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Title: SU-G-JeP2-01: A New Approach for MR-Only Treatment Planning: Tissue Segmentation-Based Pseudo-CT Generation Using T1-Weighted MRI

Abstract

Purpose: To evaluate MR-only treatment planning for brain Stereotactic Ablative Radiotherapy (SABR) based on pseudo-CT (pCT) generation using one set of T1-weighted MRI. Methods: T1-weighted MR and CT images from 12 patients who were eligible for brain SABR were retrospectively acquired for this study. MR-based pCT was generated by using a newly in-house developed algorithm based on MR tissue segmentation and voxel-based electron density (ED) assignment (pCTv). pCTs using bulk density assignment (pCTb where bone and soft tissue were assigned 800HU and 0HU,respectively), and water density assignment (pCTw where all tissues were assigned 0HU) were generated for comparison of ED assignment techniques. The pCTs were registered with CTs and contours of radiation targets and Organs-at-Risk (OARs) from clinical CT-based plans were copied to co-registered pCTs. Volumetric-Modulated-Arc-Therapy(VMAT) plans were independently created for pCTv and CT using the same optimization settings and a prescription (50Gy/10 fractions) to planning-target-volume (PTV) mean dose. pCTv-based plans and CT-based plans were compared with dosimetry parameters and monitor units (MUs). Beam fluence maps of CT-based plans were transferred to co-registered pCTs, and dose was recalculated on pCTs. Dose distribution agreement between pCTs and CT plans were quantified using Gamma analysis (2%/2mm, 1%/1mm with a 10% cut-off threshold)more » in axial, coronal and sagittal planes across PTV. Results: The average differences of PTV mean and maximum doses, and monitor units between independently created pCTv-based and CT-based plans were 0.5%, 1.5% and 1.1%, respectively. Gamma analysis of dose distributions of the pCTs and the CT calculated using the same fluence map resulted in average agreements of 92.6%/79.1%/52.6% with 1%/1mm criterion, and 98.7%/97.4%/71.5% with 2%/2mm criterion, for pCTv/CT, pCTb/CT and pCTw/CT, respectively. Conclusion: Plans produced on Voxel-based pCT is dosimetrically more similar to CT plans than bulk assignment-based pCTs. MR-only treatment planning using voxel-based pCT generated from T1-wieghted MRI may be feasible.« less

Authors:
;  [1]; ; ; ; ;  [2]
  1. N Eastern Ontario Cancer Center, Sudbury, ON (Canada)
  2. Sunnybrook Health Sciences Center, Toronto, Ontario (Canada)
Publication Date:
OSTI Identifier:
22649367
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; BONE TISSUES; BULK DENSITY; CAT SCANNING; NMR IMAGING; PLANNING; PLANT TISSUES; RADIATION DOSE DISTRIBUTIONS; RADIOTHERAPY

Citation Formats

Yu, H, Leszczynski, K, Lee, Y, Chugh, B, Tseng, C, Campbell, M, and Sahgal, A. SU-G-JeP2-01: A New Approach for MR-Only Treatment Planning: Tissue Segmentation-Based Pseudo-CT Generation Using T1-Weighted MRI. United States: N. p., 2016. Web. doi:10.1118/1.4957021.
Yu, H, Leszczynski, K, Lee, Y, Chugh, B, Tseng, C, Campbell, M, & Sahgal, A. SU-G-JeP2-01: A New Approach for MR-Only Treatment Planning: Tissue Segmentation-Based Pseudo-CT Generation Using T1-Weighted MRI. United States. doi:10.1118/1.4957021.
Yu, H, Leszczynski, K, Lee, Y, Chugh, B, Tseng, C, Campbell, M, and Sahgal, A. 2016. "SU-G-JeP2-01: A New Approach for MR-Only Treatment Planning: Tissue Segmentation-Based Pseudo-CT Generation Using T1-Weighted MRI". United States. doi:10.1118/1.4957021.
@article{osti_22649367,
title = {SU-G-JeP2-01: A New Approach for MR-Only Treatment Planning: Tissue Segmentation-Based Pseudo-CT Generation Using T1-Weighted MRI},
author = {Yu, H and Leszczynski, K and Lee, Y and Chugh, B and Tseng, C and Campbell, M and Sahgal, A},
abstractNote = {Purpose: To evaluate MR-only treatment planning for brain Stereotactic Ablative Radiotherapy (SABR) based on pseudo-CT (pCT) generation using one set of T1-weighted MRI. Methods: T1-weighted MR and CT images from 12 patients who were eligible for brain SABR were retrospectively acquired for this study. MR-based pCT was generated by using a newly in-house developed algorithm based on MR tissue segmentation and voxel-based electron density (ED) assignment (pCTv). pCTs using bulk density assignment (pCTb where bone and soft tissue were assigned 800HU and 0HU,respectively), and water density assignment (pCTw where all tissues were assigned 0HU) were generated for comparison of ED assignment techniques. The pCTs were registered with CTs and contours of radiation targets and Organs-at-Risk (OARs) from clinical CT-based plans were copied to co-registered pCTs. Volumetric-Modulated-Arc-Therapy(VMAT) plans were independently created for pCTv and CT using the same optimization settings and a prescription (50Gy/10 fractions) to planning-target-volume (PTV) mean dose. pCTv-based plans and CT-based plans were compared with dosimetry parameters and monitor units (MUs). Beam fluence maps of CT-based plans were transferred to co-registered pCTs, and dose was recalculated on pCTs. Dose distribution agreement between pCTs and CT plans were quantified using Gamma analysis (2%/2mm, 1%/1mm with a 10% cut-off threshold) in axial, coronal and sagittal planes across PTV. Results: The average differences of PTV mean and maximum doses, and monitor units between independently created pCTv-based and CT-based plans were 0.5%, 1.5% and 1.1%, respectively. Gamma analysis of dose distributions of the pCTs and the CT calculated using the same fluence map resulted in average agreements of 92.6%/79.1%/52.6% with 1%/1mm criterion, and 98.7%/97.4%/71.5% with 2%/2mm criterion, for pCTv/CT, pCTb/CT and pCTw/CT, respectively. Conclusion: Plans produced on Voxel-based pCT is dosimetrically more similar to CT plans than bulk assignment-based pCTs. MR-only treatment planning using voxel-based pCT generated from T1-wieghted MRI may be feasible.},
doi = {10.1118/1.4957021},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft-tissue) on T1 -weighted brain MRI and to create a pseudo-CT for MRI-only radiation therapy verification. Methods: Contrast-enhanced T1-weighted fast-spin-echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI-Simulator, are used.MRIs are firstly pre-processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft-tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bonemore » and soft-tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan-line filling algorithm. The air mask is extracted by morphological opening followed by a post-processing based on knowledge about air regions geometry. The remaining rough bone pre-segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo-CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft-tissue MR intensities in [-400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU]. Results: Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft-tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft-tissue (0.91) but low for air regions (0.48). Pseudo-CT produces DRRs with acceptable clinical visual agreement to CT-based DRR. Conclusion: The proposed approach makes it possible to use T1-weighted MRI to generate accurate pseudo-CT from 4-class segmentation.« less
  • Purpose: Automatically derive electron density of tissues using MR images and generate a pseudo-CT for MR-only treatment planning of brain tumours. Methods: 20 stereotactic radiosurgery (SRS) patients’ T1-weighted MR images and CT images were retrospectively acquired. First, a semi-automated tissue segmentation algorithm was developed to differentiate tissues with similar MR intensities and large differences in electron densities. The method started with approximately 12 slices of manually contoured spatial regions containing sinuses and airways, then air, bone, brain, cerebrospinal fluid (CSF) and eyes were automatically segmented using edge detection and anatomical information including location, shape, tissue uniformity and relative intensity distribution.more » Next, soft tissues - muscle and fat were segmented based on their relative intensity histogram. Finally, intensities of voxels in each segmented tissue were mapped into their electron density range to generate pseudo-CT by linearly fitting their relative intensity histograms. Co-registered CT was used as a ground truth. The bone segmentations of pseudo-CT were compared with those of co-registered CT obtained by using a 300HU threshold. The average distances between voxels on external edges of the skull of pseudo-CT and CT in three axial, coronal and sagittal slices with the largest width of skull were calculated. The mean absolute electron density (in Hounsfield unit) difference of voxels in each segmented tissues was calculated. Results: The average of distances between voxels on external skull from pseudo-CT and CT were 0.6±1.1mm (mean±1SD). The mean absolute electron density differences for bone, brain, CSF, muscle and fat are 78±114 HU, and 21±8 HU, 14±29 HU, 57±37 HU, and 31±63 HU, respectively. Conclusion: The semi-automated MR electron density mapping technique was developed using T1-weighted MR images. The generated pseudo-CT is comparable to that of CT in terms of anatomical position of tissues and similarity of electron density assignment. This method can allow MR-only treatment planning.« less
  • Purpose: To develop an image processing method for MRI-based generation of electron density maps, known as pseudo-CT (pCT), without usage of model- or atlas-based segmentation, and to evaluate the method in the pelvic and head-neck region against CT. Methods: CT and MRI scans were obtained from the pelvic region of four patients in supine position using a flat table top only for CT. Stratified CT maps were generated by classifying each voxel based on HU ranges into one of four classes: air, adipose tissue, soft tissue or bone.A hierarchical region-selective algorithm, based on automatic thresholding and clustering, was used tomore » classify tissues from MR Dixon reconstructed fat, In-Phase (IP) and Opposed-Phase (OP) images. First, a body mask was obtained by thresholding the IP image. Subsequently, an automatic threshold on the Dixon fat image differentiated soft and adipose tissue. K-means clustering on IP and OP images resulted in a mask that, via a connected neighborhood analysis, allowing the user to select the components corresponding to bone structures.The pCT was estimated through assignment of bulk HU to the tissue classes. Bone-only Digital Reconstructed Radiographs (DRR) were generated as well. The pCT images were rigidly registered to the stratified CT to allow a volumetric and voxelwise comparison. Moreover, pCTs were also calculated within the head-neck region in two volunteers using the same pipeline. Results: The volumetric comparison resulted in differences <1% for each tissue class. A voxelwise comparison showed a good classification, ranging from 64% to 98%. The primary misclassified classes were adipose/soft tissue and bone/soft tissue. As the patients have been imaged on different table tops, part of the misclassification error can be explained by misregistration. Conclusion: The proposed approach does not rely on an anatomy model providing the flexibility to successfully generate the pCT in two different body sites. This research is founded by ZonMw IMDI Programme, project name: “RASOR sharp: MRI based radiotherapy planning using a single MRI sequence”, project number: 10-104003010.« less
  • Purpose: MRI has a number of advantages over CT as a primary modality for radiation treatment planning (RTP). However, one key bottleneck problem still remains, which is the lack of electron density information in MRI. In the work, a reliable method to map electron density is developed by leveraging the differential contrast of multi-parametric MRI. Methods: We propose a probabilistic Bayesian approach for electron density mapping based on T1 and T2-weighted MRI, using multiple patients as atlases. For each voxel, we compute two conditional probabilities: (1) electron density given its image intensity on T1 and T2-weighted MR images, and (2)more » electron density given its geometric location in a reference anatomy. The two sources of information (image intensity and spatial location) are combined into a unifying posterior probability density function using the Bayesian formalism. The mean value of the posterior probability density function provides the estimated electron density. Results: We evaluated the method on 10 head and neck patients and performed leave-one-out cross validation (9 patients as atlases and remaining 1 as test). The proposed method significantly reduced the errors in electron density estimation, with a mean absolute HU error of 138, compared with 193 for the T1-weighted intensity approach and 261 without density correction. For bone detection (HU>200), the proposed method had an accuracy of 84% and a sensitivity of 73% at specificity of 90% (AUC = 87%). In comparison, the AUC for bone detection is 73% and 50% using the intensity approach and without density correction, respectively. Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection based on multi-parametric MRI of the head with highly heterogeneous anatomy. This could allow for accurate dose calculation and reference image generation for patient setup in MRI-based radiation treatment planning.« less
  • Purpose: Investigating a new approach in MRI based treatment planning using the combination of (Ultrashort Echo Time) UTE and T1 weighted spin echo pulse sequences to delineate air, bone and water (soft tissues) in generating pseudo CT images comparable with CT. Methods: A gel phantom containing chicken bones, ping pang balls filled with distilled water and air bubbles, was made. It scanned with MRI using UTE and 2D T1W SE pulse sequences with (in plane resolution= 0.53mm, slice thickness= 2 mm) and CT with (in plane resolution= 0.5 mm and slice thickness= 0.75mm) as a ground truth for geometrical accuracy.more » The UTE and T1W SE images were registered with CT using mutual information registration algorithm provided by Philips Pinnacle treatment planning system. The phantom boundaries were detected using Canny edge detection algorithm for CT, and MR images. The bone, air bubbles and water in ping pong balls were segmented from CT images using threshold 300HU, - 950HU and 0HU, respectively. These tissue inserts were automatically segmented from combined UTE and T1W SE images using edge detection and relative intensity histograms of the phantom. The obtained segmentations of air, bone and water inserts were evaluated with those obtained from CT. Results: Bone and air can be clearly differentiated in UTE images comparable to CT. Combining UTE and T1W SE images successfully segmented the air, bone and water. The maximum segmentation differences from combine MRI images (UTE and T1W SE) and CT are within 1.3 mm, 1.1mm for bone, air, respectively. The geometric distortion of UTE sequence is small less than 1 pixel (0.53 mm) of MR image resolution. Conclusion: Our approach indicates that MRI can be used solely for treatment planning and its quality is comparable with CT.« less