Therefore, our results demonstrate, that atlas based auto-contouring of the pelvic nodes could both help increase consistency for treatment volumes in high risk patients between different cancer centers as well as decrease time related to treatment planning for radiation oncologist. Author Disclosure: R.B. Patel: A. Employee; University Hospitals at Case Western Reserve University. T. Bryan: A. Employee; University Hospitals at Case Western Reserve University. D. Kaminsky: A. Employee; Uni- versity Hospitals at Case Western Reserve University. S. Pirozzi: A. Employee; MIM Software. A. Nelson: A. Employee; MIM Software. J. Piper: A. Employee; MIM Software. M. Lu: A. Employee; MIM Soft- ware. M. Machtay: A. Employee; University Hospitals at Case Western Reserve University. R.J. Ellis: A. Employee; University Hospitals at Case Western Reserve University. 159 Can 4D-CT Ventilation Imaging Replace Technegas V-SPECT for Functionally Adaptive Radiation Therapy? First Results F. Hegi-Johnson, 1 , 2 J. Kipritidis, 2,3 J. Barber, 3 K. West, 3 K. Unicomb, 3 C. Bui, 4 R. Yegiaian-Alvandi, 3 and P. Keall 2 ; 1 Central Coast Cancer Centre, Gosford, Australia, 2 Radiation Physics Laboratory, University of Sydney, Sydney, Australia, 3 Nepean Cancer Care Centre, Sydney, Australia, 4 Department of Nuclear Medicine, Nepean Hospital, Sydney, Australia Purpose/Objective(s): Four-dimensional-CT ventilation imaging (CT-VI) is a novel method for imaging regional air volume changes in the lung. Previous attempts at comparing CT-VI and DTPAV-SPECT have shown poor results due to “clumping” of DTPA in large airways. Technegas is a smaller molecule than DTPA, and disperses throughout normal lung with less clumping and no washout. In this work we perform the first com- parison of Technegas V-SPECT and CT-VI. We test the impact of two different CT ventilation metrics, and two different methods (algorithm- or clinician-based) for defining functional lung. We hypothesize that CT-VI will provide an alternative to V-SPECT, providing a simple method for functional lung avoidance radiation therapy. Materials/Methods: Re-treatment Technegas V-SPECT and planning 4D- CT scans were acquired for 5 patients with early lung cancer. V-SPECT and 4D-CT scans were both acquired with patients in the treatment planning position. CT-VI images were then produced by applying B-spline deformable image registration between the maximal exhale and inhale phases of the 4D-CT. Two ventilation metrics were computed, one based on Hounsfield-Unit (HU) changes and the other based on the Jacobian determinant of deformation. V-SPECTand CT-VI images were fused to the average 4D-CTin Velocity AI. Two methods of thresholding were inves- tigated: (1) Clinician based: Three 10 mm 3 regions of normal contralateral lung were selected on the average 4D-CT, and used to obtain minimum and mean ventilation values for normal lung in each of the V-SPECTs and CT- VIs. A threshold midway between the minimum and mean uptake values defined lung with good function. (2) Algorithm based: The threshold be- tween good and low functioning lung was set as the 20th percentile value of functional lung. This was calculated separately for each V-SPECT and CT-VI image, within the lung volume segmented from 4D-CT. The clinician and algorithm based thresholds were then applied to obtain the good-functioning lung in each V-SPECT, Jacobian and HU CT-VI. We then computed the Dice similarity index d, to assess the correlation be- tween well-functioning lung in V-SPECT and corresponding CT-VIs. Results: For the clinician-based method, HU CT-VIs correlated better than Jacobian images, with average d Z 0.71  0.19 and 0.64  0.11, respectively. The algorithm-based method significantly reduced patient-to- patient variation, but with HU CT-VIs performing slightly worse than Jacobian images (average d Z 0.74  0.03 and 0.81  0.03, respectively). Conclusions: This is the first comparison of 4D-CT ventilation imaging and Technegas V-SPECT. CT-VIs can exhibit moderate-to-good agreement with V-SPECT, and interestingly, this varies with functional lung thresh- olding method as well as ventilation metric. These results are promising, and this study has demonstrated that the thresholding method is an important factor requiring investigation. Author Disclosure: F. Hegi-Johnson: A. Employee; Central Coast Local Health District, Nepean Blue Mountains Local Health District. F. Hono- raria; Astra Zeneca Speaker’s Honoraria. S. Leadership; IAEA RTC 6065 National Project Coordinator, Central Coast Cancer Centre, Research Committee. J. Kipritidis: A. Employee; University of Sydney. E. Research Grant; Cancer Institute NSW. J. Barber: A. Employee; Nepean Blue Mountains Local Health District. K. West: A. Employee; Nepean Blue Mountains Local Health District. K. Unicomb: A. Employee; Nepean Blue Mountains Local Health District. C. Bui: A. Employee; Nepean Blue Mountains Local Health District. R. Yegiaian-Alvandi: A. Employee; Western Sydney Local Health District. P. Keall: A. Employee; University of Sydney. E. Research Grant; Australian Government, Cancer Australia, US NIH, Varian, Phillips. O. Partnership; Cancer Research Innovations. Q. Patent/License Fee/Copyright; Varian, Standard Imaging. S. Leadership; ASTRO ETC, AAPM various committees, RNSH boost. 160 Intravoxel Incoherent Motion Magnetic Resonance Imaging of Oropharyngeal Cancer in Response to Chemoradiation Therapy Y. Ding, C.D. Fuller, A.S.R. Mohamed, S.J. Frank, D.I. Rosenthal, R. Colen, and J.D. Hazle; University of Texas MD Anderson Cancer Center, Houston, TX Purpose/Objective(s): To investigate the utility of intravoxel incoherent motion (IVIM) MRI as a response indictor in human receiving chemo- radiation therapy for oropharyngeal cancer. Materials/Methods: Eight male patients with histologically documented stage II/III squamous cell carcinoma of the oropharynx were included in this prospective study. IVIM-MRI was carried out at a 3.0-T GE MRI scanner using laterally placed 6-element flex coils. Patients were scanned twice; the first scan was at baseline prior to the start of radiation therapy (RT) and the second scan was at mid-treatment after delivery 30-55 Gy of RT. All patients were scanned in supine position with the same RT immobilization devices including individualized thermoplastic head and shoulder mask, customized mold, and dental stent to reduce motion ar- tifacts and to improve image registration in longitudinal scans. IVIM parameters (D, pure diffusion coefficient; f, perfusion fraction; D*, pseudodiffusion coefficient) were calculated on a pixel-by-pixel basis using a bi-exponential model implemented within ImageJ with ten b- values ranging from 0-800 s/mm 2 . A fitting threshold of R2 > 0.5 was applied to all parametric maps. Based on post gadolinium T1-weighted images, tumors were contoured on IVIM maps for the purpose of comparing changes of IVIM parameters with tumor response. Paired- samples Wilcoxon signed rank test was used to compare IVIM param- eters for assessment of treatment response. Results: The mean IVIM parameters in pre-RT tumors were: D Z 8.94  1.67 ( 10-4 mm 2 /s), f Z 0.23  0.11, D* Z 20.52  9.71 ( 10-3 mm 2 /s); while those in mid-RT tumors were: D Z 15.43  3.68 ( 10- 4 mm 2 /s), f Z 0.34  0.20, D* Z 30.63  8.47 ( 10-3 mm 2 /s). D and f were statistically significantly higher in mid-RT tumors (P < 0.005 and P < 0.02, respectively), while changes of D* in tumor tissues during RT were considered to be not significant at this sample size (P < 0.053). Conclusions: The preliminary results of this study show IVIM parameters change during RT. In particular, D and f are robust, useful clinical markers with a high level of confidence. On the other hand, D* also show potential feasibility in assessing treatment response. We continue to accrue study patients and to collect long-term clinical outcomes to corroborate the reproducibility and clinical significance of these pilot findings. Author Disclosure: Y. Ding: A. Employee; MD Anderson Cancer Center. C.D. Fuller: A. Employee; MD Anderson Cancer Center. A.S.R. Mohamed: A. Employee; MD Anderson Cancer Center. S.J. Frank: A. Employee; MD Anderson Cancer Center. D.I. Rosenthal: A. Employee; UT MD Anderson Cancer Center. R. Colen: A. Employee; UT MD Anderson Cancer Center. J.D. Hazle: A. Employee; MD Anderson Cancer Center. Volume 90  Number 1S  Supplement 2014 Oral Scientific Sessions S75