Connection

Co-Authors

This is a "connection" page, showing publications co-authored by PETER A BALTER and LAURENCE EDWARD COURT.
Connection Strength

5.010
  1. Illustrated instructions for mechanical quality assurance of a medical linear accelerator. J Appl Clin Med Phys. 2018 May; 19(3):355-359.
    View in: PubMed
    Score: 0.620
  2. An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check. Int J Radiat Oncol Biol Phys. 2022 11 01; 114(3):516-528.
    View in: PubMed
    Score: 0.209
  3. Using Failure Mode and Effects Analysis to Evaluate Risk in the Clinical Adoption of Automated Contouring and Treatment Planning Tools. Pract Radiat Oncol. 2022 Jul-Aug; 12(4):e344-e353.
    View in: PubMed
    Score: 0.205
  4. Beam energy metrics for the acceptance and quality assurance of Halcyon linear accelerator. J Appl Clin Med Phys. 2021 Jul; 22(7):121-127.
    View in: PubMed
    Score: 0.194
  5. Evaluation of a multiview architecture for automatic vertebral labeling of palliative radiotherapy simulation CT images. Med Phys. 2020 Nov; 47(11):5592-5608.
    View in: PubMed
    Score: 0.185
  6. Automatic Verification of Beam Apertures for Cervical Cancer Radiation Therapy. Pract Radiat Oncol. 2020 Sep - Oct; 10(5):e415-e424.
    View in: PubMed
    Score: 0.181
  7. Acceptance and verification of the Halcyon-Eclipse linear accelerator-treatment planning system without 3D water scanning system. J Appl Clin Med Phys. 2019 Oct; 20(10):111-117.
    View in: PubMed
    Score: 0.173
  8. Dosimetric impact and detectability of multi-leaf collimator positioning errors on Varian Halcyon. J Appl Clin Med Phys. 2019 Aug; 20(8):47-55.
    View in: PubMed
    Score: 0.170
  9. Automated treatment planning of postmastectomy radiotherapy. Med Phys. 2019 Sep; 46(9):3767-3775.
    View in: PubMed
    Score: 0.170
  10. A risk assessment of automated treatment planning and recommendations for clinical deployment. Med Phys. 2019 Jun; 46(6):2567-2574.
    View in: PubMed
    Score: 0.168
  11. Technical Note: Density correction to improve CT number mapping in thoracic deformable image registration. Med Phys. 2019 May; 46(5):2330-2336.
    View in: PubMed
    Score: 0.167
  12. Fully Automatic Treatment Planning for External-Beam Radiation Therapy of Locally Advanced Cervical Cancer: A Tool for Low-Resource Clinics. J Glob Oncol. 2019 01; 5:1-9.
    View in: PubMed
    Score: 0.164
  13. Independent validation of machine performance check for the Halcyon and TrueBeam linacs for daily quality assurance. J Appl Clin Med Phys. 2018 Sep; 19(5):375-382.
    View in: PubMed
    Score: 0.159
  14. Interplay effect on a 6-MV flattening-filter-free linear accelerator with high dose rate and fast multi-leaf collimator motion treating breast and lung phantoms. Med Phys. 2018 Jun; 45(6):2369-2376.
    View in: PubMed
    Score: 0.157
  15. Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System. J Vis Exp. 2018 04 11; (134).
    View in: PubMed
    Score: 0.156
  16. Normal tissue doses from MV image-guided radiation therapy (IGRT) using orthogonal MV and MV-CBCT. J Appl Clin Med Phys. 2018 May; 19(3):52-57.
    View in: PubMed
    Score: 0.155
  17. Atlas ranking and selection for automatic segmentation of the esophagus from CT scans. Phys Med Biol. 2017 Nov 14; 62(23):9140-9158.
    View in: PubMed
    Score: 0.152
  18. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017 04 03; 7(1):588.
    View in: PubMed
    Score: 0.146
  19. Model for Estimating Power and Downtime Effects on Teletherapy Units in Low-Resource Settings. J Glob Oncol. 2017 Oct; 3(5):563-571.
    View in: PubMed
    Score: 0.143
  20. Reproducibility of patient setup in the seated treatment position: A novel treatment chair design. J Appl Clin Med Phys. 2017 Jan; 18(1):223-229.
    View in: PubMed
    Score: 0.143
  21. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med Phys. 2015 Dec; 42(12):6784-97.
    View in: PubMed
    Score: 0.133
  22. Upright cone beam CT imaging using the onboard imager. Med Phys. 2014 Jun; 41(6):061906.
    View in: PubMed
    Score: 0.120
  23. SU-E-T-184: LINAC Commissioning Measurements Utilizing the Cylindrical Arc Check Phantom. Med Phys. 2012 Jun; 39(6Part12):3745.
    View in: PubMed
    Score: 0.104
  24. Evaluation of dose variation to normal and critical structures for lung hypofractionated stereotactic body radiation therapy. Pract Radiat Oncol. 2012 Jul-Sep; 2(3):e15-e21.
    View in: PubMed
    Score: 0.102
  25. Comparison of Vendor-Pretrained and Custom-Trained Deep Learning Segmentation Models for Head-and-Neck, Breast, and Prostate Cancers. Diagnostics (Basel). 2024 Dec 18; 14(24).
    View in: PubMed
    Score: 0.062
  26. Deep learning-based automatic segmentation of cardiac substructures for lung cancers. Radiother Oncol. 2024 02; 191:110061.
    View in: PubMed
    Score: 0.058
  27. Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy. Int J Radiat Oncol Biol Phys. 2024 Jan 01; 118(1):231-241.
    View in: PubMed
    Score: 0.056
  28. Clinical implementation of automated treatment planning for whole-brain radiotherapy. J Appl Clin Med Phys. 2021 Sep; 22(9):94-102.
    View in: PubMed
    Score: 0.049
  29. Our Experience Leading a Large Medical Physics Practice During the COVID-19 Pandemic. Adv Radiat Oncol. 2021 Jul-Aug; 6(4):100683.
    View in: PubMed
    Score: 0.048
  30. Tissue-specific deformable image registration using a spatial-contextual filter. Comput Med Imaging Graph. 2021 03; 88:101849.
    View in: PubMed
    Score: 0.047
  31. Experience in commissioning the halcyon linac. Med Phys. 2019 Oct; 46(10):4304-4313.
    View in: PubMed
    Score: 0.043
  32. A snapshot of medical physics practice patterns. J Appl Clin Med Phys. 2018 Nov; 19(6):306-315.
    View in: PubMed
    Score: 0.040
  33. Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income Countries. J Glob Oncol. 2018 07; 4:1-11.
    View in: PubMed
    Score: 0.040
  34. A methodology to investigate the impact of image distortions on the radiation dose when using magnetic resonance images for planning. Phys Med Biol. 2018 04 05; 63(8):085005.
    View in: PubMed
    Score: 0.039
  35. Technical Note: Solving the "Chinese postman problem" for effective contour deformation. Med Phys. 2018 Feb; 45(2):767-772.
    View in: PubMed
    Score: 0.038
  36. An FMEA evaluation of intensity modulated radiation therapy dose delivery failures at tolerance criteria levels. Med Phys. 2017 Nov; 44(11):5575-5583.
    View in: PubMed
    Score: 0.038
  37. Erratum: "Modeling respiratory motion for reducing motion artifacts in 4D CT images" [Med. Phys. 40, 041716 (13pp.) (2013)]. Med Phys. 2015 Nov; 42(11):6768.
    View in: PubMed
    Score: 0.033
  38. Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion. Comput Med Imaging Graph. 2015 Mar; 40:30-8.
    View in: PubMed
    Score: 0.031
  39. Statistical modeling approach to quantitative analysis of interobserver variability in breast contouring. Int J Radiat Oncol Biol Phys. 2014 May 01; 89(1):214-21.
    View in: PubMed
    Score: 0.029
  40. Auto-segmentation of low-risk clinical target volume for head and neck radiation therapy. Pract Radiat Oncol. 2014 Jan-Feb; 4(1):e31-7.
    View in: PubMed
    Score: 0.028
  41. Modeling respiratory motion for reducing motion artifacts in 4D CT images. Med Phys. 2013 Apr; 40(4):041716.
    View in: PubMed
    Score: 0.028
  42. SU-E-J-52: Validation of 3D Structure Projection Onto 2D DRR in Commercial Treatment Planning Systems. Med Phys. 2012 Jun; 39(6Part6):3664.
    View in: PubMed
    Score: 0.026
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.