| 1. |
- Zanoli, Massimiliano, 1989, et al.
(author)
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Suitability of eigenvalue beam-forming for discrete multi-frequency hyperthermia treatment planning
- 2021
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In: Medical Physics. - : Wiley. - 2473-4209 .- 0094-2405. ; 48:11, s. 7410-7426
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Journal article (peer-reviewed)abstract
- Purpose: Thermal dose delivery in microwave hyperthermia for cancer treatment is expected to benefit from the introduction of ultra-wideband (UWB)-phased array applicators. A full exploitation of the combination of different frequencies to improve the deposition pattern is, however, a nontrivial problem. It is unclear whether the cost functions used for hyperthermia treatment planning (HTP) optimization in the single-frequency setting can be meaningfully extended to the UWB case. Method: We discuss the ability of the eigenvalue (EV) and a novel implementation of iterative-EV (i-EV) beam-forming methods to fully exploit the available frequency spectrum when a discrete set of simultaneous operating frequencies is available for treatment. We show that the quadratic power deposition ratio solved by the methods can be maximized by only one frequency in the set, therefore rendering EV inadequate for UWB treatment planning. We further investigate whether this represents a limitation in two realistic test cases, comparing the thermal distributions resulting from EV and i-EV to those obtained by optimizing for other nonlinear cost functions that allow for multi-frequency. Results: The classical EV-based single-frequency HTP yields systematically lower target SAR deposition and temperature values than nonlinear HTP. In a larynx target, the proposed single-frequency i-EV scheme is able to compensate for this and reach temperatures comparable to those given by global nonlinear optimization. In a meninges target, the multi-frequency setting outperforms the single-frequency one, achieving better target coverage and (Formula presented.) higher (Formula presented.) in the tumor than single-frequency-based HTP. Conclusions: Classical EV performs poorly in terms of resulting target temperatures. The proposed single-frequency i-EV scheme can be a viable option depending on the patient and tumor to be treated, as long as the proper operating frequency can be selected across a UWB range. Multi-frequency HTP can bring a considerable benefit in regions typically difficult to treat such as the brain.
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| 2. |
- Atefi, Seyed Reza, et al.
(author)
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Intracranial haemorrhage alters scalp potential distributions in bioimpedance cerebral monitoring applications : preliminary results from FEM simulation on a realistic head model and human subjects
- 2016
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In: Medical Physics. - : American Association of Physicists in Medicine. - 2473-4209 .- 0094-2405. ; 43:2, s. 675-686
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Journal article (peer-reviewed)abstract
- Purpose: Current diagnostic neuroimaging for detection of intracranial hemorrhage (ICH) is limited to fixed scanners requiring patient transport and extensive infrastructure support. ICH diagnosis would therefore benefit from a portable diagnostic technology, such as electrical bioimpedance (EBI). Through simulations and patient observation, the authors assessed the influence of unilateral ICH hematomas on quasisymmetric scalp potential distributions in order to establish the feasibility of EBI technology as a potential tool for early diagnosis. Methods: Finite element method (FEM) simulations and experimental leftright hemispheric scalp potential differences of healthy and damaged brains were compared with respect to the asymmetry caused by ICH lesions on quasisymmetric scalp potential distributions. In numerical simulations, this asymmetry was measured at 25 kHz and visualized on the scalp as the normalized potential difference between the healthy and ICH damaged models. Proof-of-concept simulations were extended in a pilot study of experimental scalp potential measurements recorded between 0 and 50 kHz with the authors custom-made bioimpedance spectrometer. Mean leftright scalp potential differences recorded from the frontal, central, and parietal brain regions of ten healthy control and six patients suffering from acute/subacute ICH were compared. The observed differences were measured at the 5% level of significance using the two-sample Welch ttest. Results: The 3D-anatomically accurate FEM simulations showed that the normalized scalp potential difference between the damaged and healthy brain models is zero everywhere on the head surface, except in the vicinity of the lesion, where it can vary up to 5%. The authors preliminary experimental results also confirmed that the leftright scalp potential difference in patients with ICH (e.g., 64 mV) is significantly larger than in healthy subjects (e.g., 20.8 mV; P < 0.05). Conclusions: Realistic, proof-of-concept simulations confirmed that ICH affects quasisymmetric scalp potential distributions. Pilot clinical observations with the authors custom-made bioimpedance spectrometer also showed higher leftright potential differences in the presence of ICH, similar to those of their simulations, that may help to distinguish healthy subjects from ICH patients. Although these pilot clinical observations are in agreement with the computer simulations, the small sample size of this study lacks statistical power to exclude the influence of other possible confounders such as age, ex, and electrode positioning. The agreement with previously published simulation-based and clinical results, however, suggests that EBI technology may be potentially useful for ICH detection.
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| 3. |
- Böck, Michelle
(author)
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On adaptation cost and tractability in robust adaptive radiation therapy optimization
- 2020
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In: Medical physics (Lancaster). - : Wiley. - 0094-2405 .- 2473-4209. ; 47:7, s. 2791-2804
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Journal article (peer-reviewed)abstract
- Purpose In this paper, a framework for online robust adaptive radiation therapy (ART) is discussed and evaluated. The purpose of the presented approach to ART is to: (a) handle interfractional geometric variations following a probability distribution different from the a priori hypothesis, (b) address adaptation cost, and Methods A novel framework for online robust ART using the concept of Bayesian inference and scenario reduction is introduced and evaluated in a series of simulated cases on a one-dimensional phantom geometry. The initial robust plan is generated from a robust optimization problem based on either expected-value or worst-case optimization approach using the a priori hypothesis of the probability distribution governing the interfractional geometric variations. Throughout the course of treatment, the simulated interfractional variations are evaluated in terms of their likelihood with respect to the a priori hypothesis of their distribution and violation of user-specified tolerance limits by the accumulated dose. If an adaptation is considered, the a posteriori distribution is computed from the actual variations using Bayesian inference. Then, the adapted plan is optimized to better suit the actual interfractional variations of the individual case. This adapted plan is used until the next adaptation is triggered. To address adaptation cost, the proposed framework provides an option for increased adaptation frequency. Computational tractability in robust planning and ART is addressed by an approximation algorithm to reduce the size of the optimization problem. Results According to the simulations, the proposed framework may improve target coverage compared to the corresponding nonadaptive robust approach. In particular, Bayesian inference may be useful to individualize plans to the actual interfractional variations. Concerning adaptation cost, the results indicate that mathematical methods like Bayesian inference may have a greater impact on improving individual treatment quality than increased adaptation frequency. In addition, the simulations suggest that the concept of scenario reduction may be useful to address computational tractability in ART and robust planning in general. Conclusions The simulations indicate that the adapted plans may improve target coverage and OAR protection at manageable adaptation and computational cost within the novel framework. In particular, adaptive strategies using Bayesian inference appear to perform best among all strategies. This proof-of-concept study provides insights into the mathematical aspects of robustness, tractability, and ART, which are a useful guide for further development of frameworks for online robust ART.
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| 4. |
- Meaney, Paul M, 1960, et al.
(author)
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y Two-step inversion with a logarithmic transformation for microwave breast imaging
- 2017
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In: Medical Physics. - : Wiley. - 2473-4209 .- 0094-2405. ; 44:8, s. 4239-4251
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Journal article (peer-reviewed)abstract
- Purpose: The authors have developed a new two-step microwave tomographic image reconstruction process specifically designed to incorporate logarithmic transformed microwave imaging algorithms as a means of significantly improving spatial resolution and target property recovery. Log transform eliminates the need for a priori information, but spatial filtering often integrated as part of the regularization required to stabilize image recovery, generally smooths image features and reduces object definition. The new implementation begins with this smoothed image as the first step, but then utilizes it as the starting estimate for a second step which continues the iterative process with a standard weighted Euclidean distance regularization. The penalty term of the latter restricts the new image to a multi-dimensional location close to the original but allows the algorithm to optimize the image without excessive smoothing. Methods: The overall approach is based on a Gauss-Newton iterative scheme which incorporates a log transformation as a way of making the reconstruction more linear. It has been shown to be robust and not require a priori information as a condition for convergence, but does produce somewhat smoothed images as a result of associated regularization. The new two-step process utilizes the previous technique to generate a smoothed initial estimate and then uses the same reconstruction process with a weighted Euclidean distance penalty term. A simple and repeatable method has been implemented to determine the weighting factor without significant computational burden. The reconstructions are assessed according to conventional parameter estimation metrics. Results: We apply the approach to phantom experiments using large, high contrast canonical shapes followed by a set of images recovered from an actual patient exam. The image improvements are substantial in regards to improved property recovery and feature delineation without inducing unwanted artifacts. Analysis of the residual vector after the reconstruction process further emphasizes that the minimization criterion is efficient with minimal biases. Conclusions: The outcome is a novel synergism of an established stable reconstruction algorithm with a conventional regularization technique. It maintains the ability to recover high quality microwave tomographic images without the bias of a priori information while substantially improving image quality. The results are confirmed on both phantom experiments and patient exams.
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| 5. |
- Bayisa, Fekadu, et al.
(author)
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Statistical learning in computed tomography image estimation
- 2018
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In: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 45:12, s. 5450-5460
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Journal article (peer-reviewed)abstract
- Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance of our method with the existing model-based CT image estimation methods.Methods: The statistical learning approach proposed here consists of two stages. At the trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 5% and 23% on the whole brain and bone tissues, respectively.Conclusions: Evaluation of our method shows that it is a promising method to generate CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications
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| 6. |
- Götstedt, Julia, et al.
(author)
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Development and evaluation of aperture-based complexity metrics using film and EPID measurements of static MLC openings.
- 2015
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In: Medical physics. - : Wiley. - 2473-4209 .- 0094-2405. ; 42:7, s. 3911-21
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Journal article (peer-reviewed)abstract
- Complexity metrics have been suggested as a complement to measurement-based quality assurance for intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). However, these metrics have not yet been sufficiently validated. This study develops and evaluates new aperture-based complexity metrics in the context of static multileaf collimator (MLC) openings and compares them to previously published metrics.This study develops the converted aperture metric and the edge area metric. The converted aperture metric is based on small and irregular parts within the MLC opening that are quantified as measured distances between MLC leaves. The edge area metric is based on the relative size of the region around the edges defined by the MLC. Another metric suggested in this study is the circumference/area ratio. Earlier defined aperture-based complexity metrics-the modulation complexity score, the edge metric, the ratio monitor units (MU)/Gy, the aperture area, and the aperture irregularity-are compared to the newly proposed metrics. A set of small and irregular static MLC openings are created which simulate individual IMRT/VMAT control points of various complexities. These are measured with both an amorphous silicon electronic portal imaging device and EBT3 film. The differences between calculated and measured dose distributions are evaluated using a pixel-by-pixel comparison with two global dose difference criteria of 3% and 5%. The extent of the dose differences, expressed in terms of pass rate, is used as a measure of the complexity of the MLC openings and used for the evaluation of the metrics compared in this study. The different complexity scores are calculated for each created static MLC opening. The correlation between the calculated complexity scores and the extent of the dose differences (pass rate) are analyzed in scatter plots and using Pearson's r-values.The complexity scores calculated by the edge area metric, converted aperture metric, circumference/area ratio, edge metric, and MU/Gy ratio show good linear correlation to the complexity of the MLC openings, expressed as the 5% dose difference pass rate, with Pearson's r-values of -0.94, -0.88, -0.84, -0.89, and -0.82, respectively. The overall trends for the 3% and 5% dose difference evaluations are similar.New complexity metrics are developed. The calculated scores correlate to the complexity of the created static MLC openings. The complexity of the MLC opening is dependent on the penumbra region relative to the area of the opening. The aperture-based complexity metrics that combined either the distances between the MLC leaves or the MLC opening circumference with the aperture area show the best correlation with the complexity of the static MLC openings.
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| 7. |
- Holmes, Robin B., et al.
(author)
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Creation of an anthropomorphic CT head phantom for verification of image segmentation
- 2020
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In: Medical physics (Lancaster). - : Wiley-Blackwell. - 0094-2405 .- 2473-4209. ; 47:6, s. 2380-2391
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Journal article (peer-reviewed)abstract
- Purpose: Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast.Methods: To investigate these issues we have created a three-dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom.Results: Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9-35.8 phantom, 29.9-34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other.Conclusion: The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.
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| 8. |
- Johansson, Adam, et al.
(author)
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Gastrointestinal 4D MRI with respiratory motion correction
- 2021
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In: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 48:5, s. 2521-2527
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Journal article (peer-reviewed)abstract
- Purpose Gastrointestinal motion patterns such as peristalsis and segmental contractions can alter the shape and position of the stomach and intestines with respect to other irradiated organs during radiation therapy. Unfortunately, these deformations are concealed by conventional four-dimensional (4D)-MRI techniques, which were developed to visualize respiratory motion by binning acquired data into respiratory motion states without considering the phases of GI motion. We present a method to reconstruct breathing-compensated images showing the phases of periodic gastric motion and study the effect of this motion on regional anatomical structures. Methods Sixty-seven DCE-MRI examinations were performed on patients undergoing MRI simulation for hepatocellular carcinoma using a golden-angle stack-of-stars sequence that collected 2000 radial spokes over 5 min. The collected data were reconstructed using a method with integrated respiratory motion correction into a time series of 3D image volumes without visible breathing motion. From this series, a gastric motion signal was extracted by temporal filtering of time-intensity curves in the stomach. Using this motion signal, breathing-corrected back-projection images were sorted according to the gastric phase and reconstructed into 21 gastric motion state images showing the phases of gastric motion. Results Reconstructed image volumes showed gastric motion states clearly with no visible breathing motion or related artifacts. The mean frequency of the gastric motion signal was 3 cycles/min with a standard deviation of 0.27 cycles/min. Conclusions Periodic gastrointestinal motion can be visualized without confounding respiratory motion using the presented GI 4D MRI technique. GI 4D MRIs may help define internal target volumes for treatment planning, aid in planning organ at risk volume definition, or support motion model development for gastrointestinal motion tracking algorithms for real-time MR-guided radiation therapy.
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| 9. |
- Liu, Lianli, et al.
(author)
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Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning
- 2022
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In: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 49:9, s. 6110-6119
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Journal article (peer-reviewed)abstract
- Purpose To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time. Methods A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high-resolution volumetric images. Patient-specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model-reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5-min time series of both ground truth and model-reconstructed volumetric images with a temporal resolution of 340 ms. Results Across the seven patients evaluated, the median distances between model-predicted and ground truth GTV centroids in the superior-inferior direction were 0.4 +/- 0.3 mm and 0.5 +/- 0.4 mm for cine and radial acquisitions, respectively. The 95-percentile Hausdorff distances between model-predicted and ground truth GTV contours were 4.7 +/- 1.1 mm and 3.2 +/- 1.5 mm for cine and radial acquisitions, which are of the same scale as cross-plane image resolution. Conclusion Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI-guided radiotherapy precision.
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| 10. |
- Nyholm, Tufve, et al.
(author)
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MR and CT data with multiobserver delineations of organs in the pelvic areaPart of the Gold Atlas project
- 2018
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In: Med Phys. - : Wiley. - 0094-2405 .- 2473-4209. ; 45:3, s. 1295-1300
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Journal article (peer-reviewed)abstract
- PurposeWe describe a public dataset with MR and CT images of patients performed in the same position with both multiobserver and expert consensus delineations of relevant organs in the male pelvic region. The purpose was to provide means for training and validation of segmentation algorithms and methods to convert MR to CT like data, i.e., so called synthetic CT (sCT). Acquisition and validation methodsT1- and T2-weighted MR images as well as CT data were collected for 19 patients at three different departments. Five experts delineated nine organs for each patient based on the T2-weighted MR images. An automatic method was used to fuse the delineations. Starting from each fused delineation, a consensus delineation was agreed upon by the five experts for each organ and patient. Segmentation overlap between user delineations with respect to the consensus delineations was measured to describe the spread of the collected data. Finally, an open-source software was used to create deformation vector fields describing the relation between MR and CT images to further increase the usability of the dataset. Data format and usage notesThe dataset has been made publically available to be used for academic purposes, and can be accessed from . Potential applicationsThe dataset provides a useful source for training and validation of segmentation algorithms as well as methods to convert MR to CT-like data (sCT). To give some examples: The T2-weighted MR images with their consensus delineations can directly be used as a template in an existing atlas-based segmentation engine; the expert delineations are useful to validate the performance of a segmentation algorithm as they provide a way to measure variability among users which can be compared with the result of an automatic segmentation; and the pairwise deformably registered MR and CT images can be a source for an atlas-based sCT algorithm or for validation of sCT algorithm. (c) 2018 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.
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