Sean Frigo headshot

Sean Frigo, PhD

Associate Professor (CHS)

Department of Human Oncology

My primary focus is on clinical care, and within that area, the implementation and management of treatment planning systems. We are leveraging new software technologies to bring all relevant patient information and data into one environment in order to facilitate treatment plan design. This not only applies to calculating treatment dose, but also supporting the development of calculating other quantities, such as treatment response. To support research initiatives in these areas, we have established a separate dedicated treatment planning environment that provides a sandbox in order to develop and test new approaches to treatment planning.

I engage in many informal teaching activities, from mentoring medical physics residents to serving as guest lecturer in medical physics courses. I also work with undergraduates in the Biomedical Engineering Department on medical equipment development projects. Given my background working in the medical device industry, I am actively involved in ASTRO professional organization committees and sub-committees.


Alexander von Humboldt Fellow, Technical University of Munich, X-Ray Photodissociation (1997)

PhD, University of Wisconsin–Madison, X-Ray Photodissociation (1994)

BS, University of Wisconsin–Platteville, Chemistry, Mathematics, Physics (1988)

Academic Appointments

Associate Professor (CHS), Human Oncology (2022)

Assistant Professor (CHS), Human Oncology (2014)

Clinical Adjunct Professor, Human Oncology (2013)

Clinical Adjunct Professor, Medical Physics (2007)

Selected Honors and Awards

Alexander von Humboldt Fellowship (1994)

Society of Physics Students Scholarship (1987)

Chicago Drug and Chemical Association Scholarship (1986)

Leo E. Boebel Memorial Scholarship (1984–1987)

Lawson Products Company Scholarship (1983)

Boards, Advisory Committees and Professional Organizations

ASTRO Corporate Relations Committee (2017)

ASTRO Congressional Relations Sub-committee (2017)

Dr. Sean Frigo is a medical physicist who is primarily focused on clinical care, and within that area, the implementation and management of treatment planning systems. He leverages new software technologies to bring all relevant patient information and data into one environment in order to facilitate treatment plan design.

The best treatments are ones that model the right amount of dose in the right place at the right time and accurately predict the effects of those treatments.

Activities that advance treatment planning call for a dedicated environment that closely mimics that used for patient care, yet can tolerate being broken. My research activities are aimed at providing such a resource in order for us to develop new planning techniques and patient treatment models.

Clinical Treatment Planning System

Our problem definition is inspired by asking what we can do better for our current patients and what we would want to do if we just had the right tools on hand. Thus, significant effort is being spent on implementing a new clinical treatment planning system for patient care. This will lay the groundwork for research in adaptive therapy and improved patient modeling.

An important effort going forward is the development of medical device management techniques to manage the clinical treatment planning system. By applying modern concepts of change and state management, we are able to demonstrate system stability and calculation accuracy as we upgrade software modules or change configurations.

Non-clinical Treatment Planning System

We are implementing a second treatment planning system that closely mimics the one we are using for patient care. Within this dedicated system, we can rapidly prototype and test new planning modules and techniques. It is important that the planning system for patient care be tightly controlled and limited changes be thoroughly tested prior to use. Thus it is important to do developmental work in a separate system that can tolerate being broken and subsequent down time, but also be subject to less rigorous controls and testing.

We intend to utilize this sandbox environment to improve how we perform adaptive planning. Through the use of CT, MR and PT imaging, we are able to build three-dimensional maps of a patient’s anatomy, identifying organs and tissues. We also can create corresponding three-dimensional maps of radiation treatment dose, showing where the radiation energy is deposited. We need to extend these capabilities to be able to do so as a patient’s anatomy changes over time, either during a single treatment course in the time-frame of weeks or during multiple courses over years. We need software and data frameworks to support this time element.

Planning unification

We employ three distinct external beam delivery technologies here at the University of Wisconsin (TrueBeam, Tomotherapy, ViewRay) in addition to brachytherapy. Each technology we use to deliver radiation dose currently has its own dedicated treatment planning system. However, each one of these systems is a data island.

Often, it is best to be able to use different delivery technologies in a single treatment course, such as boosting a prostate with HDR brachytherapy after an initial external beam treatment. It takes great manual effort to copy data between islands in order to create a plan that reflects the entire treatment course and to optimize the different plans over the treatment course. By supporting all treatment modalities within one planning environment, we can calculate and combine dose easily in order to explore the best treatment options for our patients.

  • IMRT QA result prediction via MLC transmission decomposition Journal of applied clinical medical physics
    Stasko JT, Ferris WS, Adam DP, Culberson WS, Frigo SP
    2023 Aug;24(8):e13990. doi: 10.1002/acm2.13990. Epub 2023 Apr 8.
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      BACKGROUND: Quality assurance measurement of IMRT/VMAT treatment plans is resource intensive, and other more efficient methods to achieve the same confidence are desirable.

      PURPOSE: We aimed to analyze treatment plans in the context of the treatment planning systems that created them, in order to predict which ones will fail a standard quality assurance measurement. To do so, we sought to create a tool external to the treatment planning system that could analyze a set of MLC positions and provide information that could be used to calculate various evaluation metrics.

      METHODS: The tool was created in Python to read in DICOM plan files and determine the beam fluence fraction incident on each of seven different zones, each classified based on the RayStation MLC model. The fractions, termed grid point fractions, were validated by analyzing simple test plans. The average grid point fractions, over all control points for 46 plans were then computed. These values were then compared with gamma analysis pass percentages and median dose differences to determine if any significant correlations existed.

      RESULTS: Significant correlation was found between the grid point fraction metrics and median dose differences, but not with gamma analysis pass percentages. Correlations were positive or negative, suggesting differing model parameter value sensitivities, as well as potential insight into the treatment planning system dose model.

      CONCLUSIONS: By decomposing MLC control points into different transmission zones, it is possible to create a metric that predicts whether the analyzed plan will pass a quality assurance measurement from a dose calculation accuracy standpoint. The tool and metrics developed in this work have potential applications in comparing clinical beam models or identifying their weak points. Implementing the tool within a treatment planning system would also provide more potential plan optimization parameters.

      PMID:37031363 | PMC:PMC10402675 | DOI:10.1002/acm2.13990

      View details for PubMedID 37031363
  • Static MLC transmission simulation using two-dimensional ray tracing Journal of applied clinical medical physics
    Adam DP, Bednarz BP, Frigo SP
    2022 Aug;23(8):e13646. doi: 10.1002/acm2.13646. Epub 2022 May 20.
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      PURPOSE: We investigated the hypothesis that the transmission function of rounded end linearly traveling multileaf collimators (MLCs) is constant with position. This assumption is made by some MLC models used in clinical treatment planning systems (TPSs) and in the Varian MLC calibration convention. If not constant, this would have implications for treatment plan QA results.

      METHODS: A two-dimensional ray-tracing tool to generate transmission curves as a function of leaf position was created and validated. The curves for clinically available leaf tip positions (-20 to 20 cm) were analyzed to determine the location of the beam edge (half-attenuation X-ray [XR]) location, the beam edge broadening (BEB, 80%-20% width), as well as the leaf tip zone width. More generalized scenarios were then simulated to elucidate trends as a function of leaf tip radius.

      RESULTS: In the analysis of the Varian high-definition MLC, two regions were identified: a quasi-static inner region centered about central axis (CAX), and an outer one, in which large deviations were observed. A phenomenon was identified where the half-attenuation ray position, relative to that of the tip or tangential ray, increases dramatically at definitive points from CAX. Similar behavior is seen for BEB. An analysis shows that as the leaf radius parameter value is made smaller, the size of the quasi-static region is greater (and vice versa).

      CONCLUSION: The MLC transmission curve properties determined by this study have implications both for MLC position calibrations and modeling within TPSs. Two-dimensional ray tracing can be utilized to identify where simple behaviors hold, and where they deviate. These results can help clinical physicists engage with vendors to improve MLC models, subsequent fluence calculations, and hence dose calculation accuracy.

      PMID:35596533 | PMC:PMC9359033 | DOI:10.1002/acm2.13646

      View details for PubMedID 35596533
  • Interinstitutional beam model portability study in a mixed vendor environment Journal of applied clinical medical physics
    Frigo SP, Ohrt J, Suh Y, Balter P
    2021 Dec;22(12):37-50. doi: 10.1002/acm2.13445. Epub 2021 Oct 13.
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      A 6 MV flattened beam model for a Varian TrueBeamSTx c-arm treatment delivery system in RayStation, developed and validated at one institution, was implemented and validated at another institution. The only parameter value adjustments were to accommodate machine output at the second institution. Validation followed MPPG 5.a. recommendations, with particular attention paid to IMRT and VMAT deliveries. With this minimal adjustment, the model passed validation across a broad spectrum of treatment plans, measurement devices, and staff who created the test plans and executed the measurements. This work demonstrates the possibility of using a single template model in the same treatment planning system with matched machines in a mixed vendor environment.

      PMID:34643323 | PMC:PMC8664150 | DOI:10.1002/acm2.13445

      View details for PubMedID 34643323
  • Evaluation of candidate template beam models for a matched TrueBeam treatment delivery system Journal of applied clinical medical physics
    Hansen JB, Frigo SP
    2021 Jun;22(6):92-103. doi: 10.1002/acm2.13278. Epub 2021 May 25.
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      PURPOSE: To explore candidate RayStation beam models to serve as a class-specific template for a TrueBeam treatment delivery system.

      METHODS: Established validation techniques were used to evaluate three photon beam models: a clinically optimized model from the authors' institution, the built-in RayStation template, and a hybrid consisting of the RayStation template except substituting average MLC parameter values from a recent IROC survey. Comparisons were made for output factors, dose profiles from open fields, as well as representative VMAT test plans.

      RESULTS: For jaw-defined output factors, each beam model was within 1.6% of expected published values. Similarly, the majority (57-66%) of jaw-defined dose curves from each model had a gamma pass rate >95% (2% / 3 mm, 20% threshold) when compared to TrueBeam representative beam data. For dose curves from MPPG 5.a MLC-defined fields, average gamma pass rates (1% / 1 mm, 20% threshold) were 92.9%, 85.1%, and 86.0% for the clinical, template, and hybrid models, respectively. For VMAT test plans measured with a diode array detector, median dose differences were 0.6%, 1.3%, and 1.1% for the clinical, template, and hybrid models, respectively. For in-phantom ionization chamber measurements with the same VMAT test plans, the average percent difference was -0.3%, -1.4%, and -1.0% for the clinical, template, and hybrid models, respectively.

      CONCLUSION: Beam model templates taken from the vendor and aggregate results within the community were both reasonable starting points, but neither approach was as optimal as a clinically tuned model, the latter producing better agreement with all validation measurements. Given these results, the clinically optimized model represents a better candidate as a consensus template. This can benefit the community by reducing commissioning time and improving dose calculation accuracy for matched TrueBeam treatment delivery systems.

      PMID:34036726 | PMC:PMC8200503 | DOI:10.1002/acm2.13278

      View details for PubMedID 34036726
  • Characteristics and limitations of a secondary dose check software for VMAT plan calculation Journal of applied clinical medical physics
    Shepard AJ, Frigo SP
    2021 Mar;22(3):216-223. doi: 10.1002/acm2.13206. Epub 2021 Mar 5.
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      PURPOSE: To assess the implementation, accuracy, and validity of the dosimetric leaf gap correction (DLGC) in Mobius3D VMAT plan calculations.

      METHODS: The optimal Mobius3D DLGC was determined for both a TrueBeam with a Millennium multi-leaf collimator and a TrueBeamSTx with a high-definition multi-leaf collimator. By analyzing a broad series of seven VMAT plans and comparing the calculated to the measured dose delivered to a cylindrical phantom, optimal DLGC values were determined by minimizing the dose difference for both the collection of all plans, as well as for each plan individually. The effects of plan removal from the optimization of the collective DLGC value, as well as plan-specific DLGC values, were explored to determine the impact of plan suite design on the final DLGC determination.

      RESULTS: Optimal collective DLGC values across all energies were between -0.71 and 0.89 mm for the TrueBeam, and between 0.35 and 1.85 mm for the TrueBeamSTx. The dose differences ranged between -6.1% and 2.6% across all plans when the optimal collective DLGC values were used. On a per-plan basis, the plan-specific optimal DLGC values ranged from -4.36 to 2.35 mm for the TrueBeam, and between -1.83 and 2.62 mm for the TrueBeamSTx. Comparing the plan-specific optimal DLGC to the average absolute leaf position from the central axis for each plan, a negative correlation was observed.

      CONCLUSIONS: The optimal DLGC determination depends on the plans investigated, making it essential for users to utilize a suite of test plans that encompasses the full range of expected clinical plans when determining the optimal DLGC value. Validation of the secondary dose calculation should always be based on measurements, and not a comparison with the primary TPS. Varying disagreement with measurements across plans for a single DLGC value indicates potential limitations in the Mobius3D MLC model.

      PMID:33666339 | PMC:PMC7984465 | DOI:10.1002/acm2.13206

      View details for PubMedID 33666339
  • Implementation of the validation testing in MPPG 5.a "Commissioning and QA of treatment planning dose calculations-megavoltage photon and electron beams" Journal of applied clinical medical physics
    Jacqmin DJ, Bredfeldt JS, Frigo SP, Smilowitz JB
    2017 Jan;18(1):115-127. doi: 10.1002/acm2.12015. Epub 2016 Dec 5.
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      The AAPM Medical Physics Practice Guideline (MPPG) 5.a provides concise guidance on the commissioning and QA of beam modeling and dose calculation in radiotherapy treatment planning systems. This work discusses the implementation of the validation testing recommended in MPPG 5.a at two institutions. The two institutions worked collaboratively to create a common set of treatment fields and analysis tools to deliver and analyze the validation tests. This included the development of a novel, open-source software tool to compare scanning water tank measurements to 3D DICOM-RT Dose distributions. Dose calculation algorithms in both Pinnacle and Eclipse were tested with MPPG 5.a to validate the modeling of Varian TrueBeam linear accelerators. The validation process resulted in more than 200 water tank scans and more than 50 point measurements per institution, each of which was compared to a dose calculation from the institution's treatment planning system (TPS). Overall, the validation testing recommended in MPPG 5.a took approximately 79 person-hours for a machine with four photon and five electron energies for a single TPS. Of the 79 person-hours, 26 person-hours required time on the machine, and the remainder involved preparation and analysis. The basic photon, electron, and heterogeneity correction tests were evaluated with the tolerances in MPPG 5.a, and the tolerances were met for all tests. The MPPG 5.a evaluation criteria were used to assess the small field and IMRT/VMAT validation tests. Both institutions found the use of MPPG 5.a to be a valuable resource during the commissioning process. The validation testing in MPPG 5.a showed the strengths and limitations of the TPS models. In addition, the data collected during the validation testing is useful for routine QA of the TPS, validation of software upgrades, and commissioning of new algorithms.

      PMID:28291929 | PMC:PMC5689890 | DOI:10.1002/acm2.12015

      View details for PubMedID 28291929

Contact Information

Sean Frigo, PhD

600 Highland Ave,
Madison, WI 53792