Fan-beam CT Research Articles

Investigation of Computed Tomography Numbers on Multiple Imaging Systems using Single and Multislice Methods.

The aim of this study is to determine the variation in Hounsfield values with single and multi-slice methods using in-house software on fan-beam computed tomography (FCT), linear accelerator (linac) cone-beam computed tomography (CBCT), and Icon-CBCT datasets acquired using Gammex and advanced electron density (AED) phantoms. The AED phantom was scanned on a Toshiba computed tomography (CT) scanner, five linac-based CBCT X-ray volumetric imaging systems, and Leksell Gamma Knife Icon. The variation between single and multi-slice methods was assessed by comparing scans acquired using Gammex and AED phantoms. The variation in Hounsfield units (HUs) between seven different clinical protocols was assessed using the AED phantom. A CIRS Model 605 Radiosurgery Head Phantom (TED) phantom was scanned on all three imaging systems to assess the target dosimetric changes due to HU variation. An in-house software was developed in MATLAB to assess the HU statistics and the trend along the longitudinal axis. The FCT dataset showed a minimal variation (central slice ± 3 HU) in HU values along the long axis. A similar trend was also observed between the studied clinical protocols acquired on FCT. Variation among multiple linac CBCTs was insignificant. In the case of the water insert, a maximum HU variation of -7.23 ± 68.67 was observed for Linac 1 towards the inferior end of the phantom. All five linacs appeared to have a similar trend in terms of HU variation from the proximal to the distal end of the phantom, with a few outliers for Linac 5. Among three imaging modalities, the maximum variation was observed in gamma knife CBCTs, whereas FCT showed no appreciable deviation from the central value. In terms of dosimetric comparison, the mean dose in CT and Linac CBCT scans differed by <0.5 Gy, whereas at least a 1 Gy difference was observed between CT and gamma knife CBCT. This study shows a minimal variation with FCT between single, volume-based, and multislice methods, and hence the current approach of determining the CT-electron density curve based on a single-slice method would be sufficient for producing a HU calibrations curve for treatment planning. However, CBCTs acquired on linac, and in particular, gamma knife systems, show noticeable variations along the long axis, which is likely to affect the dose calculations performed on CBCTs. It is highly recommended to assess the Hounsfield values on multiple slices before using the HU curve for dose calculations.

The first reported case of a patient with small cell lung cancer treated with fan beam computed tomography-guided online adaptive radiotherapy

Adaptive radiotherapy (ART) allows control of dosimetric impact of patient anatomical and functional variations over the treatment course, to minimize normal tissue exposure and maximize dose delivery to tumor. We present the first reported case of fan beam computed tomography (FBCT)-guided online ART for the treatment of small cell lung cancer (SCLC). A 62-year-old woman was diagnosed with histologically proven limited-stage SCLC. During definitive radiochemotherapy (50 Gy in daily fractions of 2.5 Gy), the tumor shrinkage resulted in an unexpected dose escalation to organs at risk (OAR). To correct the dose change, she received an online ART treatment session in our center with four-dimensional FBCT before the 12th fraction was delivered. The application of online ART, including imaging, recontouring and replanning, was feasible as the total treatment time was &lt;25 min. Further research is warranted to verify the benefit of online ART in individualized treatment.

3D-printed large-area focused grid for scatter reduction in cone-beam CT.

Cone-beam computed tomography (CBCT) systems acquire volumetric data more efficiently than fan-beam or multislice CT, particularly when the anatomy of interest resides within the axial field-of-view of the detector and data can be acquired in one rotation. For such systems, scattered radiation remains a source of image quality degradation leading to increased noise, image artifacts, and CT number inaccuracies. Recent advances in metal additive manufacturing allow the production of highly focused antiscatter grids (2D-ASGs) that can be used to reduce scatter intensity, while preserving primary radiation transmission. We present the first implementation of a large-area, 2D-ASG for flat-panel CBCT, including grid-line artifact removal and related improvements in image quality. A 245 × 194 × 10 mm 2D-ASG was manufactured from chrome-cobalt alloy using laser powder-bed fusion (LPBF) (AM-400; Renishaw plc, New Mills Wotton-under-Edge, UK). The 2D-ASG had a square profile with a pitch of 9.09 lines/cm and 10:1 grid-ratio. The nominal 0.1mm grid septa were focused to a 732 mm x-ray source to optimize primary x-ray transmission and reduce grid-line shadowing at the detector. Powder-bed fusion ensured the structural stability of the ASG with no need for additional interseptal support. The 2D-ASG was coupled to a 0.139-mm element pitch flat-panel detector (DRX 3543, Carestream Health) and proper alignment was confirmed by consistent grid-line shadow thickness across the whole detector array. A 154-mm diameter CBCT image-quality-assurance phantom was imaged using a rotary stage and a ceiling-mounted, x-ray unit (Proteus XR/a, GE Medical Systems, 80kVp, 0.5mAs). Grid-line artifacts were removed using a combination of exposure-dependent gain correction and spatial-frequency, Fourier filtering. Projections were reconstructed using a Parker-weighted, FDK algorithm and voxels were spatially averaged to 357 × 357 × 595 µm to improve the signal-to-noise characteristics of the CBCT reconstruction. Finally, in order to compare image quality with and without scatter, the phantom was scanned again under the same CBCT conditions but with no 2D-ASG. No additional antiscatter (i.e., air-gap, bowtie filtration) strategies were used to evaluate the effects in image quality caused by the 2D-ASG alone. The large-area, 2D-ASG prototype was successfully designed and manufactured using LPBF. CBCT image-quality improvements using the 2D-ASG included: an overall 14.5% CNR increase across the volume; up to 48.8% CNR increase for low-contrast inserts inside the contrast plate of the QA phantom; and a 65% reduction of cupping artifact in axial profiles of water-filled cross sections of the phantom. Advanced image processing strategies to remove grid line artifacts did not affect the spatial resolution or geometric accuracy of the system. LPBF can be used to manufacture highly efficient, 2D-focused ASGs that can be easily coupled to clinical, flat-panel detectors. The implementation of ASGs in CBCT leads to reduced scatter-related artifacts, improved CT number accuracy, and enhanced CNR with no increased equivalent dose to the patient. Further improvements to image quality might be achieved with a combination of scatter-correction algorithms and iterative-reconstruction strategies. Finally, clinical applications where other scatter removal strategies are unfeasible might now achieve superior soft-tissue visualization and quantitative capabilities.

Patient-Specific Auto-Segmentation of Target and OARs via Deep Learning on Daily Fan-Beam CT for Adaptive Prostate Radiotherapy

<h3>Purpose/Objective(s)</h3> Contouring the target and organs at risk (OARs) of the treated patient in each fraction is a key step of daily adaptive radiotherapy. We designed a patient-specific deep learning study to improve the auto-segmentation performance for adaptive prostate radiotherapy, by leveraging the daily image-guidance fan-beam CT images of the specific patient available in the current IGRT workflow. <h3>Materials/Methods</h3> Our institution has set up a newly developed biology-guided radiotherapy (BgRT) system enabling high-quality fan-beam CT imaging for daily treatments. The data of the first prostate patient treated with this system including 26 daily fan-beam CT scans were utilized in this study. A population network was first trained for the auto-segmentation of the prostate and seven pelvic OARs, based on a population dataset containing 56 different patient cases treated on the regular Linac. The pre-trained population network was intentionally fine-tuned to adapt to this specific patient with a transfer learning method. The selected OARs of this patient were contoured by two radiation oncologists on the initial treatment planning and all sets of daily fan-beam CT images as the ground truth for the patient-specific learning. Three different fine-tuning methods, i.e., fine-tuning the last convolution layer, the last convolution block and the whole network were analyzed. A longitudinal study was conducted by exploring the relationship between the auto-segmentation performance and the number of the sequential prior data used for learning. The performance of the patient-specific network was compared with the population network as well as the clinical rigid registration method to highlight its efficiency in radiotherapy practice. <h3>Results</h3> The Dice similarity coefficient (DSC) results of the proposed patient-specific network were 0.882, 0.934, 0.864, 0.827, 0.952, 0.954, 0.921 and 0.904 for the prostate, bladder, rectum, seminal vesicle, left and right femoral head, bowel and lymph nodes, respectively, outperforming the population network (mean DSC of 0.627) and the clinical registration method (mean DSC of 0.716). The whole network fine-tuning method outperformed the other two fine-tuning methods, showing it is more effective in this study. The contouring accuracy of the patient-specific network gradually increased with the increment of longitudinal training cases and approached saturation with more than four training cases. <h3>Conclusion</h3> This study proposes an accurate auto-segmentation method based on the patient-specific deep learning for adaptive prostate radiotherapy. By utilizing the image and contour similarity between the treatment fractions, the patient-specific auto-segmentation could outperform the common population network and the clinical registration method by a large margin, and thus is promising to facilitate more precise radiotherapy based on the new BgRT system.

A Fast 3D/3D Registration Method Based on Water Equivalent Path Length for Heavy-Ion Radiotherapy

<h3>Purpose/Objective(s)</h3> The accuracy of patient setup needs to be high in heavy-ion radiotherapy. Accordingly, there is an increasing demand for three-dimensional (3D) image-guided systems because of the limitations of the present two-dimensional imaging registration technology. One of the limitations is the inability to directly compare with planning computed tomography (CT) images. Due to this uncertainty in conformation, high dose per fraction radiotherapy is difficult. This study proposes a fast 3D/3D registration method comparing with water equivalent path length (WEPL) that uses planning and treatment CT images to minimize range variations by using the Lucas-Kanade (LK) algorithm. <h3>Materials/Methods</h3> A six-degrees-of-freedom (6DOF) couch correction was simulated for patient positioning at our center by performing automatic rigid image registration (RIR) and using the resultant translation/rotation. Our automatic RIR algorithm was used to align the planning CT and treatment CT images, simulating 6DOF. The alignment values (Δ<i>V</i>), which represents a six-dimensional vector of the amounts of 6DOF couch movement, were calculated by minimizing the following error function: E(<i>ΔV</i>)=Σ <i>_i</i>_∈Ω [<i>T_i</i> (<i>V+ΔV</i>)-<i>P_i</i> (<i>V</i>)]², where <i>V</i> is a six-dimensional vector that denotes a CT position and rotation in a treatment room; <i>T_i</i> (<i>V</i>) and <i>P_i</i> (<i>V</i>) are treatment and planning CT images respectively, whose position in the room is represented by <i>V; i</i> is a 3D vector which denotes a point in the room; and Ω is a set of calculation points, which was assigned a planning target volume (PTV) of Ω in this study. We minimized the error function by using the LK algorithm. To minimize the range variations, each pixel value of the CT images was changed into WEPL values and the alignment values were calculated in the same manner as the above algorithm. The method was evaluated using a dataset containing eight prostate cancer patient images, including a planning CT image with PTV and treatment CT images of four fraction with a fan-beam CT. The range variations were measured by calculating WEPL variations between the planning CT and treatment CT at (1) before registration and after registration based on (2) CT pixel values and (3) WEPL values, respectively. In addition, the calculation time was measured on a workstation with a single GPU. <h3>Results</h3> The mean WEL variation across the 32 cases (eight patients of four fractions) was (1) 1.8 ± 1.16 mm, (2) 1.08 ± 0.52 mm, and (3) 0.22 ± 0.17 mm. The worst WEPL variation were (1) 6.23 mm, (2) 3.06 mm, and (3) 1.17 mm. The mean calculation time were (2) 89 ms and (3) 1066 ms. <h3>Conclusion</h3> We developed a fast 3D/3D RIR method for reducing range variations to realize more accurate heavy-ion radiotherapy. We performed experiments involving eight prostate cancer patients, and our proposed method obtained high accuracy and short computation time.

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world’s first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose. Reducing the tube current of the FBCT system can reduce the scanning dose, but it will lead to serious noise and artifacts in the reconstructed images. In this study, we proposed a deep learning method, Content-Noise Cycle-Consistent Generative Adversarial Network (CNCycle-GAN), to improve the image quality and CT value accuracy of low-dose FBCT images to meet the requirements of adaptive radiotherapy. We selected 76 patients with abdominal and pelvic tumors who received radiation therapy. The patients received one low-dose CT scan and one normal-dose CT scan in IGRT mode during different fractions of radiotherapy. The normal dose CT images (NDCT) and low dose CT images (LDCT) of 70 patients were used for network training, and the remaining 6 patients were used to validate the performance of the network. The quality of low-dose CT images after network restoration (RCT) were evaluated in three aspects: image quality, automatic delineation performance and dose calculation accuracy. Taking NDCT images as a reference, RCT images reduced MAE from 34.34 ± 5.91 to 20.25 ± 4.27, PSNR increased from 34.08 ± 1.49 to 37.23 ± 2.63, and SSIM increased from 0.92 ± 0.08 to 0.94 ± 0.07. The P value is less than 0.01 of the above performance indicators indicated that the difference were statistically significant. The Dice similarity coefficients (DCS) between the automatic delineation results of organs at risk such as bladder, femoral heads, and rectum on RCT and the results of manual delineation by doctors both reached 0.98. In terms of dose calculation accuracy, compared with the automatic planning based on LDCT, the difference in dose distribution between the automatic planning based on RCT and the automatic planning based on NDCT were smaller. Therefore, based on the integrated CT-linac platform, combined with deep learning technology, it provides clinical feasibility for the realization of low-dose FBCT adaptive radiotherapy for abdominal and pelvic tumors.

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT-guided radiotherapy.

To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy. Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5s/rotation (denoted FB-0.5), 1.9s/rotation (FB-1.9), 16.6s/rotation (CB-16.6), and 60.0s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners. At ±2.5-mm motion, phantom measurements demonstrated mean±SD ESF widths of 0.6±0.0, 1.3±0.4, 2.0±1.1, and 2.9±2.0mm and geometric inaccuracy (excursion) of 2.7±0.4, 4.1±1.2, 2.6±0.7, and 2.0±0.5mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5). Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.

Use of a novel helical fan beam imaging system for computed tomography of the head and neck in sedated standing horses: 120 cases (2019-2020).

To evaluate the diagnostic capabilities of a novel helical fan beam CT system used for imaging of horses with clinical problems of the head and neck. 120 horses. Medical records were reviewed of horses presented for CT of the head or neck at 2 university hospitals. The following data were recorded: age, sex, breed, presenting complaint, sedation used for imaging, scanning time, procedure time, other diagnostic imaging methods performed, imaging diagnosis, clinical diagnosis, and complications during imaging. Quarter Horses and Warmbloods were the most common breeds, and the most common complaint was nasal discharge. The head (101/120 [84%] horses) was scanned most frequently, and the most common diagnoses were primary dental disease and a space-occupying lesion of the paranasal sinuses. Nuchal bursitis was the most common imaging diagnosis in the neck region. Procedure time ranged from 20 to 45 minutes with a scanning time of 30 to 40 seconds. No complications with horse sedation occurred, and horses tolerated scanning well. An imaging diagnosis was more frequently achieved with CT (109/120 [91%] horses) than with planar digital radiography (23/61 [38%] horses). The helical fan beam CT system had fast scanning times and could be used safely for routine imaging of the teeth and sinuses in horses. The caudal extent of scanning in the neck region was limited by the shape of the horse's neck and thorax.

Use of a novel helical fan beam imaging system for computed tomography of the distal limb in sedated standing horses: 167 cases (2019-2020).

To evaluate the diagnostic capabilities of a novel helical fan beam CT system used for imaging of horses with a range of clinical distal limb problems. 167 horses. Medical records were reviewed of horses presented for CT of the distal limb at 2 university-based veterinary hospitals. The following data were recorded: age, sex, breed, presenting complaint, sedation used for imaging, scanning time, procedure time, other diagnostic imaging methods performed, imaging diagnosis, clinical diagnosis, and complications during imaging. Most horses were Thoroughbreds and Quarter Horses. Procedure times ranged from 15 to 40 minutes, with scanning completed in 15 to 45 seconds for each region of interest. The foot or pastern region was commonly scanned (88/167 [53%] horses), with navicular bone disease diagnosed in 42 of 88 (48%) horses. The fetlock region was also commonly scanned (42/167 [40%] horses), with palmar or plantar osteochondral disease diagnosed in 17 of 42 (40%) horses. Horses were compliant during scanning, and no complications with sedation or damage to the scanner occurred. A specific imaging diagnosis for the lameness was achieved more frequently with CT imaging (166/167 [99%]) than with planar digital radiography (26/58 [45%]). The helical fan beam CT system could be used safely to scan sedated standing horses from the carpal or tarsal region distally. Subjectively, the machine was easy to operate, allowing CT to be incorporated into lameness investigations. CT imaging was very likely to result in a clinical diagnosis in horses with distal limb lameness.

Method to estimate fan-beam CT noise power spectrum using two basis functions with a limited number of noise realizations.

The noise power spectrum (NPS) plays a key role in image quality (IQ) evaluation as it can be used for predicting detection performance or calculating detective quantum efficiency (DQE). Traditionally, the NPS is estimated by ensemble averaging multiple realizations of noise-only images. However, the estimation error increases when there are a limited number of images. Since the estimation error directly affects the image quality (IQ) index, an accurate NPS estimation method is required. Recent works have proposed NPS estimation methods using the radial one-dimensional (1D) NPS as the basis; however, when sharp kernels are used during image reconstruction, these methods cannot accurately estimate the amplitude of each angular spoke of the 2D NPS composed of different cutoff frequencies determined from the complementary projection magnification factors for different spatial regions. In this work, we propose a 2D NPS estimation method that reflects the accurate amplitude of each angular spoke for fan-beam CTimages. An angular spoke of the 2D NPS is composed of two basis functions with different cutoff frequencies determined from the complementary projection magnification factors. The proposed method estimates these two weighting factors for each basis function by minimizing the mean-squared error (MSE) between the 2D NPS estimated from 10 noise realizations. Two noise profiles and two types of apodization filters (i.e., rectangular and Hanning) were used to reconstruct the noise-only images. To examine the nonstationary noise property of fan-beam CT images, the 2D NPS was estimated at three different local regions. The estimation accuracy of the proposed method was further improved by estimating the approximate weighting factors with sinusoidal functions, considering that the weighting factors vary slowly throughout the view angles. Regression orders of 1 to 4 were used during these estimations. The approximate weighting factors were then multiplied with each of the basis functions to estimate the 2D NPS. The normalized mean-squared error (NMSE) was used as an index to compare the performance of each NPS estimation method, with the analytical 2D NPS as the reference. Further validation was performed using XCAT phantomdata. We observed that the 2D NPS estimated using two basis functions reflected the accurate amplitude of each angular spoke, whereas the 2D NPS estimated using the radial 1D NPS as the basis could not. The 2D NPS estimated by applying the approximate weighting factors showed improved performance compared with that estimated using two basis functions. In addition, unlike the view-independent noise cases, where a lower regression order showed higher estimation performance, a higher regression order showed higher estimation performance in the view-dependent noisecases. In this work, we propose a 2D NPS estimation method that reflects the accurate amplitude of each angular spoke for fan-beam CT images using two basis functions. We observed that the proposed 2D NPS estimation method using two basis functions achieved better estimation performance compared with the method using the radial 1D NPS as the basis.

A new weighting scheme for arc based circle cone-beam CT reconstruction.

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich's helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ(x_,λ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x_. In fact, for every pixel x_, our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software "ASTRA toolbox." We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

Evaluation of Daily CT for EPID-Based Transit In Vivo Dosimetry.

PurposeThe difference in anatomical structure and positioning between planning and treatment may lead to bias in electronic portal image device (EPID)-based in vivo dosimetry calculations. The purpose of this study was to use daily CT instead of planning CT as a reference for EPID-based in vivo dosimetry calculations and to analyze the necessity of using daily CT for EPID-based in vivo dosimetry calculations in terms of patient quality assurance.Materials and MethodsTwenty patients were enrolled in this study. The study design included eight different sites (the cervical, nasopharyngeal, and oral cavities, rectum, prostate, bladder, lung, and esophagus). All treatments were delivered with a CT-linac 506c (UIH, Shanghai) using 6 MV photon beams. This machine is equipped with diagnosis-level fan-beam CT and an amorphous silicon EPID XRD1642 (Varex Imaging Corporation, UT, USA). A Monte Carlo algorithm was developed to calculate the transmit EPID image. A pretreatment measurement was performed to assess system accuracy by delivering based on a homogeneous phantom (RW3 slab, PTW, Freiburg). During treatment, each patient underwent CT scanning before delivery either once or twice for a total of 268 fractions obtained daily CT images. Patients may have had a position correction that followed our image-guided radiation therapy (IGRT) procedure. Meanwhile, transmit EPID images were acquired for each field during delivery. After treatment, all patient CTs were reviewed to ensure that there was no large anatomical change between planning and treatment. The reference of transmit EPID images was calculated based on both planning and daily CTs, and the IGRT correction was corrected for the EPID calculation. The gamma passing rate (3 mm 3%, 2 mm 3%, and 2 mm 2%) was calculated and compared between the planning CT and daily CT. Mechanical errors [ ± 1 mm, ± 2 mm, ± 5 mm multileaf collimator (MLC) systematic shift and 3%, 5% monitor unit (MU) scaling] were also introduced in this study for comparing detectability between both types of CT.ResultThe average (standard deviation) gamma passing rate (3 mm 3%, 2 mm 3%, and 2 mm 2%) in the RW3 slab phantom was 99.6% ± 1.0%, 98.9% ± 2.1%, and 97.2% ± 3.9%. For patient measurement, the average (standard deviation) gamma passing rates were 87.8% ± 14.0%, 82.2% ± 16.9%, and 74.2% ± 18.9% for using planning CTs as reference and 93.6% ± 8.2%, 89.7% ± 11.0%, and 82.8% ± 14.7% for using daily CTs as reference. There were significant differences between the planning CT and daily CT results. All p-values (Mann–Whitney test) were less than 0.001. In terms of error simulation, nonparametric test shows that there were significant differences between practical daily results and error simulation results (p < 0.001). The receiver operating characteristic (ROC) analysis indicated that the detectability of mechanical delivery error using daily CT was better than that of planning CT. AUCDaily CT = 0.63–0.96 and AUCPlanning CT = 0.49–0.93 in MLC systematic shift and AUCDaily CT = 0.56–0.82 and AUCPlanning CT = 0.45–0.73 in MU scaling.ConclusionThis study shows the feasibility and effectiveness of using two-dimensional (2D) EPID portal image and daily CT-based in vivo dosimetry for intensity-modulated radiation therapy (IMRT) verification during treatment. The daily CT-based in vivo dosimetry has better sensitivity and specificity to identify the variation of IMRT in MLC-related and dose-related errors than planning CT-based.

A convolutional neural network for estimating cone-beam CT intensity deviations from virtual CT projections

Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to demonstrate how deep learning based on phantom data can be used effectively for CBCT intensity correction in patient images. Four anthropomorphic phantoms were scanned on a CBCT and conventional fan-beam CT system. Intensity correction is performed by estimating the cone-beam intensity deviations from prior information contained in the CT. Residual projections were extracted by subtraction of raw cone-beam projections from virtual CT projections. An improved version of U-net is utilized to train on a total of 2001 projection pairs. Once trained, the network could estimate intensity deviations from input patient head and neck raw projections. The results from our novel method showed that corrected CBCT images improved the (contrast-to-noise ratio) with respect to uncorrected reconstructions by a factor of 2.08. The mean absolute error and structural similarity index improved from 318 HU to 74 HU and 0.750 to 0.812 respectively. Visual assessment based on line-profile measurements and difference image analysis indicate the proposed method reduced noise and the presence of beam-hardening artefacts compared to uncorrected and manufacturer reconstructions. Projection domain intensity correction for cone-beam acquisitions of patients was shown to be feasible using a convolutional neural network trained on phantom data. The method shows promise for further improvements which may eventually facilitate dose monitoring and ART in the clinical radiotherapy workflow.

Patch-Based DCNN Method for CBCT Image Enhancement

Cone-beam computed tomography (CBCT) often has suboptimal image quality compared with standard fan-beam CT due to increased sensitivity to scattering and motion artifacts. In radiation treatment of cancers, due to the convenience in image acquisition as it is fully incorporated into the treatment station, CBCT plays a significant role in treatment plan re-evaluation and adaptation in adaptive radiation therapy but is limited by the image quality. In this paper, we propose a deep convolutional neural network (DCNN) based method for head and neck (HN) CBCT enhancement, where the network learns to transform the low-quality CBCT images to high-quality images.CT and CBCT image pairs from 50 HN cancer patients who received radiotherapy treatment were used for model training, validation and testing. An additional 22 raw CBCT images from another vendor were also used for testing. Pre-processing procedures include rigid registration, center-cropping, and Otsu's thresholding to segment the anatomical region. Data quality control was applied before training a 2D U-Net to reduce the adverse impact from the unmatched pairs. A patch-based strategy was employed during training, since the randomly extracted patches could reduce the receptive field and thus reduce the probability of overfitting based on the global anatomy. We explored the impact of patch size by varying it to be 64 × 64, 128 × 128, 192 × 192 and 256 × 256. After fixing to the optimal patch size, a Pix2Pix network combining L1 loss and conditional generative adversarial network (GAN) loss was also trained.It was found that the U-Net trained with a patch size of 128 × 128 reached the optimal performance and improved the mean absolute error (MAE) from 57.91 to 34.78 Hounsfield units (HU), peak signal-to-noise ratio (PSNR) from 30. 56 to 32.84 dB, and structural similarity (SSIM) from 0.7546 to 0.8455 compared with the original CBCT. With the addition of conditional GAN loss, the trained Pix2Pix network better kept the image details, especially on the external testing set.In this work, we developed a DCNN-based method to improve the CBCT image quality. This method has the advantages of high adaptability, vendor neutrality and low cost, since it operates purely in the image domain. Our data quality control pipeline automatically removed the unmatched CT and CBCT image pairs from the training set. The proposed DCNN-based method with the patch-based training strategy showed promising results for fast CBCT image enhancement.

Reducing axial truncation artifacts in iterative cone-beam CT for radiation therapy using a priori preconditioned information.

Cone-beam computed tomography (CBCT) is increasingly utilized in radiation therapy for image guidance and adaptive applications. While iterative reconstruction algorithms have been shown to outperform traditional filtered back-projection methods in improving image quality and reducing imaging dose, they cannot handle data truncation in the axial view, which frequently occurs in the full-fan partial-trajectory acquisition mode. This proof-of-concept study presents a novel approach on truncation artifact reduction by utilizing a priori preconditioned information as the initial input for the iterative algorithm. Projections containing axial truncation were used for image reconstruction in extended axial field-of-view (AFOV) using the conjugate gradient least-squares (CGLS) algorithm. A priori information in the form of a planning fan-beam CT (FBCT) was repositioned in the expected CBCT imaging geometry, then further processed to dampen high-density features and convolved with a cubic Gaussian kernel to ensure differentiability for the gradient descent method. Anatomical and positional differences between the estimated and the actual imaging object were introduced to verify the efficacy of the proposed method. Extending the reconstruction AFOV alone could partially reduce truncation artifact. Using a priori information directly resulted in ghosting artifact when there were anatomical and positional differences between the estimated and the actual imaging object. Using a priori preconditioned information was shown to effectively reduce truncation artifact and recover peripheral information. Using a priori preconditioned information can effectively alleviate truncation artifact and assist recovery of peripheral information in iterative CBCT reconstruction.

Implementation and experimental evaluation of Mega-voltage fan-beam CT using a linear accelerator

BackgroundMega-voltage fan-beam Computed Tomography (MV-FBCT) holds potential in accurate determination of relative electron density (RED) and proton stopping power ratio (SPR) but is not widely available.ObjectiveTo demonstrate the feasibility of MV-FBCT using a medical linear accelerator (LINAC) with a 2.5 MV imaging beam, an electronic portal imaging device (EPID) and multileaf collimators (MLCs).MethodsMLCs were used to collimate MV beam along z direction to enable a 1 cm width fan-beam. Projection data were acquired within one gantry rotation and preprocessed with in-house developed artifact correction algorithms before the reconstruction. MV-FBCT data were acquired at two dose levels: 30 and 60 monitor units (MUs). A Catphan 604 phantom was used to evaluate basic image quality. A head-sized CIRS phantom with three configurations of tissue-mimicking inserts was scanned and MV-FBCT Hounsfield unit (HU) to RED calibration was established for each insert configuration using linear regression. The determination coefficient ({R}^{2}) was used to gauge the accuracy of HU-RED calibration. Results were compared with baseline single-energy kilo-voltage treatment planning CT (TP-CT) HU-RED calibration which represented the current standard clinical practice.ResultsThe in-house artifact correction algorithms effectively suppressed ring artifact, cupping artifact, and CT number bias in MV-FBCT. Compared to TP-CT, MV-FBCT was able to improve the prediction accuracy of the HU-RED calibration curve for all three configurations of insert materials, with {R}^{2} > 0.9994 and {R}^{2} < 0.9990 for MV-FBCT and TP-CT HU-RED calibration curves of soft-tissue inserts, respectively. The measured mean CT numbers of blood-iodine mixture inserts in TP-CT drastically deviated from the fitted values but not in MV-FBCT. Reducing the radiation level from 60 to 30 MU did not decrease the prediction accuracy of the MV-FBCT HU-RED calibration curve.ConclusionWe demonstrated the feasibility of MV-FBCT and its potential in providing more accurate RED estimation.

Study on setup errors for different body carcinoma radiotherapy with image guidance in TOMO-HD

Objective The literature study the setup errors of head and neck, thoracic, abdominal and pelvic tumors by megavoltage fan-beam CT based image guidance in TOMO-HD to provide the margin enlarging from clinic target volume (CTV) to planning target volume (PTV) in treatment planning system of TOMO-HD. Methods 103 patients with head and neck (30 patients), thoracic (42 patients), abdominal and pelvic (31 patients) carcinoma were enrolled. Megavoltage fan-beam CT based image guidance in tomotherapy-HD was used to acquire CT scan before every treatment. The left-right (X), superior-inferior (Y), anterior-posterior (Z) and rotation (Fy) setup errors of patients can be obtained from the tomography image automatically restructured by the system. Calculating the systematic error and the random error in the three dimensions and check whether the setup data accord with the normal distribution or not, then acquire the data expand in the three directions. Results According to 2 593 fan-beam CT scans, the shift errors (µ ± s) in X, Y, Z and Fy (rotation) of three study group were [(−0.31 ± 2.16) mm, (1.09 ± 3.56) mm, (2.36 ± 2.27) mm, (0.29 ± 0.96)°] (head and neck tumor), [(−0.98 ± 2.95) mm, (0.45 ± 6.86) mm, (3.79 ± 2.47) mm, (0.18 ± 0.60)°] (thoracic cancer) and [(−0.86 ± 2.85) mm, (−1.59 ± 6.91) mm, (5.77 ± 2.40) mm, (0.20 ± 0.68)°] (abdominal and pelvic carcinoma). The systematic errors (Σ) and random errors (σ) in X, Y, Z dimensions of patients with head and neck, thoracic, abdominal and pelvic tumors were (1.06 mm and 1.84 mm), (1.93 mm and 3.43 mm), (2.41 mm and 2.71 mm), (1.10 mm and 2.56 mm), (3.79 mm and 5.46 mm), (1.38 mm and 1.99 mm) and (1.39 mm and 0.87 mm), (4.98 mm and 5.69 mm), (1.19 mm and 2.05 mm), respectively. Conclusion It is recommended as a reference for image guidance in TOMO-HD according to the frequency distribution of setup errors, for patients with head and neck, chest and abdominal and pelvic tumors, the maximum range of motion in three dimensions are (5.00, 5.00, 5.00) mm, (6,63, 17.25, 16.00) mm and (6.49, 16.24, 13.60) mm. 摘要： 目的 利用 TOMO-HD 螺旋加速器的 MV 级扇形束 CT 研宄头颈部、胸部、腹盆部肿瘤在放疗中的摆位误差, 为 PTV 边界的外扩提供数据支持。 方法 采用 TOMO-HD 螺旋断层系统分别对 30 例头颈部、42 例胸部和 31 例腹盆 部肿瘤患者每次放疗前行 MV 级扇形束 CT 扫描, 系统自动重建图像并与治疗计划 CT 图像进行配准, 获得患者左右 (X 轴)、头脚 (Y 轴)、腹背 (Z 轴)、沿Y轴方向的旋转 (Fy) 方向的摆位误差。计算出X、Y、Z方向的系统误差(Σ) 和随 机误差(σ)并对摆位误差进行正态性检验, 计算出 PTV 的各方向外扩边界。 结果 共行 2 593 次扫描, 头颈部、胸部、腹盆部患者在 X 轴、Y 轴、Z轴、Fy 方向 (沿 Y 轴方向的旋转) 的摆位误差 (µ ± s) 分别为 [(−0.31 ± 2.16)mm、(1.09 ± 3.56)mm、(2.36 ± 2.27)mm、(0.29 ± 0.96)°], [(−0.98 ± 2.95)mm、(−0.45 ± 6.86)mm、(3.79 ± 2.47)mm、(0.18 ± 0.60)°] 和 [(−0.86 ± 2.85)mm、(−1.59 ± 6.91)mm、(5.77 ± 2.40)mm、(0.20 ± 0.68)°]。头颈部、胸部、腹盆部患者在 X 轴、Y 轴、Z轴方向的系统误差(Σ)和随机误差(σ)分别为 (1.06 mm 和 1.84 mm)、(1.93 mm 和 3.43 mm)、(2.41 mm 和 2.71 mm)、(1.10 mm 和 2.56 mm)、(3.79 mm 和 5.46 mm)、(1.38 mm 和 1.99 mm) 和 (1.39 mm 和 0.87 mm)、(4.98 mm 和 5.69 mm)、(1.19 mm 和 2.05 mm)。 结论 根据不同部位的摆位误差频数分布, 头颈部、胸部、腹盆部不同 部位肿瘤患者的靶区在三维方向上最大运动范围参考值为 (5.00, 5.00, 5.00)mm, (6.63, 17.25, 16.00)mm 和 (6.49, 16.24,13.60)mm, 推荐以此作为图像引导参考。

Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, “Cycle-Deblur GAN”, combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.

Self-channel-and-spatial-attention neural network for automated multi-organ segmentation on head and neck CT images

Accurate segmentation of organs at risk (OARs) is necessary for adaptive head and neck (H&N) cancer treatment planning, but manual delineation is tedious, slow, and inconsistent. A self-channel-and-spatial-attention neural network (SCSA-Net) is developed for H&N OAR segmentation on CT images. To simultaneously ease the training and improve the segmentation performance, the proposed SCSA-Net utilizes the self-attention ability of the network. Spatial and channel-wise attention learning mechanisms are both employed to adaptively force the network to emphasize the meaningful features and weaken the irrelevant features simultaneously. The proposed network was first evaluated on a public dataset, which includes 48 patients, then on a separate serial CT dataset, which contains ten patients who received weekly diagnostic fan-beam CT scans. On the second dataset, the accuracy of using SCSA-Net to track the parotid and submandibular gland volume changes during radiotherapy treatment was quantified. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), average surface distance (ASD), and 95% maximum surface distance (95SD) were calculated on the brainstem, optic chiasm, optic nerves, mandible, parotid glands, and submandibular glands to evaluate the proposed SCSA-Net. The proposed SCSA-Net consistently outperforms the state-of-the-art methods on the public dataset. Specifically, compared with Res-Net and SE-Net, which is constructed from squeeze-and-excitation block equipped residual blocks, the DSC of the optic nerves and submandibular glands is improved by 0.06, 0.03 and 0.05, 0.04 by the SCSA-Net. Moreover, the proposed method achieves statistically significant improvements in terms of DSC on all and eight of nine OARs over Res-Net and SE-Net, respectively. The trained network was able to achieve good segmentation results on the serial dataset, but the results were further improved after fine-tuning of the model using the simulation CT images. For the parotids and submandibular glands, the volume changes of individual patients are highly consistent between the automated and manual segmentation (Pearson’s correlation 0.97–0.99). The proposed SCSA-Net is computationally efficient to perform segmentation (sim 2 s/CT).

Optimization of solid tank design for fan-beam optical CT based 3D radiation dosimetry

Optical computed tomography (CT) is one of the leading modalities for imaging gel dosimeters for 3D radiation dosimetry. There exist multiple scanner designs that have showcased excellent 3D dose verification capabilities of optical CT gel dosimetry. However, due to multiple experimental and reconstruction based factors there is currently no single scanner that has become a preferred standard. A significant challenge with setup and maintenance can be attributed to maintaining a large refractive index bath (1–15 l). In this work, a prototype solid ‘tank’ optical CT scanner is proposed that minimizes the volume of refractive index bath to between 10 and 35 ml. A ray-path simulator was created to optimize the design such that the solid tank geometry maximizes light collection across the detector array, maximizes the volume of the dosimeter scanned, and maximizes the collected signal dynamic range. An objective function was created to score possible geometries, and was optimized to find a local maximum geometry score from a set of possible design parameters. The design parameters optimized include the block length x bl , bore position x bc , fan-laser position x lp , lens block face semi-major axis length x ma , and the lens block face eccentricity x be . For the proposed design it was found that each of these parameters can have a significant effect on the signal collection efficacy within the scanner. Simulations scores are specific to the attenuation characteristics and refractive index of a simulated dosimeter. It was found that for a FlexyDos3D dosimeter, the ideal values for each of the five variables were: x bl = 314 mm, x bc = 6.5 mm, x lp = 50 mm, x ma = 66 mm, and x be = 0. In addition, a ClearView™ dosimeter was found to have ideal values at: x bl = 204 mm, x bc = 13 mm, x lp = 58 mm, x ma = 69 mm, and x be = 0. The ray simulator can also be used for further design and testing of new, unique and purpose-built optical CT geometries.