ISSN / EISSN : 0094-2405 / 2473-4209
Published by: Wiley (10.1002)
Total articles ≅ 40,630
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Medical Physics; doi:10.1002/mp.15088
Purpose The MetisTM PET/CT is a self-developed, silicon photomultiplier (SiPM) detector-based, rodent PET/CT system. The objective of this study was to evaluate the performance of the system using the National Electrical Manufacturers Association (NEMA) NU 4–2008 standard protocol. Methods Energy resolution, spatial resolution, sensitivity, scatter fraction (SF), noise-equivalent count rate (NECR), and image quality characteristics were measured. A micro Derenzo phantom experiment was performed to evaluate the spatial resolution using three-dimensional ordered-subsets expectation maximization (3D-OSEM) and maximum likelihood expectation maximization (MLEM) reconstructed images. In addition, the CT imaging agent Ioverol 350 was mixed with fluorine-18 (18F)-fluorodeoxyglucose (FDG) and then injected into the micro Derenzo phantom to evaluate the PET/CT imaging. In vivo PET/CT imaging studies were also conducted in a healthy mouse and rat using 18F-FDG. Results The mean energy resolution of the system was 15.3%. The tangential resolution was 0.82 mm full-width half-maximum (FWHM) at the center of the field of the view (FOV), and the radial and axial resolution were generally lower than 2.0 mm FWHM. The spatial resolution was significantly improved when using 3D-OSEM, especially the axial FWHM could be improved by up to about 57%. The system absolute sensitivity was 7.7% and 6.8% for an energy window of 200-750 keV and 350-750 keV, respectively. The scatter fraction was 8.2% and 12.1% for the mouse- and rat-like phantom, respectively. The peak NECR was 1343.72 kcps at 69 MBq and 640.32 kcps at 53 MBq for the mouse- and rat-like phantom, respectively. The 1-mm fillable rod in the image quality phantom can be clearly observed. We can identify the 0.6-mm aperture of the micro Derenzo phantom image clearly using 3D-OSEM (10 subsets, 5 iterations). We also performed the fusion of the PET and CT images of the mouse and the brain imaging of the rat. Conclusions The results show that the system has the characteristics of high-resolution, high-sensitivity and excellent image quality and is suitable for rodent imaging-based research.
Medical Physics; doi:10.1002/mp.15084
Purpose The SYRMA-3D collaboration is setting up a breast computed tomography (bCT) clinical program at the Elettra synchrotron radiation facility in Trieste, Italy. Unlike the few dedicated scanners available at hospitals, synchrotron radiation bCT requires the patient's rotation, which in turn implies a long scan duration (from tens of seconds to few minutes). At the same time, it allows the achievement of high spatial resolution. These features make synchrotron radiation bCT prone to motion artifacts. This article aims at assessing and compensating for motion artifacts through an optical tracking approach. Methods In this study, patients' movements due to breathing have been first assessed on 7 volunteers and then simulated during the CT scans of a breast phantom and a surgical specimen, by adding a periodic oscillatory motion (constant speed, 1 mm amplitude, 12 cycles/minute). CT scans were carried out at 28 keV with a mean glandular dose of 5 mGy. Motion artifacts were evaluated and a correction algorithm based on the optical tracking of fiducial marks was introduced. A quantitative analysis based on the structural similarity (SSIM) index and the normalized mean square error (nMSE) was performed on the reconstructed CT images. Results CT images reconstructed through the optical tracking procedure were found to be as good as the motionless reference image. Moreover, the analysis of SSIM and nMSE demonstrated that an uncorrected motion of the order of the system's point spread function (around 0.1 mm in the present case) can be tolerated. Conclusions Results suggest that a motion correction procedure based on an optical tracking system would be beneficial in synchrotron-radiation breast CT.
Medical Physics; doi:10.1002/mp.15089
Purpose Clinically, single radiotracer positron emission tomography (PET) imaging is a commonly used examination method; however, since each radioactive tracer reflects the information of only one kind of cell, it easily causes false negatives or false positives in disease diagnosis. Therefore, reasonably combining two or more radiotracers is recommended to improve the accuracy of diagnosis and the sensitivity and specificity of the disease when conditions permit. Methods This paper proposes incorporating 18F-fluorodeoxyglucose (FDG) as a higher-quality PET image to guide the reconstruction of other lower-count 11C-methionine (MET) PET datasets to compensate for the lower image quality by using a popular kernel algorithm. Specifically, the FDG prior is needed to extract kernel features, and these features were used to build a kernel matrix using a k-nearest-neighbor (kNN) search for MET image reconstruction. We created a 2-D brain phantom to validate the proposed method by simulating sinogram data containing Poisson random noise and quantitatively compared the performance of the proposed FDG-guided kernelized expectation maximization (KEM) method with the performance of Gaussian and non-local means (NLM) smoothed maximum likelihood expectation maximization (MLEM), MR-guided KEM and multi-guided-S KEM algorithms. Mismatch experiments between FDG/MR and MET data were also carried out to investigate the outcomes of possible clinical situations. Results In the simulation study, the proposed method outperformed the other algorithms by at least 3.11% in the signal-to-noise ratio (SNR) and 0.68% in the contrast recovery coefficient (CRC), and it reduced the mean absolute error (MAE) by 8.07%. Regarding the tumor in the reconstructed image, the proposed method contained more pathological information. Furthermore, the proposed method was still superior to the MR-guided KEM method in the mismatch experiments. Conclusions The proposed FDG-guided KEM algorithm can effectively utilize and compensate for the tissue metabolism information obtained from dual-tracer PET to maximize the advantages of PET imaging.
Medical Physics; doi:10.1002/mp.15045
Purpose There is a growing trend towards the adoption of model-based calculation algorithms (MBDCAs) for brachytherapy dose calculations which can properly handle media and source/applicator heterogeneities. However, most of dose calculations in ocular plaque therapy are based on homogeneous water media and standard in-silico ocular phantoms, ignoring non-water equivalency of the anatomic tissues and heterogeneities in applicators and patient anatomy. In this work, we introduce EyeMC, a Monte Carlo (MC) model-based calculation algorithm for ophthalmic plaque brachytherapy using realistic and adaptable patient-specific eye geometries and materials. Methods We used the MC code PENELOPE in EyeMC to model Bebig IsoSeed I25.S16 seeds in COMS plaques and 106Ru/106Rh applicators that are coupled onto a customizable eye model with realistic geometry and composition. To significantly reduce calculation times, we integrated EyeMC with CloudMC, a cloud computing platform for radiation therapy calculations. EyeMC is equipped with an evaluation module that allows the generation of isodose distributions, dose–volume histograms, and comparisons with Plaque Simulator three-dimensional dose distribution. We selected a sample of patients treated with 125I and 106Ru isotopes in our institution, covering a variety of different type of plaques, tumor sizes, and locations. Results from EyeMC were compared to the original plan calculated by the TPS Plaque Simulation, studying the influence of heterogeneous media composition as well. Results EyeMC calculations for Ru plaques agreed well with manufacturer’s reference data and data of MC simulations from Hermida et al. (2013). Significant deviations, up to 20%, were only found in lateral profiles for notched plaques. As expected, media composition significantly affected estimated doses to different eye structures, especially in the 125I cases evaluated. Dose to sclera and lens were found to be about 12% lower when considering real media, while average dose to tumor was 9% higher. 106Ru cases presented a 1%–3% dose reduction in all structures using real media for calculation, except for the lens, which showed an average dose 7.6% lower than water-based calculations. Comparisons with Plaque Simulator calculations showed large differences in dose to critical structures for 106Ru notched plaques. 125I cases presented significant and systematic dose deviations when using the default calculation parameters from Plaque Simulator version 5.3.8., which were corrected when using calculation parameters from a custom physics model for carrier-attenuation and air-interface correction functions. Conclusions EyeMC is a MC calculation system for ophthalmic brachytherapy based on a realistic and customizable eye-tumor model which includes the main eye structures with their real composition. Integrating this tool into a cloud computing environment allows to perform high-precision MC calculations of ocular plaque treatments in short times. The observed variability in eye anatomy among the selected cases justifies the use of patient-specific models.
Medical Physics; doi:10.1002/mp.15047
The American Board of Radiology offers certification in three specialties of medical physics: Therapeutic Medical Physics, Diagnostic Medical Physics, and Nuclear Medical Physics. Of these specialties, medical nuclear physics has the fewest active diplomates, only a few hundred. The diagnostic medical physics specialty certification incudes a variety of modalities (ultrasound, radiography, computed tomography, and magnetic resonance imaging) yet does not address nuclear medicine imaging or therapy. This separation dates to the beginning of the ABR certification process for medical physicists in 1947; originally there were three certificates available: X-ray and Radium Physics, Medical Nuclear Physics and, as combination of these two, Radiological Physics. Over the span of 75 years since the Medical Nuclear Physics certification was created, much has changed in the scope and proliferation of the nuclear medicine endeavor and the question arises as to the need for change in the preparation process for medical physicists in the field. I offer thanks to our contributors and note that they are writing in the classic style of a debate, the opinions that they argue may or may not reflect their personal views.
Medical Physics; doi:10.1002/mp.15083
Purpose In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response. Methods Using a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered planning CT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask 3D deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality planning CT-like images. Results We compared the synthetic CT and OAR segmentations generated by the model to real planning CT and manual OAR segmentations and showed promising results. The real week 1 (base-line) CBCT images which had an average MAE of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D organs-at-risk segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83 and esophagus 0.66. Conclusions We demonstrate an approach to translate artifact-ridden CBCT images to high quality synthetic CT images while simultaneously generating good quality segmentation masks for different organs-at-risk. This approach could allow clinicians to adjust treatment plans using only the routine low-quality CBCT images, potentially improving patient outcomes. Our code, data, and pre-trained models will be made available via our physics-based data augmentation library, Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.
Medical Physics; doi:10.1002/mp.15075
Purpose Radiotherapy presents unique challenges and clinical requirements for longitudinal tumor and organ-at-risk (OAR) prediction during treatment. The challenges include tumor inflammation/edema and radiation-induced changes in organ geometry, whereas the clinical requirements demand exibility in input/output sequence timepoints to update the predictions on rolling basis and the grounding of all predictions in relationship to the pre-treatment imaging information for response and toxicity assessment in adaptive radiotherapy. Methods To deal with the aforementioned challenges and to comply with the clinical requirements, we present a novel 3D sequence-to-sequence model based on Convolution Long Short Term Memory (ConvLSTM) that makes use of series of deformation vector fields (DVF) between individual timepoints and reference pre-treatment/planning CTs to predict future anatomical deformations and changes in gross tumor volume as well as critical OARs. High-quality DVF training data is created by employing hyper-parameter optimization on the subset of the training data with DICE coefficient and mutual information metric. We validated our model on two radiotherapy datasets: a publicly available head-and-neck dataset (28 patients with manually contoured pre-, mid-, and post-treatment CTs), and an internal non-small cell lung cancer dataset (63 patients with manually contoured planning CT and 6 weekly CBCTs). Results The use of DVF representation and skip connections overcomes the blurring issue of ConvL-STM prediction with the traditional image representation. The mean and standard deviation of DICE for predictions of lung GTV at week 4, 5, and 6 were 0.83±0.09, 0.82±0.08, and 0.81±0.10, respectively, and for post-treatment ipsilateral and contralateral parotids, were 0.81±0.06 and 0.85±0.02. Conclusion We presented a novel DVF based Seq2Seq model for medical images, leveraging the complete 3D imaging information of a relatively large longitudinal clinical dataset, to carry out longitudinal GTV/OAR predictions for anatomical changes in HN and lung radiotherapy patients, which has potential to improve RT outcomes.
Medical Physics; doi:10.1002/mp.15082
Purpose The aim of this paper is to propose a fracture model for human ribs based on Acoustic Emission (AE) data. The accumulation of micro-cracking until a macroscopic crack is produced can be monitored by AE. This macro-crack propagation causes the loss of the structural integrity of the rib. Methods The AE technique was used in in-vitro bending tests of human ribs. The AE data obtained were used to construct a quantitative model that allows an estimation of the failure stress from the signals detected. The model predicts the ultimate stress with an error of less than 3.5% (even at stresses 15% lower than failure stress), which makes it possible to safely anticipate the failure of the rib. Results The Percolation Theory was used to model crack propagation. Moreover, a quantitative probability-based model for the expected number of AE signals has been constructed, incorporating some ideas of percolation theory. The model predicts that AE signals associated with micro-failures should exhibit a vertical asymptote when stress increases. The occurrence of this vertical asymptote was attested in our experimental observations. The total number of micro-failures detected prior to the failure is N ≈ 100 and the ultimate stress is σ∞ = 197 + 62 MPa. A significant correlation (p < 0.0001) between σ∞ and the predicted value is found, using only the first N = 30 micro-failures (correlation improves for N higher). Conclusions The measurements and the shape of the curves predicted by the model fit well. In addition, the model parameters seem to explain quantitatively and qualitatively the distribution of the AE signals as the material approaches the macroscopic fracture. Moreover, some of these parameters correlate with anthropometric variables, such as age or BMI. The proposed model could be used to predict the structural failure of ribs subjected to bending.
Medical Physics; doi:10.1002/mp.14996
The past decade has seen the increasing integration of magnetic resonance (MR) imaging into radiation therapy (RT). This growth can be contributed to multiple factors, including hardware and software advances that have allowed the acquisition of high-resolution volumetric data of RT patients in their treatment position (also known as MR simulation) and the development of methods to image and quantify tissue function and response to therapy. More recently, the advent of MR-guided radiation therapy (MRgRT) - achieved through the integration of MR imaging systems and linear accelerators - has further accelerated this trend. As MR imaging in RT techniques and technologies, such as MRgRT, gain regulatory approval worldwide, these systems will begin to propagate beyond tertiary care academic medical centers and into more community-based health systems and hospitals, creating new opportunities to provide advanced treatment options to a broader patient population. Accompanying these opportunities are unique challenges related to their adaptation, adoption, and use including modification of hardware and software to meet the unique and distinct demands of MR imaging in RT, the need for standardization of imaging techniques and protocols, education of the broader RT community (particularly in regards to MR safety) as well as the need to continue and support research, and development in this space. In response to this, an ad hoc committee of the American Association of Physicists in Medicine (AAPM) was formed to identify the unmet needs, roadblocks, and opportunities within this space. The purpose of this document is to report on the major findings and recommendations identified. Importantly, the provided recommendations represent the consensus opinions of the committee’s membership, which were submitted in the committee's report to the AAPM Board of Directors. In addition, AAPM ad hoc committee reports differ from AAPM task group reports in that ad hoc committee reports are neither reviewed nor ultimately approved by the committee’s parent groups, including at the council and executive committee level. Thus, the recommendations given in this summary should not be construed as being endorsed by or official recommendations from the AAPM.
Medical Physics; doi:10.1002/mp.15077
Purpose Recently, high-precision radiotherapy systems have been developed by integrating computerized tomography or magnetic resonance imaging to enhance the precision of radiotherapy. For integration with additional imaging systems in a limited space, miniaturization and weight reduction of the linear accelerator (linac) system have become important. The aim of this work is to develop a compact medical linac based on 9.3 GHz X-band RF technology instead of the S-band RF technology typically used in the radiotherapy field. Methods The accelerating tube was designed by using 3D finite-difference time-domain and particle-in-cell simulations because the frequency variation resulting from the structural parameters and processing errors is relatively sensitive to the operation performance of the X-band linac. Through 3D simulation of the electric field distribution and beam dynamics process, we designed an accelerating tube to efficiently accelerate the electron beam and used a magnetron as the RF source to miniaturize the entire linac. In addition, a side-coupled structure was adopted to design a compact linac to reduce the RF power loss. To verify the performance of the linac, we developed a beam diagnostic system to analyze the electron beam characteristics and a quality assurance (QA) experimental environment including 3D lateral water phantoms to analyze the primary performance parameters (energy, dose rate, flatness, symmetry, and penumbra) The QA process was based on the standard protocols AAPM TG-51, 106, 142 and IAEA TRS-398. Results The X-band linac has high shunt impedance and electric field strength. Therefore, even though the length of the accelerating tube is 37 cm, the linac could accelerate an electron beam to more than 6 MeV and produce a beam current of more than 90 mA. The transmission ratio measured to be approximately 30 ~ 40 % when the electron gun operates in the constant emission region. The percent depth dose ratio at the measured depths of 10 cm and 20 cm was approximately 0.572, so we verified that the photon beam energy was matched to approximately 6 MV. The maximum dose rate was measured as 820 cGy/min when the source-to-skin distance was 80 cm. The symmetry was smaller than the QA standard and the flatness had a higher than standard value due to the flattening filter-free beam characteristics. In the case of the penumbra, it was not sufficiently steep compared to commercial equipment, but it could be compensated by improving additional devices such as multileaf collimator and jaw. Conclusions A 9.3 GHz X-band medical linac was developed for high-precision radiotherapy. Since a more precise design and machining process are required for X-band RF technology, this linac was developed by performing a 3D simulation and ultraprecision machining. The X-band linac has a short length and a compact volume, but it can generate a validated therapeutic beam. Therefore, it has more flexibility to be coupled with imaging systems such as CT or MRI and can reduce the bore size of the gantry. In addition, the reduction in weight can improve the mechanical stiffness of the unit and reduce the mechanical load.