency to meet the restricted clinical treatment time requirement in image guided radiotherapy. METHODS The method developed in this study used soft tissues to capture the transformation between the 3D planning CT (pCT) images and 3D cone-beam CT (CBCT) treatment images. The method incorporated a global-to-local deformable mesh model based registration framework as well as an automatic anatomy-constrained robust active shape model (ACRASM) based segmentation algorithm in the 3D CBCT images. The global registration was based on the mutual information method, and the local registration was to minimize the Euclidian distance of the corresponding nodal points from the global transformation of deformable mesh models, which implicitly used the information of the segmented target volume. The method was applied on six data sets of prostate cancer patients. Target volumes delineated by the same radiation oncologist on the pCT and CBCT were chosen as the benchmarks and were compared to the segmented and registered results. The distance-based and the volume-based estimators were used to quantitatively evaluate the results of segmentation and registration. RESULTS The ACRASM segmentation algorithm was compared to the original active shape mode (ASM) algorithm by evaluating the values of the distance-based estimators. With respect to the corresponding benchmarks, the mean distance ranged from -0.85 to 0.84 mm for ACRASM and from -1.44 to 1.17 mm for ASM. The mean absolute distance ranged from 1.77 to 3.07 mm for ACRASM and from 2.45 to 6.54 mm for ASM. The volume overlap ratio ranged from 79% to 91% for ACRASM and from 44% to 80% for ASM. These data demonstrated that the segmentation results of ACRASM were in better agreement with the corresponding benchmarks than those of ASM. The developed registration algorithm was quantitatively evaluated by comparing the registered target volumes from the pCT to the benchmarks on the CBCT. The mean distance and the root mean square error ranged from 0.38 to 2.2 mm and from 0.45 to 2.36 mm, respectively, between the CBCT images and the registered pCT. The mean overlap ratio of the prostate volumes ranged from 85.2% to 95% after registration. The average time of the ACRASM-based segmentation was under 1 min. The average time of the global transformation was from 2 to 4 min on two 3D volumes and the average time of the local transformation was from 20 to 34 s on two deformable superquadrics mesh models. CONCLUSIONS A novel and fast segmentation and deformable registration method was developed to capture the transformation between the planning and treatment images for external beam radiotherapy of prostate cancers. This method increases the computational efficiency and may provide foundation to achieve real time adaptive radiotherapy.

A novel learning based segmentation method for rodent brain structures using MRI

This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.

3D-3D tubular organs registration based on bifurcations for the CT images

The registration of tubular organs (pulmonary tracheobronchial tree or vasculature) of 3D medical images is critical in various clinical applications such as surgical planning and radiotherapy. In this paper, we present a novel method for tubular organs registration based on the automatically detected bifurcation points of the tubular organs. We first perform a 3D tubular organ segmentation method to extract the centerlines of tubular organs and radius estimation in both planning and respiration-correlated CT (RCCT) images. This segmentation method automatically detects the bifurcation points by applying Adaboost algorithm with specially designed filters. We then apply a rigid registration method which minimizes the least square error of the corresponding bifurcation points between the planning CT images and the respiration-correlated CT images. Our method has over 96% success rate for detecting bifurcation points.We present very promising results of our method applied to the registration of the planning and respiration-correlated CT images. On average, the mean distance and the root-mean-square error (RMSE) of the corresponding bifurcation points between the respiration-correlated images and the registered planning images are less than 2.7 mm.