Zeitschrift fur Medizinische Physik最新文献

Purpose

The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution.

Methods

Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich’s theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins.

Results

Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20–33, 33–47, 47–58, 58–69, 69–80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image.

Conclusions

A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

Purpose

In robotic stereotactic radiosurgery (SRS), optimal selection of collimators from a set of fixed cones must be determined manually by trial and error. A unique and uniformly scaled metric to characterize plan quality could help identify Pareto-efficient treatment plans.

Methods

The concept of dose-area product (DAP) was used to define a measure (DAP_ratio) of the targeting efficiency of a set of beams by relating the integral DAP of the beams to the mean dose achieved in the target volume. In a retrospective study of five clinical cases of brain metastases with representative target volumes (range: 0.5–5.68 ml) and 121 treatment plans with all possible collimator choices, the DAP_ratio was determined along with other plan metrics (conformity index CI, gradient index R50%, treatment time, total number of monitor units TotalMU, radiotoxicity index f12, and energy efficiency index η50%), and the respective Spearman's rank correlation coefficients were calculated. The ability of DAP_ratio to determine Pareto efficiency for collimator selection at DAP_ratio < 1 and DAP_ratio < 0.9 was tested using scatter plots.

Results

The DAP_ratio for all plans was on average 0.95 ± 0.13 (range: 0.61–1.31). Only the variance of the DAP_ratio was strongly dependent on the number of collimators. For each target, there was a strong or very strong correlation of DAP_ratio with all other metrics of plan quality. Only for R50% and η50% was there a moderate correlation with DAP_ratio for the plans of all targets combined, as R50% and η50% strongly depended on target size. Optimal treatment plans with CI, R50%, f12, and η50% close to 1 were clearly associated with DAP_ratio < 1, and plans with DAP_ratio < 0.9 were even superior, but at the cost of longer treatment times and higher total monitor units.

Conclusions

The newly defined DAP_ratio has been demonstrated to be a metric that characterizes the target efficiency of a set of beams in robotic SRS in one single and uniformly scaled number. A DAP_ratio < 1 indicates Pareto efficiency. The trade-off between plan quality on the one hand and short treatment time or low total monitor units on the other hand is also represented by DAP_ratio.

Purpose

A new insert for a commercially available end-to-end test phantom was designed and in-house manufactured by 3D printing. Subsequently, the insert was tested for different stereotactic radiation therapy workflows (SRS, SBRT, FSRT, and Multimet) also in comparison to the original insert.

Material and methods

Workflows contained imaging (MR, CT), treatment planning, positioning, and irradiation. Positioning accuracy was evaluated for non-coplanar x-ray, kV- and MV-CBCT systems, as well as surface guided radiation therapy. Dosimetric accuracy of the irradiation was measured with an ionization chamber at four different linear accelerators including dynamic tumor tracking for SBRT.

Results

CT parameters of the insert were within the specification. For MR images, the new insert allowed quantitative analysis of the MR distortion. Positioning accuracy of the phantom with the new insert using the imaging systems of the different linacs was < 1 mm/degree also for MV-CBCT and a non-coplanar imaging system which caused > 3 mm deviation with the original insert. Deviation of point dose values was < 3% for SRS, FSRT, and SBRT for both inserts. For the Multimet plans deviations exceeded 10% because the ionization chamber was not positioned in each metastasis, but in the center of phantom and treatment plan.

Conclusion

The in-house manufactured insert performed well in all steps of four stereotactic treatment end-to-end tests. Advantages over the commercially available alternative were seen for quantitative analysis of deformation correction in MR images, applicability for non-coplanar x-ray imaging, and dynamic tumor tracking.

Purpose

The Type A standard uncertainty in Monte Carlo (MC) dose calculations is usually determined using the “history by history” method. Its applicability is based on the assumption that the central limit theorem (CLT) can be applied such that the dispersion of repeated calculations can be modeled by a Normal distribution. The justification for this assumption, however, is not obvious. The concept of stochastic quantities used in the field of microdosimetry offers an alternative approach to assess uncertainty. This leads to a new and simple expression.

Methods

The value of the MC determined absorbed dose is considered a random variable which is comparable to the stochastic quantity specific energy, z. This quantity plays an important role in microdosimetry and in the definition of the quantity absorbed dose, D. One of the main features of z is that it is itself the product of two other random variables, specifically of the mean dose contribution in a ‘single event’ and of the mean number of such events. The term ‘single event’ signifies the sum of energies imparted by all correlated particles to the matter in a given volume. The similarity between the MC calculated absorbed dose and the specific energy is used to establish the ‘event by event’ method for the determination of the uncertainty. MC dose calculations were performed to test and compare both methods.

Results

It is shown that the dispersion of values obtained by MC dose calculations indeed depend on the product of the mean absorbed dose per event, and the number of events. Applying methods to obtain the variance of a product of two random variables, a simple formula for the assessment of uncertainties is obtained which is slightly different from the ‘history by history’ method. Interestingly, both formulas yield indistinguishable results. This finding is attributed to the large number of histories used in MC simulations. Due to the fact that the values of a MC calculated absorbed dose are the product of two approximately Normal distributions it can be demonstrated that the resulting product is also approximately normally distributed.

Conclusions

The event by event approach appears to be more suitable than the history by history approach because it takes into account the randomness of the number of events involved in MC dose calculations. Under the condition of large numbers of histories, however, both approaches lead to the same simple expression for the determination of uncertainty in MC dose calculations. It is suggested to replace the formula currently used by the new expression. Finally, it turned out that the concept and ideas that were developed in the field of microdosimetry already 50 years ago can be usefully applied also in MC calculations.

This work proposes a method for automatic standardized assessment of bone marrow volume and spatial distribution of the proton density fat fraction (PDFF) in vertebral bodies. Intra- and interindividual variability in size and shape of vertebral bodies is a challenge for comparable interindividual evaluation and monitoring of changes in the composition and distribution of bone marrow due to aging and/or intervention. Based on deep learning image segmentation, bone marrow PDFF of single vertebral bodies is mapped to a cylindrical template and corrected for the inclination with respect to the horizontal plane. The proposed technique was applied and tested in a cohort of 60 healthy (30 males, 30 females) individuals. Obtained bone marrow volumes and mean PDFF values are comparable to former manual and (semi-)automatic approaches. Moreover, the proposed method allows shape-independent characterization of the spatial PDFF distribution inside vertebral bodies.

Before introducing new treatment techniques, an investigation of hazards due to unintentional radiation exposures is a reasonable activity for proactively increasing patient safety. As dedicated software is scarce, we developed a tool for risk assessment to design a quality management program based on best practice methods, i.e., process mapping, failure modes and effects analysis and fault tree analysis. Implemented as a web database application, a single dataset was used to describe the treatment process and its failure modes. The design of the system and dataset allowed failure modes to be represented both visually as fault trees and in a tabular form. Following the commissioning of the software for our department, previously conducted risk assessments were migrated to the new system after being fully re-assessed which revealed a shift in risk priorities. Furthermore, a weighting factor was investigated to bring risk levels of the migrated assessments into perspective. The compensation did not affect high priorities but did re-prioritize in the midrange of the ranking. We conclude that the tool is suitable to conduct multiple risk assessments and concomitantly keep track of the overall quality management activities.

Purpose

The first aim of the study was to create a general template for analyzing potential failures in external beam radiotherapy, EBRT, using the process failure mode and effects analysis (PFMEA). The second aim was to modify the action priority (AP), a novel prioritization method originally introduced by the Automotive Industry Action Group (AIAG), to work with different severity, occurrence, and detection rating systems used in radiation oncology.

Methods and materials

The AIAG PFMEA approach was employed in combination with an extensive literature survey to develop the EBRT-PFMEA template. Subsets of high-risk failure modes found through the literature survey were added to the template where applicable. Our modified AP for radiation oncology (RO AP) was defined using a weighted sum of severity, occurrence, and detectability. Then, Monte Carlo simulations were conducted to compare the original AIAG AP, the RO AP, and the risk priority number (RPN). The results of the simulations were used to determine the number of additional corrective actions per failure mode and to parametrize the RO AP to our department’s rating system.

Results

An EBRT-PFMEA template comprising 75 high-risk failure modes could be compiled. The AIAG AP required 1.7 additional corrective actions per failure mode, while the RO AP ranged from 1.3 to 3.5, and the RPN required 3.6. The RO AP could be parametrized so that it suited our rating system and evaluated severity, occurrence, and detection ratings equally to the AIAG AP.

Conclusions

An adjustable EBRT-PFMEA template is provided which can be used as a practical starting point for creating institution-specific templates. Moreover, the RO AP introduces transparent action levels that can be adapted to any rating system.

Purpose

To maximize acquisition bandwidth in zero echo time (ZTE) sequences, readout gradients are already switched on during the RF pulse, creating unwanted slice selectivity. The resulting image distortions are amplified especially when the anatomy of interest is not located at the isocenter. We aim to characterize off-center ZTE MRI of extremities such as the shoulder, knee, and hip, adjusting the carrier frequency of the RF pulse excitation for each TR.

Methods

In ZTE MRI, radial encoding schemes are used, where the distorted slice profile due to the finite RF pulse length rotates with the k-space trajectory. To overcome these modulations for objects far away from the magnet isocenter, the frequency of the RF pulse is shifted for each gradient setting so that artifacts do not occur at a given off-center target position. The sharpness of the edges in the images were calculated and the ZTE acquisition with off-center excitation was compared to an acquisition with isocenter excitation both in phantom and in vivo off-center MRI of the shoulder, knee, and hip at 1.5 and 3T MRI systems.

Results

Distortion and blurriness artifacts on the off-center MRI images of the phantom, in vivo shoulder, knee, and hip images were mitigated with off-center excitation without time or noise penalty, at no additional computational cost.

Conclusion

The off-center excitation allows ZTE MRI of the shoulder, knee, and hip for high-bandwidth image acquisitions for clinical settings, where positioning at the isocenter is not possible.

Purpose

Personalized treatment planning in Molecular Radiotherapy (MRT) with accurately determining the absorbed dose is highly desirable. The absorbed dose is calculated based on the Time-Integrated Activity (TIA) and the dose conversion factor. A crucial unresolved issue in MRT dosimetry is which fit function to use for the TIA calculation. A data-driven population-based fitting function selection could help solve this problem. Therefore, this project aims to develop and evaluate a method for accurately determining TIAs in MRT, which performs a Population-Based Model Selection within the framework of the Non-Linear Mixed-Effects (NLME-PBMS) model.

Methods

Biokinetic data of a radioligand for the Prostate-Specific Membrane Antigen (PSMA) for cancer treatment were used. Eleven fit functions were derived from various parameterisations of mono-, bi-, and tri-exponential functions. The functions' fixed and random effects parameters were fitted (in the NLME framework) to the biokinetic data of all patients. The goodness of fit was assumed acceptable based on the visual inspection of the fitted curves and the coefficients of variation of the fitted fixed effects. The Akaike weight, the probability that the model is the best among the whole set of considered models, was used to select the fit function most supported by the data from the set of functions with acceptable goodness of fit. NLME-PBMS Model Averaging (MA) was performed with all functions having acceptable goodness of fit. The Root-Mean-Square Error (RMSE) of the calculated TIAs from individual-based model selection (IBMS), a shared-parameter population-based model selection (SP-PBMS) reported in the literature, and the functions from NLME-PBMS method to the TIAs from MA were calculated and analysed. The NLME-PBMS (MA) model was used as the reference as this model considers all relevant functions with corresponding Akaike weights.

Results

The function $f_{3a}=A_1{e}^{-\left({\lambda }_1+{\lambda }_{\mathrm{phys}}\right)t}+A_2{e}^{-\left({\lambda }_{\mathrm{phys}}\right)t}$ was selected as the function most supported by the data with an Akaike weight of (54 ± 11) %. Visual inspection of the fitted graphs and the RMSE values show that the NLME model selection method has a relatively better or equivalent performance than the IBMS or SP-PBMS methods. The RMSEs of the IBMS, SP-PBMS, and NLME-PBMS ($f_{3a}$) methods are 7.4%, 8.8%, an