| 1. |
- Andersson, Jonas, 1975-, et al.
(författare)
-
Estimation of patient skin dose in fluoroscopy : summary of a joint report by AAPM TG357 and EFOMP
- 2021
-
Ingår i: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 48:7, s. e671-e696
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Physicians use fixed C-arm fluoroscopy equipment with many interventional radiological and cardiological procedures. The associated effective dose to a patient is generally considered low risk, as the benefit-risk ratio is almost certainly highly favorable. However, X-ray-induced skin injuries may occur due to high absorbed patient skin doses from complex fluoroscopically guided interventions (FGI). Suitable action levels for patient-specific follow-up could improve the clinical practice. There is a need for a refined metric regarding follow-up of X-ray-induced patient injuries and the knowledge gap regarding skin dose-related patient information from fluoroscopy devices must be filled. The most useful metric to indicate a risk of erythema, epilation or greater skin injury that also includes actionable information is the peak skin dose, that is, the largest dose to a region of skin.Materials and Methods: The report is based on a comprehensive review of best practices and methods to estimate peak skin dose found in the scientific literature and situates the importance of the Digital Imaging and Communication in Medicine (DICOM) standard detailing pertinent information contained in the Radiation Dose Structured Report (RDSR) and DICOM image headers for FGI devices. Furthermore, the expertise of the task group members and consultants have been used to bridge and discuss different methods and associated available DICOM information for peak skin dose estimation.Results: The report contributes an extensive summary and discussion of the current state of the art in estimating peak skin dose with FGI procedures with regard to methodology and DICOM information. Improvements in skin dose estimation efforts with more refined DICOM information are suggested and discussed.Conclusions: The endeavor of skin dose estimation is greatly aided by the continuing efforts of the scientific medical physics community, the numerous technology enhancements, the dose-controlling features provided by the FGI device manufacturers, and the emergence and greater availability of the DICOM RDSR. Refined and new dosimetry systems continue to evolve and form the infrastructure for further improvements in accuracy. Dose-related content and information systems capable of handling big data are emerging for patient dose monitoring and quality assurance tools for large-scale multihospital enterprises.
|
|
| 2. |
- Bayisa, Fekadu, et al.
(författare)
-
Statistical learning in computed tomography image estimation
- 2018
-
Ingår i: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 45:12, s. 5450-5460
-
Tidskriftsartikel (refereegranskat)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
|
|
| 3. |
- Gotz, Malte, et al.
(författare)
-
Correction for volume recombination in liquid ionization chambers at high dose-per-pulse
- 2019
-
Ingår i: Medical physics (Lancaster). - : WILEY. - 0094-2405 .- 2473-4209. ; 46:8, s. 3692-3699
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose To determine the volume recombination at high dose-per-pulse in liquid ionization chambers (LIC) and to ascertain whether existing calculation methods verified in air-filled chambers may be used to calculate a correction factor. Methods Two LICs, one filled with 2,2,4-trimethylpentane (isooctane) the other with tetramethylsilane (TMS), were irradiated in a pulsed, 20 MeV electron beam. Via reference measurements with a Faraday cup, the saturation correction for volume recombination was determined for dose-per-pulse values ranging from about 5 mGy to 1 Gy for both chambers at a pulse duration of 693 ns. In addition, the isooctane chamber was irradiated with pulses of varying duration, ranging from 5 ps to 10 ms, at a dose-per-pulse of about 76.5 mGy. The dose-per-pulse-dependent measurements were compared to calculations based on Boag's models (with and without a free electron fraction), the two-dose-rate method, and a numerical calculation. The pulse duration dependent measurements were compared only to a numerical calculation that iteratively calculates the charge transport and loss in a 1D model of an ionization chamber. Results In TMS only Boag's model with a free electron fraction and the numerical calculation are in good agreement with the experimental data. However, in isooctane, good agreement is observed between the experimental data, the numerical calculation as well as the two-dose-rate method, and Boag's model including a free electron fraction. Only Boag's model without a free electron fraction shows a good agreement with lesser extend. Furthermore, the pulse duration-dependent data for isooctane are well described by the numerical model. Conclusion With isooctane as an active medium, a LIC could be directly used in a field with high dose-per-pulse utilizing the well-established two-dose-rate method to correct the volume recombination. In addition, pulsed fields with variable pulse duration are easily modeled for this medium using a numerical calculation. Other media, as exemplified by the TMS-filled chamber, might require additional considerations, such as including a fraction of free electrons in the consideration of volume recombination.
|
|
| 4. |
- Holmes, Robin B., et al.
(författare)
-
Creation of an anthropomorphic CT head phantom for verification of image segmentation
- 2020
-
Ingår i: Medical physics (Lancaster). - : Wiley-Blackwell. - 0094-2405 .- 2473-4209. ; 47:6, s. 2380-2391
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 5. |
- Nyholm, Tufve, et al.
(författare)
-
MR and CT data with multiobserver delineations of organs in the pelvic areaPart of the Gold Atlas project
- 2018
-
Ingår i: Med Phys. - : Wiley. - 0094-2405 .- 2473-4209. ; 45:3, s. 1295-1300
-
Tidskriftsartikel (refereegranskat)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.
|
|
| 6. |
- Näsmark, Torbjörn, 1986-, et al.
(författare)
-
Proton stopping power prediction based on dual-energy CT-generated virtual mono-energetic images
- 2021
-
Ingår i: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 48:9, s. 5232-5243
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual-energy CT generated virtual monoenergetic (VM) images.Materials and methods: A rapid kV-switching dual-energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120 kV conventional single-energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140 keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT-based method. The impact of different scan and reconstruction parameters was also investigated.Results: The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT-based methods for SPR prediction, as these methods could not be directly implemented on a fast kV-switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT-based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone.Conclusions: The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT-based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR-V, reconstruction kernels, and dose levels.
|
|
| 7. |
- Vu, Minh H., et al.
(författare)
-
Evaluation of multislice inputs to convolutional neural networks for medical image segmentation
- 2020
-
Ingår i: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 47:12, s. 6216-6231
-
Tidskriftsartikel (refereegranskat)abstract
- Purpose: When using convolutional neural networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice [two-dimensional (2D)] or whole volumes [three-dimensional (3D)]. One common alternative, in this study denoted as pseudo-3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost.Methods: In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo-3D approach as a function of the number of input slices, and compare the results to conventional end-to-end 2D and 3D CNNs, and to triplanar orthogonal 2D CNNs. The standard pseudo-3D method regards the neighboring slices as multiple input image channels. We additionally design and evaluate a novel, simple approach where the input stack is a volumetric input that is repeatably convolved in 3D to obtain a 2D feature map. This 2D map is in turn fed into a standard 2D network. We conducted experiments using two different CNN backbone architectures and on eight diverse data sets covering different anatomical regions, imaging modalities, and segmentation tasks.Results: We found that while both pseudo-3D methods can process a large number of slices at once and still be computationally much more efficient than fully 3D CNNs, a significant improvement over a regular 2D CNN was only observed with two of the eight data sets. triplanar networks had the poorest performance of all the evaluated models. An analysis of the structural properties of the segmentation masks revealed no relations to the segmentation performance with respect to the number of input slices. A post hoc rank sum test which combined all metrics and data sets yielded that only our newly proposed pseudo-3D method with an input size of 13 slices outperformed almost all methods.Conclusion: In the general case, multislice inputs appear not to improve segmentation results over using 2D or 3D CNNs. For the particular case of 13 input slices, the proposed novel pseudo-3D method does appear to have a slight advantage across all data sets compared to all other methods evaluated in this work.
|
|
| 8. |
- Sjogren, R, et al.
(författare)
-
Influence of electron contamination on in vivo surface dosimetry for high-energy photon beams
- 1998
-
Ingår i: Medical physics (Lancaster). - 0094-2405. ; 25:6, s. 916-921
-
Tidskriftsartikel (refereegranskat)abstract
- The influence of the electron contamination at in vivo dosimetry with diodes on the patient surface has been investigated by introducing different accessories in the beam path and by changing the field size and SSD. The results show a clear correlation between the electron contamination at an effective measuring depth of the diode and the signal from the patient diode. When the electron contamination is taken into account the agreement between the diode values and the absorbed dose is greatly improved. More accurate in vivo dosimetry with less error margins is therefore possible if better predictions of the electron contamination in high-energy photon beams can be performed. (C) 1998 American Association of Physicists in Medicine. [S0094-2405(98)00606-3].
|
|
| 9. |
- Sjogren, R, et al.
(författare)
-
Methods for the determination of effective monitor chamber thickness
- 1999
-
Ingår i: Medical physics (Lancaster). - 0094-2405. ; 26:9, s. 1871-1873
-
Tidskriftsartikel (refereegranskat)abstract
- There are a number of models, both analytical and Monte Carlo, which are used to describe the fluence from the treatment head of accelerators. One common problem in these simulations is to find relevant information about details in the treatment head. A complex unit in the treatment head for which reliable data is seldom given is the monitor chamber. In this work two methods are described for obtaining this information by analyzing the increased scattering of an electron beam when the monitor chamber is introduced in the beam. It was found that the effective thickness of the electrodes in a monitor chamber can be determined with sufficient accuracy by using experimental results combined with Fermi-Eyges theory or Monte Carlo simulations. (C) 1999 American Association of Physicists in Medicine. [S0094-2405(99)01009-3].
|
|
| 10. |
- Karlsson, M G, et al.
(författare)
-
Treatment head design for multileaf collimated high-energy electrons
- 1999
-
Ingår i: Medical physics (Lancaster). - 0094-2405. ; 26:10, s. 2161-2167
-
Tidskriftsartikel (refereegranskat)abstract
- This paper describes how a conventional treatment head can be modified for use of multileaf collimated electron beams. Automatic and dynamic beam delivery are possible for both electrons and photons by using the computer controlled multileaf collimator (MLC) for both photon and electron beams. Thereby, the electron beams can be mixed more freely into the treatment to take advantage of the specific depth modulation characteristics of electrons. The investigation was based on Monte Carlo calculations using the software package BEAM. The physical parameters used in this optimization were the beam penumbra and the virtual/effective point source position. These parameters are essential for shaping beams, beam matching and for dosimetry calculations. The optimization was carried out by modifying a number of parameters: replacing the air atmosphere in the treatment head with helium, adding a helium bag below the MLC, changing the position of the scattering foils, modifying the monitor chamber, and adjusting the position of the MLC. The beam characteristics for some of these designs were found to fulfil our criteria for clinically useful beams down to at least 9 MeV. (C) 1999 American Association of Physicists in Medicine. [S0094-2405(99)00610-0].
|
|
