Jennie Crosby image

Jennie Crosby, PhD

Assistant Professor (CHS)

Department of Human Oncology

2022 Physics Residency Alumna

My clinical work focuses on adaptive and MR-guided radiotherapy through my work with the ViewRay MRIdian system. This system gives us the opportunity, via MR imaging, to adapt the patient’s radiation plan to their anatomy on the day of treatment and enables us to visualize the anatomy throughout treatment. In addition to my clinical work on the ViewRay system, I have a strong interest in increasing automation throughout the clinic to streamline workflows. My research has primarily focused on patient safety and quality management, including analysis of trends and investigation of workflow inefficiencies.

I also participate in the educational mission of the department. I teach lectures for our MD resident physics class, as well as lectures for our radiation therapy students. In addition to lectures, I have conducted labs for the radiation therapy students, giving them the opportunity to get hands on experience with physics equipment and learn more about what physicists do to support the clinic.

Education

Residency, University of Wisconsin–Madison, Therapeutic Medical Physics (2022)

PhD, University of Chicago, Medical Physics (2020)

BS, University of Wisconsin–Madison, Nuclear Engineering (2015)

Academic Appointments

Assistant Professor , Department of Human Oncology (2022)

Selected Honors and Awards

Lawrence H. Lanzl Medical Physics Graduate Fellowship Award – University of Chicago (2019)

American Association of Physicists in Medicine Expanding Horizons Travel Grant (2018)

Boards, Advisory Committees and Professional Organizations

American Association of Physicists in Medicine (AAPM) Member (2017 to Pres.)

Society for Imaging Informatics in Medicine (SIIM) Member (2019 to Pres.)

Research Focus

MR-Guided and Adaptive Radiotherapy, Patient Safety and Quality Management, Automation

  • Technical note: Characterization of a multi-point scintillation dosimetry research platform for a low-field MR-Linac Medical physics
    Crosby J, Ruff C, Gregg K, Turcotte J, Glide-Hurst C
    2024 Jun 6. doi: 10.1002/mp.17192. Online ahead of print.
    • More

      BACKGROUND: MRI-guided radiation therapy (MRgRT) requires unique quality assurance equipment to address MR-compatibility needs, minimize electron return effect, handle complex dose distributions, and evaluate real-time dosimetry for gating. Plastic scintillation detectors (PSDs) are an attractive option to address these needs.

      PURPOSE: To perform a comprehensive characterization of a multi-probe PSD system in a low-field 0.35 T MR-linac, including detector response assessment and gating performance.

      METHODS: A four-channel PSD system (HYPERSCINT RP-200) was assembled. A single channel was used to evaluate repeatability, percent depth dose (PDD), detector response as a function of orientation with respect to the magnetic field, and intersession variability. All four channels were used to evaluate repeatability, linearity, and output factors. The four PSDs were integrated into an MR-compatible motion phantom at isocenter and in gradient regions. Experiments were conducted to evaluate gating performance and tracking efficacy.

      RESULTS: For repeatability, the maximum standard deviation of repeated measurements was 0.13% (single PSD). Comparing the PSD to reference data, PDD had a maximum difference of 1.12% (10 cm depth, 6.64 × 6.64 cm2). Percent differences for rotated detector setups were negligible (< 0.3%). All four PSDs demonstrated linear response over 10-1000 MU delivered and the maximum percent difference between the baseline and measured output factors was 0.78% (2.49 × 2.49 cm2). Gating experiments had 400 cGy delivered to isocenter with < 0.8 cGy variation for central axis measures and < 0.7 cGy for the gradient sampled region. Real-time dosimetry measurements captured spurious beam-on incidents that correlated to tracking algorithm inaccuracies and highlighted gating parameter impact on delivery efficiency.

      CONCLUSIONS: Characterization of the multi-point PSD dosimetry system in a 0.35 T MR-linac demonstrated reliability in a low-field MR-Linac setting, with high repeatability, linearity, small intersession variability, and similarity to baseline data for PDD and output factors. Time-resolved, multi-point dosimetry also showed considerable promise for gated MR-Linac applications.

      PMID:38843532 | DOI:10.1002/mp.17192


      View details for PubMedID 38843532
  • Transcatheter Arterial Chemoembolization Imaging Features in MR-Linac Radiation Therapy Planning for the Liver Cureus
    Crosby J, Bassetti MF, Hurst NJ, Kruser T, Glide-Hurst CK
    2023 Dec 13;15(12):e50459. doi: 10.7759/cureus.50459. eCollection 2023 Dec.
    • More

      For MR-guided radiation therapy treatment planning, an MRI and CT of the intended treatment site are typically acquired. Patients' prior treatments or procedures can cause image artifacts in one or both scans, which may impact treatment planning or the radiation dose calculation. In this case report, a patient with several previous transcatheter arterial chemoembolization (TACE) procedures was planned for radiation therapy on a low-field MR-linac, and the impact of residual iodinated oil on the radiation dose calculation and MR-guided adaptive workflow was evaluated.

      PMID:38222202 | PMC:PMC10784766 | DOI:10.7759/cureus.50459


      View details for PubMedID 38222202
  • Experimental determination of magnetic field quality conversion factors for eleven ionization chambers in 1.5 T and 0.35 T MR-linac systems Medical physics
    Orlando N, Crosby J, Glide-Hurst C, Culberson W, Keller B, Sarfehnia A
    2024 Apr;51(4):2998-3009. doi: 10.1002/mp.16858. Epub 2023 Dec 7.
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      BACKGROUND: The static magnetic field present in magnetic resonance (MR)-guided radiotherapy systems can influence dose deposition and charged particle collection in air-filled ionization chambers. Thus, accurately quantifying the effect of the magnetic field on ionization chamber response is critical for output calibration. Formalisms for reference dosimetry in a magnetic field have been proposed, whereby a magnetic field quality conversion factor kB,Q is defined to account for the combined effects of the magnetic field on the radiation detector. Determination of kB,Q in the literature has focused on Monte Carlo simulation studies, with experimental validation limited to only a few ionization chamber models.

      PURPOSE: The purpose of this study is to experimentally measure kB,Q for 11 ionization chamber models in two commercially available MR-guided radiotherapy systems: Elekta Unity and ViewRay MRIdian.

      METHODS: Eleven ionization chamber models were characterized in this study: Exradin A12, A12S, A28, and A26, PTW T31010, T31021, and T31022, and IBA FC23-C, CC25, CC13, and CC08. The experimental method to measure kB,Q utilized cross-calibration against a reference Exradin A1SL chamber. Absorbed dose to water was measured for the reference A1SL chamber positioned parallel to the magnetic field with its centroid placed at the machine isocenter at a depth of 10 cm in water for a 10 × 10 cm2 field size at that depth. Output was subsequently measured with the test chamber at the same point of measurement. kB,Q for the test chamber was computed as the ratio of reference dose to test chamber output, with this procedure repeated for each chamber in each MR-guided radiotherapy system. For the high-field 1.5 T Elekta Unity system, the dependence of kB,Q on the chamber orientation relative to the magnetic field was quantified by rotating the chamber about the machine isocenter.

      RESULTS: Measured kB,Q values for our test dataset of ionization chamber models ranged from 0.991 to 1.002, and 0.995 to 1.004 for the Elekta Unity and ViewRay MRIdian, respectively, with kB,Q tending to increase as the chamber sensitive volume increased. Measured kB,Q values largely agreed within uncertainty to published Monte Carlo simulation data and available experimental data. kB,Q deviation from unity was minimized for ionization chamber orientation parallel or antiparallel to the magnetic field, with increased deviations observed at perpendicular orientations. Overall (k = 1) uncertainty in the experimental determination of the magnetic field quality conversion factor, kB,Q was 0.71% and 0.72% for the Elekta Unity and ViewRay MRIdian systems, respectively.

      CONCLUSIONS: For a high-field MR-linac, the characterization of ionization chamber performance as angular orientation varied relative to the magnetic field confirmed that the ideal orientation for output calibration is parallel. For most of these chamber models, this study represents the first experimental characterization of chamber performance in clinical MR-linac beams. This is a critical step toward accurate output calibration for MR-guided radiotherapy systems and the measured kB,Q values will be an important reference data source for forthcoming MR-linac reference dosimetry protocols.

      PMID:38060696 | DOI:10.1002/mp.16858


      View details for PubMedID 38060696
  • The impact of COVID-19 on a high-volume incident learning system: A retrospective analysis Journal of applied clinical medical physics
    Jacqmin DJ, Crosby SM
    2022 Jul;23(7):e13653. doi: 10.1002/acm2.13653. Epub 2022 May 26.
    • More

      PURPOSE: The purpose of this work was to assess how the coronavirus disease 2019 (COVID-19) pandemic impacted our incident learning system data and communicate the impact of a major exogenous event on radiation oncology clinical practice.

      METHODS: Trends in our electronic incident reporting system were analyzed to ascertain the impact of the COVID-19 pandemic, including any direct clinical changes. Incident reports submitted in the 18 months prior to the pandemic (September 14, 2018 to March 13, 2020) and reports submitted during the first 18 months of the pandemic (March 14, 2020 to September 13, 2021) were compared. The incident reports include several data elements that were evaluated for trends between the two time periods, and statistical analysis was performed to compare the proportions of reports.

      RESULTS: In the 18 months prior to COVID-19, 192 reports were submitted per 1000 planning tasks (n = 832 total). In the first 18 months of the pandemic, 147 reports per 1000 planning tasks were submitted (n = 601 total), a decrease of 23.4%. Statistical analysis revealed that there were no significant changes among the data elements between the pre- and during COVID-19 time periods. An analysis of the free-text narratives in the reports found that phrases related to pretreatment imaging were common before COVID-19 but not during. Conversely, phrases related to intravenous contrast, consent for computed tomography, and adaptive radiotherapy became common during COVID-19.

      CONCLUSIONS: The data elements captured by our incident learning system were stable after the onset of the COVID-19 pandemic, with no statistically significant findings after correction for multiple comparisons. A trend toward fewer reports submitted for low-risk issues was observed. The methods used in the work can be generalized to events with a large-scale impact on the clinic or to monitor an incident learning system to drive future improvement activities.

      PMID:35616007 | PMC:PMC9278685 | DOI:10.1002/acm2.13653


      View details for PubMedID 35616007
  • Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs Journal of digital imaging
    Li F, Armato SG, Engelmann R, Rhines T, Crosby J, Lan L, Giger ML, MacMahon H
    2021 Aug;34(4):922-931. doi: 10.1007/s10278-021-00494-7. Epub 2021 Jul 29.
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      Our objective is to investigate the reliability and usefulness of anatomic point-based lung zone segmentation on chest radiographs (CXRs) as a reference standard framework and to evaluate the accuracy of automated point placement. Two hundred frontal CXRs were presented to two radiologists who identified five anatomic points: two at the lung apices, one at the top of the aortic arch, and two at the costophrenic angles. Of these 1000 anatomic points, 161 (16.1%) were obscured (mostly by pleural effusions). Observer variations were investigated. Eight anatomic zones then were automatically generated from the manually placed anatomic points, and a prototype algorithm was developed using the point-based lung zone segmentation to detect cardiomegaly and levels of diaphragm and pleural effusions. A trained U-Net neural network was used to automatically place these five points within 379 CXRs of an independent database. Intra- and inter-observer variation in mean distance between corresponding anatomic points was larger for obscured points (8.7 mm and 20 mm, respectively) than for visible points (4.3 mm and 7.6 mm, respectively). The computer algorithm using the point-based lung zone segmentation could diagnostically measure the cardiothoracic ratio and diaphragm position or pleural effusion. The mean distance between corresponding points placed by the radiologist and by the neural network was 6.2 mm. The network identified 95% of the radiologist-indicated points with only 3% of network-identified points being false-positives. In conclusion, a reliable anatomic point-based lung segmentation method for CXRs has been developed with expected utility for establishing reference standards for machine learning applications.

      PMID:34327625 | PMC:PMC8455736 | DOI:10.1007/s10278-021-00494-7


      View details for PubMedID 34327625
  • Deep convolutional neural networks in the classification of dual-energy thoracic radiographic views for efficient workflow: analysis on over 6500 clinical radiographs Journal of medical imaging (Bellingham, Wash.)
    Crosby J, Rhines T, Li F, MacMahon H, Giger M
    2020 Jan;7(1):016501. doi: 10.1117/1.JMI.7.1.016501. Epub 2020 Feb 3.
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      DICOM header information is frequently used to classify medical image types; however, if a header is missing fields or contains incorrect data, the utility is limited. To expedite image classification, we trained convolutional neural networks (CNNs) in two classification tasks for thoracic radiographic views obtained from dual-energy studies: (a) distinguishing between frontal, lateral, soft tissue, and bone images and (b) distinguishing between posteroanterior (PA) or anteroposterior (AP) chest radiographs. CNNs with AlexNet architecture were trained from scratch. 1910 manually classified radiographs were used for training the network to accomplish task (a), then tested with an independent test set (3757 images). Frontal radiographs from the two datasets were combined to train a network to accomplish task (b); tested using an independent test set of 1000 radiographs. ROC analysis was performed for each trained CNN with area under the curve (AUC) as a performance metric. Classification between frontal images (AP/PA) and other image types yielded an AUC of 0.997 [95% confidence interval (CI): 0.996, 0.998]. Classification between PA and AP radiographs resulted in an AUC of 0.973 (95% CI: 0.961, 0.981). CNNs were able to rapidly classify thoracic radiographs with high accuracy, thus potentially contributing to effective and efficient workflow.

      PMID:32042858 | PMC:PMC6995870 | DOI:10.1117/1.JMI.7.1.016501


      View details for PubMedID 32042858
  • Single-institution report of setup margins of voluntary deep-inspiration breath-hold (DIBH) whole breast radiotherapy implemented with real-time surface imaging Journal of applied clinical medical physics
    Xiao A, Crosby J, Malin M, Kang H, Washington M, Hasan Y, Chmura SJ, Al-Hallaq HA
    2018 Jul;19(4):205-213. doi: 10.1002/acm2.12368. Epub 2018 Jun 22.
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      PURPOSE: We calculated setup margins for whole breast radiotherapy during voluntary deep-inspiration breath-hold (vDIBH) using real-time surface imaging (SI).

      METHODS AND MATERIALS: Patients (n = 58) with a 27-to-31 split between right- and left-sided cancers were analyzed. Treatment beams were gated using AlignRT by registering the whole breast region-of-interest to the surface generated from the simulation CT scan. AlignRT recorded (three-dimensional) 3D displacements and the beam-on-state every 0.3 s. Means and standard deviations of the displacements during vDIBH for each fraction were used to calculate setup margins. Intra-DIBH stability and the intrafraction reproducibility were estimated from the medians of the 5th to 95th percentile range of the translations in each breath-hold and fraction, respectively.

      RESULTS: A total of 7269 breath-holds were detected over 1305 fractions in which a median dose of 200 cGy was delivered. Each fraction was monitored for 5.95 ± 2.44 min. Calculated setup margins were 4.8 mm (A/P), 4.9 mm (S/I), and 6.4 mm (L/R). The intra-DIBH stability and the intrafraction reproducibility were ≤0.7 mm and ≤2.2 mm, respectively. The isotropic margin according to SI (9.2 mm) was comparable to other institutions' calculations that relied on x-ray imaging and/or spirometry for patients with left-sided cancer (9.8-11.0 mm). Likewise, intra-DIBH variability and intrafraction reproducibility of breast surface measured with SI agreed with spirometry-based positioning to within 1.2 and 0.36 mm, respectively.

      CONCLUSIONS: We demonstrated that intra-DIBH variability, intrafraction reproducibility, and setup margins are similar to those reported by peer studies who utilized spirometry-based positioning.

      PMID:29935001 | PMC:PMC6036385 | DOI:10.1002/acm2.12368


      View details for PubMedID 29935001

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