Assessment of different patient‐to‐phantom matching criteria applied in Monte Carlo–based computed tomography dosimetry

Stepusin, Elliott J.; Long, Daniel J.; Marshall, Emily L.; Bolch, Wesley E.

doi:10.1002/mp.12502

Cited by 20 publications

(23 citation statements)

References 17 publications

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“…Several habitus‐dependent phantom series have been developed to perform patient‐specific dose estimation by matching anthropometric characteristics of patients, such as gender, age, height, and body weight . Stepusin et al suggested to match patient's data to a computational phantom from a predefined library using height and weight matching for patient‐specific CT dosimetry. The construction of patient‐specific models from regional CT images is another alternative for patient‐specific dosimetry, which was adopted in a number of studies by mapping the segmented model of patient CT images to a template anatomy through a deformable registration process .…”

Section: Introductionmentioning

confidence: 99%

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

2019

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Purpose Diagnostic imaging procedures require optimization depending on the medical task at hand, the apparatus being used, and patient physical and anatomical characteristics. The assessment of the radiation dose and associated risks plays a key role in safety and quality management for radiation protection purposes. In this work, we aim at developing a methodology for personalized organ‐level dose assessment in x‐ray computed tomography (CT) imaging. Methods Regional voxel models representing reference patient‐specific computational phantoms were generated through image segmentation of CT images for four patients. The best‐fitting anthropomorphic phantoms were selected from a previously developed comprehensive phantom library according to patient's anthropometric parameters, then registered to the anatomical masks (skeleton, lung, and body contour) of patients to produce a patient‐specific whole‐body phantom. Well‐established image registration metrics including Jaccard's coefficients for each organ, organ mass, body perimeter, organ‐surface distance, and effective diameter are compared between the reference patient model, registered model, and anchor phantoms. A previously validated Monte Carlo code is utilized to calculate the absorbed dose in target organs along with the effective dose delivered to patients. The calculated absorbed doses from the reference patient models are then compared with the produced personalized model, anchor phantom, and those reported by commercial dose monitoring systems. Results The evaluated organ‐surface distance and body effective diameter metrics show a mean absolute difference between patient regional voxel models, serving as reference, and patient‐specific models around 4.4% and 4.5%, respectively. Organ‐level radiation doses of patient‐specific models are in good agreement with those of the corresponding patient regional voxel models with a mean absolute difference of 9.1%. The mean absolute difference of organ doses for the best‐fitting model extracted from the phantom library and Radimetrics™ commercial dose tracking software are 15.5% and 41.1%, respectively. Conclusion The results suggest that the proposed methodology improves the accuracy of organ‐level dose estimation in CT, especially for extreme cases [high body mass index (BMI) and large skeleton]. Patient‐specific radiation dose calculation and risk assessment can be performed using the proposed methodology for both monitoring of cumulative radiation exposure of patients and epidemiological studies. Further validation using a larger database is warranted.

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Section: Introductionmentioning

confidence: 99%

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

2019

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“…Such a phantom library provides some variation in anatomies of different age, gender, height, and weight categories, but for the task of providing a good resemblance of a historical patient's anatomy, how to match the historical patient with an existing phantom remains a question. In a recent study, several patient‐to‐phantom matching methods were tested with Monte Carlo‐based dose calculation for CT . The results indicated that (a) the water equivalent diameter of the phantom is the superior matching metric, though the method is less feasible to implement in a retrospective setting; and (b) height‐and‐weight matching is superior to age‐and‐gender matching.…”

Section: Introductionmentioning

confidence: 99%

“…However, similar to the challenge for dose reconstruction using computational phantoms, it is yet unknown how such a CT scan should be selected based on the data available from historically treated patients so as to get the best match. In this pilot study, in line with the previously published phantom‐based reconstruction methods, we therefore tested the suitability of only using age and gender as selection criteria . We focused on Wilms’ tumor (WT) plans .…”

Section: Introductionmentioning

confidence: 99%

Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients’ CT scans?

et al. 2018

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Purpose: The purpose of this work was to assess the feasibility of using surrogate CT scans of matched patients for organ dose reconstructions for childhood cancer (CC) survivors, treated in the past with only 2D imaging data available instead of 3D CT data, and in particular using the current literature standard of matching patients based on similarity in age and gender. Methods: Thirty-one recently treated CC patients with abdominal CT scans were divided into six age-and gender-matched groups. From each group, two radiotherapy plans for Wilms' tumor were selected as reference plans and applied to the age-and gender-matched patients' CTs in the respective group. Two reconstruction strategies were investigated: S1) without field adjustments; S2) with manual field adjustments according to anatomical information, using a visual check in digitally reconstructed radiographs. To assess the level of agreement between the reconstructed and the reference dose distributions, we computed (using a collapsed cone algorithm) and compared the absolute deviation in mean and maximum dose normalized by the prescribed dose (i.e., normalized errors |NE mean | and |NE 2cc |) in eight organs at risk (OARs): heart, lungs, liver, spleen, kidneys, and spinal cord. Furthermore, we assessed the quality of a reconstruction case by varying acceptance thresholds for |NE mean | and |NE 2cc |. A reconstruction case was accepted (i.e., considered to pass) if the errors in all OARs are smaller than the threshold. The pass fraction for a given threshold was then defined as the percentage of reconstruction cases that were classified as a pass. Furthermore, we consider the impact of allowing to use a different CT scan for each OAR. Results: Slightly smaller reconstruction errors were achieved with S2 in multiple OARs than with S1 (P < 0.05). Among OARs, the best reconstruction was found for the spinal cord (average |NE mean | and |NE 2cc | ≤ 4%). The largest average |NE mean | was found in the spleen (18%). The largest average |NE 2cc | was found in the left lung (26%). Less than 30% of the reconstruction cases (i.e., pass fraction) meet the criteria that |NE mean | < 20% and |NE 2cc | < 20% in all OARs when using age and gender matching and a single CT to do reconstructions. Allowing other matchings and combining reconstructions for OARs from multiple patients, the pass fraction increases substantially to more than 60%. Conclusions: To conclude, reconstructions with small deviations can be obtained by using CC patients' CT scans, making the general approach promising. However, using age and gender as the only matching criteria to select a CT scan for the reconstruction is not sufficient to guarantee sufficiently low reconstruction errors. It is therefore suggested to include more features (e.g., height, features extracted from 2D radiographs) than only age and gender for dose reconstruction for CC survivors treated in the pre-3D radiotherapy planning era and to consider ways to combine multiple reconstructions focused on different OARs.

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“…This approach can be related to literature, where new ways to identify which phantom to pick are studied. 3,51,52 Although our phantom construction pipeline can predict the different 3-D metrics independently (position for each direction, sDSC for each OAR segmentation), for a single CT approach, an overall score needs to be defined that expresses how representative a CT scan is. The design of such a score is not trivial.…”

Section: Human-designed Criteria For Phantom Selectionmentioning

confidence: 99%

Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

et al. 2020

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Purpose: Current phantoms used for the dose reconstruction of long-term childhood cancer survivors lack individualization. We design a method to predict highly individualized abdominal three-dimensional (3-D) phantoms automatically. Approach: We train machine learning (ML) models to map (2-D) patient features to 3-D organat-risk (OAR) metrics upon a database of 60 pediatric abdominal computed tomographies with liver and spleen segmentations. Next, we use the models in an automatic pipeline that outputs a personalized phantom given the patient's features, by assembling 3-D imaging from the database. A step to improve phantom realism (i.e., avoid OAR overlap) is included. We compare five ML algorithms, in terms of predicting OAR left-right (LR), anterior-posterior (AP), inferior-superior (IS) positions, and surface Dice-Sørensen coefficient (sDSC). Furthermore, two existing human-designed phantom construction criteria and two additional control methods are investigated for comparison. Results: Different ML algorithms result in similar test mean absolute errors: ∼8 mm for liver LR, IS, and spleen AP, IS; ∼5 mm for liver AP and spleen LR; ∼80% for abdomen sDSC; and ∼60% to 65% for liver and spleen sDSC. One ML algorithm (GP-GOMEA) significantly performs the best for 6/9 metrics. The control methods and the human-designed criteria in particular perform generally worse, sometimes substantially (þ5-mm error for spleen IS, −10% sDSC for liver). The automatic step to improve realism generally results in limited metric accuracy loss, but fails in one case (out of 60). Conclusion: Our ML-based pipeline leads to phantoms that are significantly and substantially more individualized than currently used human-designed criteria.

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Assessment of different patient‐to‐phantom matching criteria applied in Monte Carlo–based computed tomography dosimetry

Cited by 20 publications

References 17 publications

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

Are age and gender suitable matching criteria in organ dose reconstruction using surrogate childhood cancer patients’ CT scans?

Machine learning for the prediction of pseudorealistic pediatric abdominal phantoms for radiation dose reconstruction

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