CT Simulation Research Articles

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network

To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images. A 3-dimensional (3D) convolutional neural network using inputs from diagnostic PETs and simulation CTs was constructed with 2 parallel convolution paths for independent feature extraction at multiple resolution levels and a single deconvolution path. At each resolution level, the extracted features from the convolution arms were concatenated and fed through the skip connections into the deconvolution path that produced the tumor segmentation. Our network was trained/validated/tested by a 3:1:1 split on 290 pairs of PET and CT images from patients with lung cancer treated at our clinic, with manual physician contours as the ground truth. A stratified training strategy based on the magnitude of the gross tumor volume (GTV) was investigated to improve performance, especially for small tumors. Multiple radiation oncologists assessed the clinical acceptability of the network-produced segmentations. The mean Dice similarity coefficient, Hausdorff distance, and bidirectional local distance comparing manual versus automated contours were 0.79 ± 0.10, 5.8 ± 3.2 mm, and 2.8 ± 1.5 mm for the unstratified 3D dual-modality model. Stratification delivered the best results when the model for the large GTVs (>25 mL) was trained with all-size GTVs and the model for the small GTVs (<25 mL) was trained with small GTVs only. The best combined Dice similarity coefficient, Hausdorff distance, and bidirectional local distance from the 2 stratified models on their corresponding test data sets were 0.83 ± 0.07, 5.9 ± 2.5 mm, and 2.8 ± 1.4 mm, respectively. In the multiobserver review, 91.25% manual versus 88.75% automatic contours were accepted or accepted with modifications. By using an expansive clinical PET and CT image database and a dual-modality architecture, the proposed 3D network with a novel GTVbased stratification strategy generated clinically useful lung cancer contours that were highly acceptable on physician review.

Improving reproducibility and performance of radiomics in low-dose CT using cycle GANs.

BackgroundAs a means to extract biomarkers from medical imaging, radiomics has attracted increased attention from researchers. However, reproducibility and performance of radiomics in low‐dose CT scans are still poor, mostly due to noise. Deep learning generative models can be used to denoise these images and in turn improve radiomics’ reproducibility and performance. However, most generative models are trained on paired data, which can be difficult or impossible to collect.PurposeIn this article, we investigate the possibility of denoising low‐dose CTs using cycle generative adversarial networks (GANs) to improve radiomics reproducibility and performance based on unpaired datasets.Methods and materialsTwo cycle GANs were trained: (1) from paired data, by simulating low‐dose CTs (i.e., introducing noise) from high‐dose CTs and (2) from unpaired real low dose CTs. To accelerate convergence, during GAN training, a slice‐paired training strategy was introduced. The trained GANs were applied to three scenarios: (1) improving radiomics reproducibility in simulated low‐dose CT images and (2) same‐day repeat low dose CTs (RIDER dataset), and (3) improving radiomics performance in survival prediction. Cycle GAN results were compared with a conditional GAN (CGAN) and an encoder–decoder network (EDN) trained on simulated paired data.ResultsThe cycle GAN trained on simulated data improved concordance correlation coefficients (CCC) of radiomic features from 0.87 (95%CI, [0.833,0.901]) to 0.93 (95%CI, [0.916,0.949]) on simulated noise CT and from 0.89 (95%CI, [0.881,0.914]) to 0.92 (95%CI, [0.908,0.937]) on the RIDER dataset, as well improving the area under the receiver operating characteristic curve (AUC) of survival prediction from 0.52 (95%CI, [0.511,0.538]) to 0.59 (95%CI, [0.578,0.602]). The cycle GAN trained on real data increased the CCCs of features in RIDER to 0.95 (95%CI, [0.933,0.961]) and the AUC of survival prediction to 0.58 (95%CI, [0.576,0.596]).ConclusionThe results show that cycle GANs trained on both simulated and real data can improve radiomics’ reproducibility and performance in low‐dose CT and achieve similar results compared to CGANs and EDNs.

Clinical experience of volumetric-modulated flattening filter free stereotactic body radiation therapy of lesions in the lung with deep inspiration breath-hold.

This clinical study aimed to evaluate lung cancer patients’ ability to perform deep inspiration breath‐hold (DIBH) during CT simulation and throughout the treatment course of stereotactic body radiation therapy (SBRT). In addition, target sizes, organ at risk (OAR) sizes, and doses to the respective volumes in filter‐free volumetric‐modulated arc therapy plans performed under free‐breathing (FB) and DIBH conditions were evaluated. Twenty‐one patients with peripheral lesions were included, of which 13 were eligible for SBRT. All patients underwent training for breath‐hold during CT, and if they complied with the requirements, two CT scans were obtained: CT scan in DIBH and a four‐dimensional CT scan in FB. The treatment plans in FB and DIBH were generated, and the dose parameters and volume sizes were compared. The endpoints for evaluation were patient compliance, target dose coverage, and doses to the OARs. This clinical study showed high patient DIBH compliance during both CT simulation and treatment for patients with lung cancer. A significant reduction in target volumes was achieved with SBRT in DIBH, in addition to significantly decreased doses to the heart, chest wall, and lungs. DIBH in SBRT of lung lesions is feasible, and a routine to manage intra‐fractional deviation should be established upon implementation.

Automated coronary artery calcium scoring in patients with breast cancer to assess the risk of heart disease following adjuvant radiation therapy

AimValidation of coronary artery calcium (CAC) scores as prognostic factors of acute coronary events (ACE) development in breast cancer patients are demanded. We investigated prognostic impact of CAC on ACE development with cardiac exposure to radiation. MethodsWe evaluated breast cancer patients with (n = 511) or without (n = 600) adjuvant radiotherapy (RT) between 2005 and 2013. CAC Agatston scores were analyzed using a deep-learning-based algorithm. Individual mean heart dose (MHD) was calculated, and no RT was categorized as 0 Gy. The primary endpoint was the development of ACE following breast surgery. ResultsIn the RT and no-RT cohorts, 11.2% and 3.7% exhibited CAC >0, respectively. Over a 9.3-year follow-up period, the 10-year ACE rate was 0.7%. In the multivariate analysis, the CAC score was a significant risk factor for ACE (CAC >0 vs CAC = 0, 10-year 6.2% vs 0.2%, P < 0.001). In the subgroup with CAC >0, the 10-year ACE rates were 0%, 3.7%, and 13.7% for patients receiving mean heart doses of 0 Gy, 0–3 Gy, and >3 Gy, respectively (P = 0.133). Although CAC score was not predictive for non-ACE heart disease risk (P > 0.05), the 10-year non-ACE heart disease rates were 1.7%, 5.7%, and 7.1% for patients with CAC = 0 receiving MHD of 0 Gy, 0–3 Gy, and >3 Gy, respectively (P < 0.001). ConclusionsThe CAC score was a significant predictor of ACE in patients with breast cancer. Although further studies are required, CAC score screening on simulation CT in patients undergoing breast RT can help identify those with high risk for ACE on a per-patient basis.

Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study

Objective. While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach. The upper abdomen of a live 4 year old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of variation was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results. A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion. DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features.

Sinogram interpretability based CT artefact reduction for multi-material workpieces

ABSTRACT Computed tomography (CT) is a technology for the simultaneous inspection of internal and external features of workpieces. However, beam hardening and scattered radiation artefacts caused by the different material densities and atomic numbers commonly confound CT measurements of multi-material workpieces. Multispectral CT (MSP CT) measurements reduce artefacts by combining multiple CT volumes performed with different x-ray spectra. However, all known simulation-based CT parameter optimisation approaches for industrial applications consider only the single-spectrum case. This paper investigates the CT radon space, focusing on sinogram interpretability. Multispectral CT x-ray spectra can be determined by defining and analysing sinogram areas corresponding to equivalent volume slice artefacts. Validation indicates that the two MSP CT spectra recommended via a proposed CT simulation considering critical sinogram areas cause an artefact reduction. Results are quantified by a standard deviation reduction of measured geometrical features.

An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check

Complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with incorrect level treatments of the spine. The purpose of this work was to mitigate such challenges by fully automating the treatment planning process for diagnostic and simulation computed tomography (CT) scans. Vertebral bodies are labeled on CT scans of any length using 2 intendent deep-learning models-mirroring 2 different experts labeling the spine. Then, a U-Net++ architecture was trained, validated, and tested to contour each vertebra (n=220 CT scans). Features from the CT and auto-contours were input into a random forest classifier to predict whether vertebrae were correctly labeled. This classifier was trained using auto-contours from cone beam computed tomography, positron emission tomography/CT, simulation CT, and diagnostic CT images (n=56 CT scans, 751 contours). Auto-plans were generated via scripting. Each model was combined into a framework to make a fully automated clinical tool. A retrospective planning study was conducted in which 3 radiation oncologists scored auto-plan quality on an unseen patient cohort (n=60) on a 5-point scale. CT scans varied in scan length,presence of surgicalimplants, imagingprotocol,andmetastatic burden. The results showed that the uniquely designed convolutional neural networks accurately labeled and segmented vertebral bodies C1-L5 regardless of imaging protocol or metastatic burden. Mean dice-similarity coefficientwas85.0% (cervical),90.3% (thoracic), and93.7% (lumbar).The randomforestclassifier predicted mislabelingacrossvariousCT scan types with an area under the curve of 0.82.All contouring and labeling errorswithintreatmentregions (11 of 11), includingerrors frompatient plans with atypical anatomy(eg,T13, L6) were detected.Radiation oncologists scored98%of simulation CT-based plansand 92% ofdiagnostic CT-basedplans as clinically acceptableor needingminor edits for patients with typical anatomy.On average, end-to-end treatment planning time of the clinical tool was less than 8 minutes. This novel method to automatically verify, contour, and plan palliative spine treatments is efficient and effective acrossvariousCT scan types.Furthermore, it is the first to create a clinical tool that can automatically verify vertebral level in CT images.

Characterization and Prediction of Signal Intensity Changes in Normal Liver Parenchyma on Gadoxetic Acid-enhanced MRI Scans after Liver-directed Radiation Therapy.

Purpose To better characterize and understand the significance of focal liver reaction (FLR) development in a large cohort of patients who underwent gadoxetic acid-enhanced MRI after being treated with radiation therapy (RT) for hepatobiliary tumors. Materials and Methods This retrospective study evaluated 100 patients (median age, 65 years [first and third quartiles, 60-69 years]; 80 men) who underwent RT for hepatocellular carcinoma, bile duct tumors, or liver metastases at Mount Sinai Hospital between March 1, 2018, and February 29, 2020. CT simulation scans were fused to MRI scans obtained 1-6 months and 6-12 months after RT, using the hepatobiliary phase of the MRI. To define FLR volume, two radiation oncologists independently delineated the borders of the hypointensity observed on MRI scans in the liver region where RT was delivered. Biologically effective dose (BED) thresholds for the formation of FLRs were calculated, along with albumin-bilirubin (ALBI) scores and grades, and overall survival. Results Most patients developed FLRs, which decreased in volume over time. Median BED threshold values for FLR development were 63.6 Gy at 1-6 months and 88.7 Gy at 6-12 months. While higher baseline ALBI scores were associated with a lower rate of FLRs, there was a significant association between FLR volume and increase in ALBI score at 1-6 months (P = .048). Twelve- and 24-month survival estimates for the cohort were 81% and 48%, respectively. Histopathologic analysis of seven explanted liver specimens demonstrated findings consistent with radiation-induced liver disease. Conclusion FLRs were a clear measure of liver damage after RT and were associated with the development of liver dysfunction and focal radiation-induced liver disease. Keywords: MRI, Radiation Therapy Supplemental material is available for this article. © RSNA, 2022.

Curvature Gradient-estimation Using CT Sinogram and its Application to Reverse Engineering

In industrial manufacturing, reverse engineering, which can be applied to analyze or re-design, is an essential process, whereby scanned data of real objects are used to generate a CAD model. CAD data have various features such as fillets; therefore, robust and versatile reverse engineering is still difficult. Particularly, an adequate segmentation from real scanned data is challenging, and it is a major obstacle for reverse engineering. In this study, we propose an innovative segmentation from CT scanned data, wherein user-friendly segmentation is achieved by directly computing the curvature gradient from a CT sinogram. We used the proposed method to patch a B-spline surface onto each segmented region. The experimental results show that the proposed method offers robust, versatile, and user-friendly reverse engineering that is faster than conventional manual modeling. In addition, as it is supported by X-ray CT simulation, the proposed method can be applied to various surface mesh data without requiring any actual X-ray CT equipment. • Robust curvature gradients are computed directly from CT sinogram. • Flexible and powerful segmentation is performed using multi-level Morse complex. • B-spline surfaces are patched onto each segmented region as a demonstration. • X-ray CT simulations are conducted and used for the proposed method.

Improving template-based CT data evaluation by integrating CMM reference data into a CAD model-based high fidelity triangle mesh

ABSTRACT Industrial X-ray computed tomography is a relatively recent technology in the field of 3D coordinate metrology. The available technical solutions represent capable systems, which are continuously adapted for different applications. Nonetheless, some profound limitations and technical challenges remain (e.g. effects of the polychromatic X-ray spectrum and a strong correlation between image quality and measurement duration), which require continuous efforts in order to improve the accuracy of such systems in the context of dimensional metrology. Therefore, this contribution demonstrates the integration of CMM measurement data into a CAD model in order to generate full reference measurements, which can be used to evaluate the measurement accuracy of CT systems. Currently, much effort is being made in order to improve simulations of CT systems with the goal to realistically recreate real CT measurements. In order to validate the simulation model by comparison against physical CT measurements, the geometry of the measurement object has to be known as accurately as possible. The presented approach shows how to generate geometries from CMM reference data which are suited to be used as an input for CT simulation tools. Detailed technical descriptions of the algorithms are given and demonstrated based on real measurements.

High-resolution X-ray computed tomography simulations of synthetically-generated volume porosity in continuous carbon fibre-reinforced polymer samples

ABSTRACT The fast and correct determination of porosity in carbon fibre–reinforced polymers and other polymeric material is an important application area of X-ray computed tomography (CT). In this CT simulation study, microstructures such as individual carbon fibres, micro-voids, as well as the polymer matrix including mesoscale voids, were taken into account to generate CT data synthetically with well-known porosity. It was found that the ratio of total surface area (SA) of the voids divided by the total volume (V) of the voids is suitable to describe and differentiate individual porosity samples. Our investigations revealed that the ratio SA/V can be used to estimate a minimal necessary voxel size for proper porosity segmentation by a simple ISO50 threshold. Under certain conditions, using an adapted ISOxx threshold value at a resolution of (10 µm)3 voxel size, the porosity in a specimen can be determined with an average measurement error below 10%. As long as the CT resolution is not high enough to completely resolve all void structures, using global threshold segmentation is always a compromise between over-segmentation of the macro-voids and under-segmentation of micro-voids.

A Network-Based Model for Optimization of Container Location Assignment at Inland Terminals

The development of inland terminals helps seaports mitigate inevitable storage capacity problems, extending their gravitational field and strengthening their competitive advantage. Optimizing the container storage, efficiency, and productivity of seaports and inland terminals is becoming increasingly important. The present paper develops a network-based mathematical model to optimize the location assignment of inbound containers at inland terminals. The model’s assumptions are based on the analysis of criteria at representative European inland terminals, and the model aims to reduce unproductive manipulations. The present study is one of the few to develop a single model that integrates all important criteria for container location assignment, including a container’s dimensions, its occupancy, cargo type, the container’s owner, and distance to the final user. These criteria are important for the terminal operator so that they can determine container locations in a way that minimizes unproductive manipulations. Data from a single inland terminal were used to validate the model within the FlexSim CT simulation environment. Our results suggest that the model can reduce unproductive manipulations and associated costs at inland terminals.

Coronary access after transcatheter aortic valve replacement in bicuspid versus tricuspid aortic stenosis.

It is unknown whether there are differences in coronary access after transcatheter aortic valve replacement (TAVR) between bicuspid and tricuspid anatomy. Our aim was to investigate coronary access after TAVR using a self-expanding transcatheter heart valve (THV) in bicuspid versus tricuspid aortic valves (BAV vs TAV), based on CT simulation. A total of 86 type 0 BAV, 70 type 1 BAV, and 132 TAV patients were included. If the coronary ostium faced the sealed parts of the THV or the tilted-up native leaflet (NL), this was defined as THV- or NL-related challenging coronary access, respectively. If coaxial engagement was not allowed due to interference from the unwrapped frame, THV-related complex coronary access was defined. The incidence of THV-related challenging coronary access was 21.2% for the left coronary artery (LCA) and 17.7% for the right coronary artery (RCA), and type 0 BAV patients encountered fewer THV-related challenging LCA access than their TAV counterparts (OR 0.42, 95% CI: 0.20-0.89). NL-related challenging coronary access was observed in 3.1% for LCA and 1.4% for RCA, and THV-related complex coronary access was identified in 5.9% for LCA and 17.0% for RCA; however, no significant differences were found among groups. The proportion of optimal fluoroscopic viewing angles suitable for guiding LCA engagement was similar among groups (64.0% vs 70.0% vs 62.1%), but those suitable for guiding RCA engagement were significantly higher in the type 0 BAV group (31.4% vs 4.3% vs 9.1%). Coronary access may be challenging or complex in a significant proportion of both BAV and TAV patients after TAVR. Type 0 BAV anatomy may be more favourable for post-TAVR coronary access.

MRI-Guided Intraluminal HDR Brachytherapy for Inoperable Malignant Biliary Tract Obstruction

Aim: Malignant biliary tract obstruction (MBTO) leads to abdominal pain, jaundice, poor quality of life and portends a poor prognosis. Management of MBTO is a significant therapeutic challenge given that treatment with chemotherapy or radiotherapy has limited utility and percutaneous biliary drainage or stent insertion provides only transient symptomatic relief. Intraluminal HDR brachytherapy via catheters inserted percutaneously at the time of stent insertion can be effective in the management of inoperable MBTO. Here, we describe the workflow developed within our department for simulation and treatment using an intraluminal brachytherapy technique. Process: Delivery of bile duct brachytherapy requires the participation of multiple disciplines including interventional radiology (IR), medical physics, radiation oncology (RO) and radiation therapy with careful coordination of numerous tasks. A drain/stent and 1-2 treatment catheters are inserted into the patient's bile ducts by IR before the CT and MR simulation (sim). During the CT sim, multiple surview and low dose helical scans are acquired to verify the treatment catheter position and adjustments are made by the RO as needed. Once the position is finalized, a planning CT scan is done with IV contrast and the patient in voluntary breath hold. Then, the CT and MR sim images are fused. The CT is used to digitize the brachytherapy source path, while the MR is used for target and organs at risk delineation. Treatment is delivered 1-2 days after sim, with 15-25 Gy in 3-5 fractions using an Ir-192 HDR source. Radiographs are taken to confirm catheter position prior to each treatment. Benefits/Challenges: Brachytherapy can be used to treat the tumor at the same time as placement of a stent/drain, thus providing both symptom management and disease control. Although the patient is admitted for the IR procedure, the actual brachytherapy treatment is done on an outpatient basis with no anesthesia, thus treatment does not require significant time or resources in the brachytherapy suite. Patients often present with biliary obstruction emergently and stent/drain placement is booked urgently in IR with little notice to the radiotherapy department. The tight time line makes it difficult to facilitate communication between the multidisciplinary team, prepare staff, assemble supplies, and coordinate subsequent appointments. The CT sim process continues to evolve as new issues are identified with each patient. Variations in catheter/stent placement is a potential challenge and 2/8 patients have required reinsertion of angio-sheath by IR after problems were discovered during the CT sim. MR sim is not always possible depending on nature of inserted material (i.e. metallic stent or drainage sheath). Treatment catheters must be secured in place and covered with a dressing between appointments to prevent damage, change in position or obstruction. Patient education and cooperation is crucial to ensure proper care. Impact/Outcomes: In April 2021, our institution became the first in Canada to offer MRI-guided brachytherapy treatment for patients with inoperable MBTO. As of Jan 2022, 8 patients have been treated using this protocol. With each subsequent case, the time required for treatment delivery has decreased from 1 hour to 30 minutes per fraction and sim processes optimized. This treatment option has potential to improve the quality of life in patients with minimal therapeutic options.

Transferability of learning from Computed Tomography simulation into clinical practice: student perspectives

ABSTRACT Simulation has an ever-increasing role in health professions’ education, however there is limited evidence on the transferability of learning to practice. In this study, a pragmatic approach using a convergent mixed methods design explored the use of a virtual high-fidelity Computed Tomography (CT) simulator in preparing students for clinical placement. Specifically, it aimed to describe radiography students’ perceptions of their confidence and ability to transfer their learning from a virtual CT simulator into clinical practice. Radiography students (n = 60) were surveyed on their perceptions of how using the CT simulator (5 hours) prepared them for clinical placement. Four themes emerged from the data analysis: Simulation platform, Teaching and learning activities, Confidence and Transferability. Key findings demonstrated a significant link between the students’ perceptions of their ability to transfer their learning into practice and the CT scanner platform they used on clinical placement. Students who used a CT scanner on placement that was similar to the simulator were more likely to believe their learning was transferable to practice compared with those who did not use a similar scanner on placement. However, confidence gained was not dependent on the scanner used on clinical placement. Discussion on the metacognitive processes linked to these findings are included.

An Evidence-Based Approach to The Application of Bladder Filling Criteria at CT Simulation

Purpose: Patients undergoing radiation therapy for prostate cancer are required to have a comfortably full bladder at our institution to achieve greater normal tissue sparing and better target position reproducibility. At the time of simulation, bladder filling is assessed and considered acceptable when the dome of the bladder is 1-4cm above the superior aspect of the femoral heads, but not above L5/S1. Failure to meet this criteria would result in either repeated imaging on the same day or rescheduling to a different day.

Establishment of diagnostic reference level and radiation dose variation in head &amp; neck and pelvis treatment planning in radiation therapy computed tomography

Background: This observational study aims to establish a discrepancy in the radiation dose distributed for head &amp; neck and pelvis localization in Computed Tomography (CT) imaging in radiation therapy. The objective and need of the current study are to establish Diagnostic Reference Level (DRL) for head &amp; neck and pelvic CT protocols in Radiation Therapy planning and compare them with other regional, national and international DRL. The established DRL will be used to find further optimized DRL for Radiation Therapy Computed Tomography (RTCT). Methods: A total of 120 patients with Head &amp; Neck cancers and 90 patients with pelvic cancers to be treated with radiotherapy prescribed for the RTCT with ages above 18 years old and above were included in the current study. The advanced Philips 16 slice big bore CT acquired all the CT simulation images. Results: Third quartile standards of Dose Length Product and effective dose for Head &amp; Neck, and pelvis were 790.65mGy.cm, 2.45179 mSv, and 999.7 mGy.cm, 15.48mSv respectively. The third quartile CTDIvol value for the same order of procedure is 17.6 mGy. Tube voltage of 120kVp and 300mAs and used for radiotherapy planning. Conclusion: The first regional Radiation therapy Computed tomography simulation Diagnostic reference levels have been projected and deliver a platform for dose evaluation and optimization due to the limited number of papers on radiation therapy, computed tomography, and diagnostic reference levels. Comparison with previously available RT CT diagnostic reference levels indicated some radiation dose variation, so exposure parameters should be revised and improved.

Hybrid split-arc partial-field volumetric modulated arc therapy: an improved beam arrangement for linear accelerator-based hippocampal-avoidance whole brain radiation therapy

BackgroundThis technical note aims to verify the hippocampus and adjacent organs at risk (OARs) sparing ability of an improved beam arrangement, namely hybrid split-arc partial-field volumetric modulated arc therapy (VMAT) (Hsapf-VMAT) during whole brain radiation therapy (WBRT).Materials and methodsComputed tomography simulation images of 22 patients with brain metastases were retrieved in this retrospective planning study. The hippocampus was manually delineated according to the criterion of RTOG 0933. Plans delivering 30 Gy in 10 fractions were generated for each patient using split-arc partial-field VMAT (sapf-VMAT) and Hsapf-VMAT. The sapf-VMAT plans consisted of 4 arc fields of 179.9° each with reduced field size. The Hsapf-VMAT consisted of 4 arc fields similar to sapf-VMAT in addition to 2 lateral opposing static fields. Statistical comparisons between treatment plans of both techniques were performed using the paired t-test at 5% level significance.ResultsThe results demonstrated that Hsapf-VMAT can achieve superior dose sparing in hippocampus which is comparable to sapf-VMAT (p > 0.05). In both eyes, Hsapf-VMAT had significantly lower Dmean and Dmax compared to sapf-VMAT (p < 0.005). Decrease in Dmax of both lenses using Hsapf-VMAT (p < 0.005) were statistically significant when compared to sapf-VMAT. Hsapf-VMAT demonstrated significant reduction of Dmean and Dmedian to the optic nerves (p < 0.05). Whole brain planning target volume (PTV) coverage was not compromised in both techniques.ConclusionThe present study adopts a hybrid technique, namely Hsapf-VMAT, for hippocampal sparing WBRT. Hsapf-VMAT can achieve promising dose reduction to the hippocampus, both eyes and lenses. Therefore, Hsapf-VMAT can be considered an improved version of sapf-VMAT.

Development of an anthropomorphic multimodality pelvic phantom for quantitative evaluation of a deep-learning-based synthetic computed tomography generation technique.

PurposeThe objective of this study was to fabricate an anthropomorphic multimodality pelvic phantom to evaluate a deep‐learning‐based synthetic computed tomography (CT) algorithm for magnetic resonance (MR)‐only radiotherapy.MethodsPolyurethane‐based and silicone‐based materials with various silicone oil concentrations were scanned using 0.35 T MR and CT scanner to determine the tissue surrogate. Five tissue surrogates were determined by comparing the organ intensity with patient CT and MR images. Patient‐specific organ modeling for three‐dimensional printing was performed by manually delineating the structures of interest. The phantom was finally fabricated by casting materials for each structure. For the quantitative evaluation, the mean and standard deviations were measured within the regions of interest on the MR, simulation CT (CTsim), and synthetic CT (CTsyn) images. Intensity‐modulated radiation therapy plans were generated to assess the impact of different electron density assignments on plan quality using CTsim and CTsyn. The dose calculation accuracy was investigated in terms of gamma analysis and dose‐volume histogram parameters.ResultsFor the prostate site, the mean MR intensities for the patient and phantom were 78.1 ± 13.8 and 86.5 ± 19.3, respectively. The mean intensity of the synthetic image was 30.9 Hounsfield unit (HU), which was comparable to that of the real CT phantom image. The original and synthetic CT intensities of the fat tissue in the phantom were −105.8 ± 4.9 HU and −107.8 ± 7.8 HU, respectively. For the target volume, the difference in D 95% was 0.32 Gy using CTsyn with respect to CTsim values. The V 65Gy values for the bladder in the plans using CTsim and CTsyn were 0.31% and 0.15%, respectively.ConclusionThis work demonstrated that the anthropomorphic phantom was physiologically and geometrically similar to the patient organs and was employed to quantitatively evaluate the deep‐learning‐based synthetic CT algorithm.

Trans patellar tendon sagittal tibial cut for lateral unicompartmental knee arthroplasty-location of the split- CT simulation study

BackgroundIn lateral unicompartmental knee arthroplasty (UKA), a sagittal cut is often performed through the patellar tendon (PT). Although the approach is likely widely used, it has not been described in detail, especially regarding the site of the split. This study aimed to clarify where the split should be made. MethodsThis single-center retrospective cohort study included 49 consecutive patients and 51 knees with lateral osteoarthritis. Using preoperative computed tomography, we measured the distance from the medial edge of the PT to the intersection of the PT and the sagittal cutting line, defined as a line parallel to the Akagi's line and passing the tip of the lateral tibial spine. ResultsThe sagittal cut line passed a mean of 45 ± 11% of the patellar tendon width from the PT medial edge. ConclusionsThe tendon split should be made just medial to the center of the PT because it is where the sagittal cut line for lateral UKA passes.