This paper presents a blind detection and compensation technique for camera lens geometric distortions. The lens distortion introduces higher-order correlations in the frequency domain and in turn it can be detected using higher-order spectral analysis tools without assuming any specific calibration target. The existing blind lens distortion removal method only considered a single-coefficient radial distortion model. In this paper, two coefficients are considered to model approximately the geometric distortion. All the models considered have analytical closed-form inverse formulae.

Background

Whole-brain radiotherapy (WBRT) remains an important treatment for over 200,000 cancer patients in the United States annually. Hippocampal-avoidant WBRT (HA-WBRT) reduces neurocognitive toxicity compared to standard WBRT, but HA-WBRT contouring and planning are more complex and time-consuming than standard WBRT. We designed and evaluated a workflow using commercially available artificial intelligence tools for automated hippocampal segmentation and treatment planning to efficiently generate clinically acceptable HA-WBRT radiotherapy plans.

Methods

We retrospectively identified 100 consecutive adult patients treated for brain metastases outside the hippocampal region. Each patient's T1 post-contrast brain MRI was processed using NeuroQuant, an FDA-approved software that provides segmentations of brain structures in less than 8 min. Automated hippocampal segmentations were reviewed for accuracy, then converted to files compatible with a commercial treatment planning system, where hippocampal avoidance regions and planning target volumes (PTV) were generated. Other organs-at-risk (OARs) were previously contoured per clinical routine. A RapidPlan knowledge-based planning routine was applied for a prescription of 30 Gy in 10 fractions using volumetric modulated arc therapy (VMAT) delivery. Plans were evaluated based on NRG CC001 dose-volume objectives (Brown et al. in J Clin Oncol, 2020).

Results

Of the 100 cases, 99 (99%) had acceptable automated hippocampi segmentations without manual intervention. Knowledge-based planning was applied to all cases; the median processing time was 9 min 59 s (range 6:53-13:31). All plans met per-protocol dose-volume objectives for PTV per the NRG CC001 protocol. For comparison, only 65.5% of plans on NRG CC001 met PTV goals per protocol, with 26.1% within acceptable variation. In this study, 43 plans (43%) met OAR constraints, and the remaining 57 (57%) were within acceptable variation, compared to 42.5% and 48.3% on NRG CC001, respectively. No plans in this study had unacceptable dose to OARs, compared to 0.8% of manually generated plans from NRG CC001. 8.4% of plans from NRG CC001 were not scored or unable to be evaluated.

Conclusions

An automated pipeline harnessing the efficiency of commercially available artificial intelligence tools can generate clinically acceptable VMAT HA-WBRT plans with minimal manual intervention. This process could improve clinical efficiency for a treatment established to improve patient outcomes over standard WBRT.

Purpose

The purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator.

Methods

A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean (SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc) with ΔD_x=D_x,actual-D_x,predicted mean, standard deviation, and Pearson correlation coefficient further quantifying model performance.

Results

Ranges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯₉₀±σ_ΔD = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder ΔD¯_2cc±σ_ΔD = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum ΔD¯_2cc±σ_ΔD= -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid ΔD¯_2cc±σ_ΔD = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy).

Conclusions

A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.

Online adaptive radiotherapy platforms present a unique challenge for commissioning as guidance is lacking and specialized adaptive equipment, such as deformable phantoms, are rare. We designed a novel adaptive commissioning process consisting of end-to-end tests using standard clinical resources. These tests were designed to simulate anatomical changes regularly observed at patient treatments. The test results will inform users of the magnitude of uncertainty from on-treatment changes during the adaptive workflow and the limitations of their systems. We implemented these tests for the cone-beam computed tomography (CT)-based Varian Ethos online adaptive platform. Many adaptive platforms perform online dose calculation on a synthetic CT (synCT). To assess the impact of the synCT generation and online dose calculation on dosimetric accuracy, we conducted end-to-end tests using commonly available equipment: a CIRS IMRT Thorax phantom, PinPoint ionization chamber, Gafchromic film, and bolus. Four clinical scenarios were evaluated: weight gain and weight loss were simulated by adding and removing bolus, internal target shifts were simulated by editing the CTV during the adaptive workflow to displace it, and changes in gas were simulated by removing and reinserting rods in varying phantom locations. The effect of overriding gas pockets during planning was also assessed. All point dose measurements agreed within 2.7% of the calculated dose, with one exception: a scenario simulating gas present in the planning CT, not overridden during planning, and dissipating at treatment. Relative film measurements passed gamma analysis (3%/3 mm criteria) for all scenarios. Our process validated the Ethos dose calculation for online adapted treatment plans. Based on our results, we made several recommendations for our clinical adaptive workflow. This commissioning process used commonly available equipment and, therefore, can be applied in other clinics for their respective online adaptive platforms.

Objectives

To develop a training program designed to meet the specific needs of medical physicists as they transition into a clinical role with direct patient care responsibilities.

Materials and methods

The training program was designed in collaboration with the faculty at the UC San Diego School of Medicine and incorporates training techniques that have been shown to be effective in improving communication skills. The program emphasizes experiential, practice-based learning over didactic presentations.

Results

The training program is comprised of 5 components: 1) a 1-day Clinician-Patient Communication Workshop run by the UC San Diego School of Medicine, 2) Communication Strategies for Radiation Oncology, which consists of two, 2-hour sessions designed to provide trainees with patient communication skills that are specific to patient interactions in radiation oncology, 3) Simulated Patient Interactions, in which trainees perform mock physicist-patient consults with trained patient actors, 4) Faculty-Observed Patient Consults, and 5) a Case-Based Treatment Toxicity Course. A competency assessment mechanism was also developed to provide a clear set of objectives and to guide trainer feedback. [Correction added after first online publication on November 7, 2018: The phrase ", which consists of two, 2-hour" was added above.] CONCLUSIONS: The training program that we have developed incorporates an array of established education techniques and provides a comprehensive, accessible, means of improving medical physicists' patient communication skills.

Purpose

To provide insight into the types of questions asked to medical physicists by patients during one-on-one physicist-patient consults at one institution.

Materials and methods

Medical physicists trained in patient communication techniques met with patients to provide an overview of the treatment planning and delivery processes, discuss the patient's treatment plan, and answer any technical questions. From August 2016 to December 2019, 152 physicist-patient consults were conducted. In the initial months of the study (August 2016-December 2017), following each physicist-patient consult, all patient questions were documented by the physicists. For the remaining time period (January 2018-December 2019), any newly encountered questions were periodically added to the list. The questions were compiled into a comprehensive list and organized into categories.

Results

There were a total of 88 unique patient questions. These questions fit into four topical categories. Fifty-four questions (61.4%) were in the "Treatment Planning and Delivery Questions" category, 15 questions (17.1%) were in the "General Radiation Questions or Concerns" category, 13 questions (14.8%) were in the "Safety and Quality Assurance Questions" category, and 6 questions (6.8%) were in the "Medical Questions" category. Overall, patients were primarily concerned about how radiation works, the treatment planning and delivery processes, and what is being done to keep them safe throughout their treatment.

Conclusion

Physicist-patient consults provided an opportunity to address the technical aspects of radiation therapy with patients in greater detail. The fact that patient questions could be conveniently grouped into only four topical categories indicates that it may be straightforward for other medical physicists to prepare for effectively addressing technical questions during physicist-patient consults.

Purpose

To demonstrate an efficient method for training and validation of a knowledge-based planning (KBP) system as a radiation therapy clinical trial plan quality-control system.

Methods and materials

We analyzed 86 patients with stage IB through IVA cervical cancer treated with intensity modulated radiation therapy at 2 institutions according to the standards of the INTERTECC (International Evaluation of Radiotherapy Technology Effectiveness in Cervical Cancer, National Clinical Trials Network identifier: 01554397) protocol. The protocol used a planning target volume and 2 primary organs at risk: pelvic bone marrow (PBM) and bowel. Secondary organs at risk were rectum and bladder. Initial unfiltered dose-volume histogram (DVH) estimation models were trained using all 86 plans. Refined training sets were created by removing sub-optimal plans from the unfiltered sample, and DVH estimation models… and DVH estimation models were constructed by identifying 30 of 86 plans emphasizing PBM sparing (comparing protocol-specified dosimetric cutpoints V₁₀ (percentage volume of PBM receiving at least 10 Gy dose) and V₂₀ (percentage volume of PBM receiving at least 20 Gy dose) with unfiltered predictions) and another 30 of 86 plans emphasizing bowel sparing (comparing V₄₀ (absolute volume of bowel receiving at least 40 Gy dose) and V₄₅ (absolute volume of bowel receiving at least 45 Gy dose), 9 in common with the PBM set). To obtain deliverable KBP plans, refined models must inform patient-specific optimization objectives and/or priorities (an auto-planning "routine"). Four candidate routines emphasizing different tradeoffs were composed, and a script was developed to automatically re-plan multiple patients with each routine. After selection of the routine that best met protocol objectives in the 51-patient training sample (KBP_FINAL), protocol-specific DVH metrics and normal tissue complication probability were compared for original versus KBP_FINAL plans across the 35-patient validation set. Paired t tests were used to test differences between planning sets.

Results

KBP_FINAL plans outperformed manual planning across the validation set in all protocol-specific DVH cutpoints. The mean normal tissue complication probability for gastrointestinal toxicity was lower for KBP_FINAL versus validation-set plans (48.7% vs 53.8%, P<.001). Similarly, the estimated mean white blood cell count nadir was higher (2.77 vs 2.49 k/mL, P<.001) with KBP_FINAL plans, indicating lowered probability of hematologic toxicity.

Conclusions

This work demonstrates that a KBP system can be efficiently trained and refined for use in radiation therapy clinical trials with minimal effort. This patient-specific plan quality control resulted in improvements on protocol-specific dosimetric endpoints.

Background

To test the hypothesis that atlas-based active bone marrow (ABM)-sparing intensity modulated radiation therapy (IMRT) yields similar dosimetric results compared to custom ABM-sparing IMRT for cervical cancer patients.

Methods

We sampled 62 cervical cancer patients with pre-treatment FDG-PET/CT in training (n=32) or test (n=30) sets. ABM was defined as the subvolume of the pelvic bone marrow (PBM) with standardized uptake value (SUV) above the mean on the average FDG-PET image (ABM_Atlas) vs. the individual's PET (ABM_Custom). Both were deformed to the planning CT. Overlap between the two subvolumes was measured using the Dice coefficient. Three IMRT plans designed to spare PBM, ABM_Atlas, or ABM_Custom were compared for 30 test patients. Dosimetric parameters were used to evaluate plan quality.

Results

ABM_Atlas and ABM_Custom volumes were not significantly different (p=0.90), with a mean Dice coefficient of 0.75, indicating good agreement. Compared to IMRT plans designed to spare PBM and ABM_Custom, ABM_Atlas-sparing IMRT plans achieved excellent target coverage and normal tissue sparing, without reducing dose to ABM_Custom (mean ABM_Custom dose 29.4Gy vs. 27.1Gyvs. 26.9Gy, respectively; p=0.10); however, PTV coverage and bowel sparing were slightly reduced.

Conclusions

Atlas-based ABM sparing IMRT is clinically feasible and may obviate the need for customized ABM-sparing as a strategy to reduce hematologic toxicity.