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Title: SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning

Abstract

Purpose: To develop an image fusion method using multiple contrast MRI scans for MR-based treatment planning. Methods: T1 weighted (T1-w), T2 weighted (T2-w) and diffusion weighted images (DWI) were acquired from liver cancer patient with breath-holding. Image fade correction and deformable image registration were performed using VelocityAI (Varian Medical Systems, CA). Registered images were normalized to mean voxel intensity for each image dataset. Contrast to noise ratio (CNR) between tumor and liver was quantified. Tumor area was defined as the GTV contoured by physicians. Normal liver area with equivalent dimension was used as background. Noise was defined by the standard deviation of voxel intensities in the same liver area. Linear weightings were applied to T1-w, T2-w and DWI images to generate composite image and CNR was calculated for each composite image. Optimization process were performed to achieve different clinical goals. Results: With a goal of maximizing tumor contrast, the composite image achieved a 7–12 fold increase in tumor CNR (142.8 vs. −2.3, 11.4 and 20.6 for T1-w, T2-w and DWI only, respectively), while anatomical details were largely invisible. With a weighting combination of 100%, −10% and −10%, respectively, tumor contrast was enhanced from −2.3 to −5.4, while the anatomical detailsmore » were clear. With a weighting combination of 25%, 20% and 55%, balanced tumor contrast and anatomy was achieved. Conclusion: We have investigated the feasibility of performing image fusion optimization on multiple contrast MRI images. This mechanism could help utilize multiple contrast MRI scans to potentially facilitate future MR-based treatment planning.« less

Authors:
; ; ;  [1]
  1. Duke University Medical Center, Durham, NC (United States)
Publication Date:
OSTI Identifier:
22649373
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; IMAGES; LIVER; NEOPLASMS; NMR IMAGING; OPTIMIZATION; PLANNING; RADIOTHERAPY

Citation Formats

Zhang, L, Yin, F, Liang, X, and Cai, J. SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning. United States: N. p., 2016. Web. doi:10.1118/1.4957027.
Zhang, L, Yin, F, Liang, X, & Cai, J. SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning. United States. doi:10.1118/1.4957027.
Zhang, L, Yin, F, Liang, X, and Cai, J. 2016. "SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning". United States. doi:10.1118/1.4957027.
@article{osti_22649373,
title = {SU-G-JeP2-07: Fusion Optimization of Multi-Contrast MRI Scans for MR-Based Treatment Planning},
author = {Zhang, L and Yin, F and Liang, X and Cai, J},
abstractNote = {Purpose: To develop an image fusion method using multiple contrast MRI scans for MR-based treatment planning. Methods: T1 weighted (T1-w), T2 weighted (T2-w) and diffusion weighted images (DWI) were acquired from liver cancer patient with breath-holding. Image fade correction and deformable image registration were performed using VelocityAI (Varian Medical Systems, CA). Registered images were normalized to mean voxel intensity for each image dataset. Contrast to noise ratio (CNR) between tumor and liver was quantified. Tumor area was defined as the GTV contoured by physicians. Normal liver area with equivalent dimension was used as background. Noise was defined by the standard deviation of voxel intensities in the same liver area. Linear weightings were applied to T1-w, T2-w and DWI images to generate composite image and CNR was calculated for each composite image. Optimization process were performed to achieve different clinical goals. Results: With a goal of maximizing tumor contrast, the composite image achieved a 7–12 fold increase in tumor CNR (142.8 vs. −2.3, 11.4 and 20.6 for T1-w, T2-w and DWI only, respectively), while anatomical details were largely invisible. With a weighting combination of 100%, −10% and −10%, respectively, tumor contrast was enhanced from −2.3 to −5.4, while the anatomical details were clear. With a weighting combination of 25%, 20% and 55%, balanced tumor contrast and anatomy was achieved. Conclusion: We have investigated the feasibility of performing image fusion optimization on multiple contrast MRI images. This mechanism could help utilize multiple contrast MRI scans to potentially facilitate future MR-based treatment planning.},
doi = {10.1118/1.4957027},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • 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: 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 EDmore » 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.« less
  • Purpose: Patient-specific distortions, particularly near tissue/air interfaces, require assessment and possible corrections for MRI-only radiation treatment planning (RTP). However, patients are dynamic due to changes in physiological status and motion during imaging sessions. This work investigated the need for dynamic patient-specific distortion corrections to support pelvis MR-only RTP. Methods: The pelvises of healthy volunteers were imaged at 1.0T, 1.5T, and 3.0T. Patient-specific distortion field maps were generated using a dual-echo gradient-recalled echo (GRE) sequence with B0 field maps obtained from the phase difference between the two echoes acquired at two timepoints: empty and full bladders. To quantify changes arising frommore » respiratory state, end-inhalation and end-expiration data were acquired. Distortion map differences were computed between the empty/full bladder and inhalation/expiration to characterize local changes. The normalized frequency distortion distributions in T2-weighted TSE images were characterized, particularly for simulated prostate planning target volumes (PTVs). Results: Changes in rectal and bowel air location were observed, likely due to changes in bladder filling. Within the PTVs, displacement differences (mean ± stdev, range) were −0.02 ± 0.02 mm (−0.13 to 0.07 mm) for 1.0T, −0.1 ± 0.2 mm (−0.92 to 0.74 mm) for 1.5T, and −0.20 ± 0.03 mm (−0.61 to 0.38 mm) for 3.0T. Local changes of ∼1 mm at the prostate-rectal interface were observed for an extreme case at 1.5T. For end-inhale and end-exhale scans at 3.0T, 99% of the voxels had Δx differences within ±0.25mm, thus the displacement differences due to respiratory state appear negligible in the pelvis. Conclusion: Our work suggests that transient bowel/rectal gas due to bladder filling may yield non-negligible patient-specific distortion differences near the prostate/rectal interface, whereas respiration had minimal effect. A temporal patient model for patient-specific distortion corrections may be advantageous for MR-only RTP, although further investigations in larger cohorts are needed to fully characterize distortion magnitude. The submitting institution has research agreements with Philips Healthcare. Research sponsored by a Henry Ford Health System Internal Mentored Grant.« less
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