An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

2007 ◽  
Vol 34 (2) ◽  
pp. 711-721 ◽  
Author(s):  
Jan Ehrhardt ◽  
René Werner ◽  
Dennis Säring ◽  
Thorsten Frenzel ◽  
Wei Lu ◽  
...  
Keyword(s):  
Optical Flow ◽  
Free Breathing ◽  
Data Sets ◽  
4D Ct ◽  
Ct Data

Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory Dynamics

2007 ◽  
Vol 46 (03) ◽  
pp. 254-260 ◽  
Author(s):  
J. Ehrhardt ◽  
T. Frenzel ◽  
D. Säring ◽  
W. Lu ◽  
D. Low ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Analysis ◽  
Respiratory Motion ◽  
Image Data ◽  
Motion Artifacts ◽  
Data Sets ◽  
Ct Image ◽  
Ct Reconstruction ◽  
4D Ct ◽  
Ct Data

Summary Objectives: Respiratory motion represents a major problem in radiotherapy of thoracic and abdominal tumors. Methods for compensation require comprehensive knowledge of underlying dynamics. Therefore, 4D (= 3D + t) CT data can be helpful. But modern CT scanners cannot scan a large region of interest simultaneously. So patients have to be scanned in segments. Commonly used approaches for reconstructing the data segments into 4D CT images cause motion artifacts. In orderto reduce the artifacts, a new method for 4D CT reconstruction is presented. The resulting data sets are used to analyze respiratory motion. Methods: Spatiotemporal CT image sequences of lung cancer patients were acquired using a multi-slice CT in cine mode during free breathing. 4D CT reconstruction was done by optical flow based temporal interpolation. The resulting 4D image data were compared with data generated bythe commonly used nearest neighbor reconstruction. Subsequent motion analysis is mainly concerned with tumor mobility. Results: The presented optical flow-based method enables the reconstruction of 3D CT images at arbitrarily chosen points of the patient’s breathing cycle. A considerable reduction of motion artifacts has been proven in eight patient data sets. Motion analysis showed that tumor mobility differs strongly between the patients. Conclusions: Due to the proved reduction of motion artifacts, the optical flow-based 4D CT reconstruction offers the possibility of high-quality motion analysis. Because the method is based on an interpolation scheme, it additionally has the potential to enable the reconstruction of 4D CT data from a lesser number of scans.

Reconstruction of 4D-CT data sets acquired during free breathing for the analysis of respiratory motion

2006 ◽  
Author(s):  
Jan Ehrhardt ◽  
Rene Werner ◽  
Thorsten Frenzel ◽  
Dennis Säring ◽  
Wei Lu ◽  
...  
Keyword(s):  
Respiratory Motion ◽  
Free Breathing ◽  
Data Sets ◽  
4D Ct ◽  
Ct Data

scholarly journals Lung Segmentation in 4D CT Volumes Based on Robust Active Shape Model Matching

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Gurman Gill ◽  
Reinhard R. Beichel
Keyword(s):  
Image Data ◽  
Active Shape Model ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Shape Model ◽  
Lung Segmentation ◽  
4D Ct ◽  
Active Shape ◽  
Ct Data

Dynamic and longitudinal lung CT imaging produce 4D lung image data sets, enabling applications like radiation treatment planning or assessment of response to treatment of lung diseases. In this paper, we present a 4D lung segmentation method that mutually utilizes all individual CT volumes to derive segmentations for each CT data set. Our approach is based on a 3D robust active shape model and extends it to fully utilize 4D lung image data sets. This yields an initial segmentation for the 4D volume, which is then refined by using a 4D optimal surface finding algorithm. The approach was evaluated on a diverse set of 152 CT scans of normal and diseased lungs, consisting of total lung capacity and functional residual capacity scan pairs. In addition, a comparison to a 3D segmentation method and a registration based 4D lung segmentation approach was performed. The proposed 4D method obtained an average Dice coefficient of0.9773±0.0254, which was statistically significantly better (pvalue≪0.001) than the 3D method (0.9659±0.0517). Compared to the registration based 4D method, our method obtained better or similar performance, but was 58.6% faster. Also, the method can be easily expanded to process 4D CT data sets consisting of several volumes.

scholarly journals Does Motion Assessment With 4-Dimensional Computed Tomographic Imaging for Non–Small Cell Lung Cancer Radiotherapy Improve Target Volume Coverage?

2017 ◽  
Vol 11 ◽  
pp. 117955491769846 ◽  
Author(s):  
Naseer Ahmed ◽  
Sankar Venkataraman ◽  
Kate Johnson ◽  
Keith Sutherland ◽  
Shaun K Loewen
Keyword(s):  
Lung Cancer ◽  
Gross Tumor Volume ◽  
Tumor Volume ◽  
Target Volume ◽  
Data Sets ◽  
Nodal Disease ◽  
Dice Coefficient ◽  
Data Set ◽  
4D Ct ◽  
Ct Data

Introduction: Modern radiotherapy with 4-dimensional computed tomographic (4D-CT) image acquisition for non–small cell lung cancer (NSCLC) captures respiratory-mediated tumor motion to provide more accurate target delineation. This study compares conventional 3-dimensional (3D) conformal radiotherapy (3DCRT) plans generated with standard helical free-breathing CT (FBCT) with plans generated on 4D-CT contoured volumes to determine whether target volume coverage is affected. Materials and methods: Fifteen patients with stage I to IV NSCLC were enrolled in the study. Free-breathing CT and 4D-CT data sets were acquired at the same simulation session and with the same immobilization. Gross tumor volume (GTV) for primary and/or nodal disease was contoured on FBCT (GTV_3D). The 3DCRT plans were obtained, and the patients were treated according to our institution’s standard protocol using FBCT imaging. Gross tumor volume was contoured on 4D-CT for primary and/or nodal disease on all 10 respiratory phases and merged to create internal gross tumor volume (IGTV)_4D. Clinical target volume margin was 5 mm in both plans, whereas planning tumor volume (PTV) expansion was 1 cm axially and 1.5 cm superior/inferior for FBCT-based plans to incorporate setup errors and an estimate of respiratory-mediated tumor motion vs 8 mm isotropic margin for setup error only in all 4D-CT plans. The 3DCRT plans generated from the FBCT scan were copied on the 4D-CT data set with the same beam parameters. GTV_3D, IGTV_4D, PTV, and dose volume histogram from both data sets were analyzed and compared. Dice coefficient evaluated PTV similarity between FBCT and 4D-CT data sets. Results: In total, 14 of the 15 patients were analyzed. One patient was excluded as there was no measurable GTV. Mean GTV_3D was 115.3 cm3 and mean IGTV_4D was 152.5 cm3 ( P = .001). Mean PTV_3D was 530.0 cm3 and PTV_4D was 499.8 cm3 ( P = .40). Both gross primary and nodal disease analyzed separately were larger on 4D compared with FBCT. D95 (95% isodose line) covered 98% of PTV_3D and 88% of PTV_4D ( P = .003). Mean dice coefficient of PTV_3D and PTV_4D was 84%. Mean lung V20 was 24.0% for the 3D-based plans and 22.7% for the 4D-based plans ( P = .057). Mean heart V40 was 12.1% for the 3D-based plans and 12.7% for the 4D-based plans ( P = .53). Mean spinal cord Dmax was 2517 and 2435 cGy for 3D-based and 4D-based plans, respectively ( P = .019). Mean esophageal dose was 1580 and 1435 cGy for 3D and 4D plans, respectively ( P = .13). Conclusions: IGTV_4D was significantly larger than GTV_3D for both primary and nodal disease combined or separately. Mean PTV_3D was larger than PTV_4D, but the difference was not statistically significant. The PTV_4D coverage with 95% isodose line was inferior, indicating the importance of incorporating the true size and shape of the target volume. Relatively less dose was delivered to spinal cord and esophagus with plans based on 4D data set. Dice coefficient analysis for degree of similarity revealed that 16% of PTVs from both data sets did not overlap, indicating different anatomical positions of the PTV due to tumor/nodal motion during a respiratory cycle. All patients with lung cancer planned for radical radiotherapy should have 4D-CT simulation to ensure accurate coverage of the target volumes.

Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT

2007 ◽  
Vol 46 (01) ◽  
pp. 38-42 ◽  
Author(s):  
V. Schulz ◽  
I. Nickel ◽  
A. Nömayr ◽  
A. H. Vija ◽  
C. Hocke ◽  
...  
Keyword(s):  
Vertebral Body ◽  
Facet Joint ◽  
One Dimension ◽  
Patient Specific ◽  
Data Sets ◽  
Facet Joints ◽  
Lumbar Vertebral Body ◽  
Ct System ◽  
Ct Data ◽  
Attenuation Corrected

SummaryThe aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting leftto- right ratios. Therefore, at clinical evaluation of attenuation- corrected scans special attention should be given to possible misalignments between SPECT and CT.

4D Computed Tomography for Dynamic Upper Airway Evaluation in Robin Sequence

2021 ◽  
pp. 019459982110021
Author(s):  
Austin S. Lam ◽  
Michael D. Bindschadler ◽  
Kelly N. Evans ◽  
Seth D. Friedman ◽  
Jeffrey P. Otjen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Upper Airway ◽  
Quantitative Information ◽  
4D Ct ◽  
Robin Sequence ◽  
Analysis Tools ◽  
Airway Caliber ◽  
4D Computed Tomography ◽  
Ct Data ◽  
Sample Case

Thorough assessment of dynamic upper airway obstruction (UAO) in Robin sequence (RS) is critical, but traditional evaluation modalities have significant limitations. Four-dimensional computed tomography (4D-CT) is promising in that it enables objective and quantitative evaluation throughout all phases of respiration. However, there exist few protocols or analysis tools to assist in obtaining and interpreting the vast amounts of obtained data. A protocol and set of data analysis tools were developed to enable quantification and visualization of dynamic 4D-CT data. This methodology was applied to a sample case at 2 time points. In the patient with RS, overall increases in normalized airway caliber were observed from 5 weeks to 1 year. There was, however, continued dynamic obstruction at all airway levels, though objective measures of UAO did improve at the nasopharynx and oropharynx. Use of 4D-CT and novel analyses provide additional quantitative information to evaluate UAO in patients with RS.

Planning lung radiotherapy using 4D CT data and a motion model

2008 ◽  
Vol 53 (20) ◽  
pp. 5815-5830 ◽  
Author(s):  
R Colgan ◽  
J McClelland ◽  
D McQuaid ◽  
P M Evans ◽  
D Hawkes ◽  
...  
Keyword(s):  
Motion Model ◽  
4D Ct ◽  
Ct Data ◽  
Lung Radiotherapy

Computer-assisted Diagnosis for Early Stage Pleural Mesothelioma

2007 ◽  
Vol 46 (03) ◽  
pp. 324-331 ◽  
Author(s):  
P. Jäger ◽  
S. Vogel ◽  
A. Knepper ◽  
T. Kraus ◽  
T. Aach ◽  
...  
Keyword(s):  
Early Stage ◽  
Computation Time ◽  
Hounsfield Unit ◽  
Computer Assisted ◽  
Clinical Test ◽  
Pleural Mesothelioma ◽  
Data Sets ◽  
Data Set ◽  
Average Computation Time ◽  
Ct Data

Summary Objectives: Pleural thickenings as biomarker of exposure to asbestos may evolve into malignant pleural mesothelioma. Foritsearly stage, pleurectomy with perioperative treatment can reduce morbidity and mortality. The diagnosis is based on a visual investigation of CT images, which is a time-consuming and subjective procedure. Our aim is to develop an automatic image processing approach to detect and quantitatively assess pleural thickenings. Methods: We first segment the lung areas, and identify the pleural contours. A convexity model is then used together with a Hounsfield unit threshold to detect pleural thickenings. The assessment of the detected pleural thickenings is based on a spline-based model of the healthy pleura. Results: Tests were carried out on 14 data sets from three patients. In all cases, pleural contours were reliably identified, and pleural thickenings detected. PC-based Computation times were 85 min for a data set of 716 slices, 35 min for 401 slices, and 4 min for 75 slices, resulting in an average computation time of about 5.2 s per slice. Visualizations of pleurae and detected thickeningswere provided. Conclusion: Results obtained so far indicate that our approach is able to assist physicians in the tedious task of finding and quantifying pleural thickenings in CT data. In the next step, our system will undergo an evaluation in a clinical test setting using routine CT data to quantifyits performance.

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