Automated air region delineation on MRI for synthetic CT creation.

Automatically and accurately separating air from other low signal regions (especially bone, liver, etc) in an MRI is difficult because these tissues produce similar MR intensities, resulting in errors in synthetic CT generation for MRI-based radiation therapy planning. This work aims to develop a technique to accurately and automatically determine air-regions for MR-guided adaptive radiation therapy. CT and MRI scans (T2-weighted) of phantoms with fabricated air-cavities and abdominal cancer patients were used to establish an MR intensity threshold for air delineation. From the phantom data, air/tissue boundaries in MRI were identified by CT-MRI registration. A formula relating the MRI intensities of air and surrounding materials was established to auto-threshold air-regions. The air-regions were further refined by using quantitative image texture features. A naive Bayesian classifier was trained using the extracted features with a leave-one-out cross validation technique to differentiate air from non-air voxels. The multi-step air auto-segmentation method was tested against the manually segmented air-regions. The dosimetry impacts of the air-segmentation methods were studied. Air-regions in the abdomen can be segmented on MRI within 1 mm accuracy using a multi-step auto-segmentation method as compared to manually delineated contours. The air delineation based on the MR threshold formula was improved using the MRI texture differences between air and tissues, as judged by the area under the receiver operating characteristic curve of 81% when two texture features (autocorrelation and contrast) were used. The performance increased to 82% with using three features (autocorrelation, sum-variance, and contrast). Dosimetric analysis showed no significant difference between the auto-segmentation and manual MR air delineation on commonly used dose volume parameters. The proposed techniques consisting of intensity-based auto-thresholding and image texture-based voxel classification can automatically and accurately segment air-regions on MRI, allowing synthetic CT to be generated quickly and precisely for MR-guided adaptive radiation therapy.

A Technique to Rapidly Generate Synthetic Computed Tomography for Magnetic Resonance Imaging-Guided Online Adaptive Replanning: An Exploratory Study.

PURPOSE: To develop an automatic, accurate, atlas-based technique for synthetic computed tomography (sCT) generation to be used for online adaptive replanning during magnetic resonance imaging (MRI)-guided radiation therapy (RT). METHODS AND MATERIALS: The proposed method uses deformable image registration (DIR) of daily MRI and reference computed tomography (CT) with additional corrections to maintain bone rigidity and to transfer random air regions by thresholding. The DIR is performed with constraints on the bony structures using a special algorithm of ADMIRE (Elekta). The air regions are delineated from low-signal regions on the daily MRI and forced to air density. The bone regions in the MRI (already determined from the CT) are separated from the air regions because both bone and air have low signal density in MRI. All these steps are automated. The generated sCT is compared with reference CT and the alternative voxel-based CT (bCT) for 4 extracranial sites (head and neck, thorax, abdomen, pelvis) in terms of mean absolute error (MAE), gamma analysis of 3-dimensional doses, and dose volume histogram parameters. RESULTS: Both MAE and dosimetric analysis results were favorable for the proposed sCT generation method. The average MAE for the sCT/bCT were 25.5/66.7, 25.9/65.3, 24.8/44.2 and 16.6/47.7 for head and neck, thorax, abdomen, and pelvis, respectively, and the gamma analysis (1.5%, 2 mm) yielded 98.7/97.1, 99.1/93.9, 99.5/99.4, 99.7/99.4, respectively, for those sites. CONCLUSIONS: The proposed method generates equal or more accurate sCT than those from the bulk density assignment, without the need for multiple MRI sequences. This method can be fully automated and applicable for online adaptive replanning.

The Importance of CD44 as a Stem Cell Biomarker and Therapeutic Target in Cancer.

CD44 is a cell surface HA-binding glycoprotein that is overexpressed to some extent by almost all tumors of epithelial origin and plays an important role in tumor initiation and metastasis. CD44 is a compelling marker for cancer stem cells of many solid malignancies. In addition, interaction of HA and CD44 promotes EGFR-mediated pathways, consequently leading to tumor cell growth, tumor cell migration, and chemotherapy resistance in solid cancers. Accumulating evidence indicates that major HA-CD44 signaling pathways involve a specific variant of CD44 isoforms; however, the particular variant almost certainly depends on the type of tumor cell and the stage of the cancer progression. Research to date suggests use of monoclonal antibodies against different CD44 variant isoforms and targeted inhibition of HA/CD44-mediated signaling combined with conventional radio/chemotherapy may be the most favorable therapeutic strategy for future treatments of advanced stage malignancies. Thus, this paper briefly focuses on the association of the major CD44 variant isoforms in cancer progression, the role of HA-CD44 interaction in oncogenic pathways, and strategies to target CD44-overexpressed tumor cells.