Difference in performance between 3D and 4D CBCT for lung imaging: a dose and image quality analysis

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation

Four-dimensional cone-beam computed tomography, or 4D CBCT, is a technique used in image-guided radiation treatment to generate a series of phase-resolved reconstructions of the patient's anatomy. This is done to improve radiation therapy's capacity to target the tumour with more precision. However, both noise and streaking aberrations significantly reduce the quality of 4D CBCT pictures. This technique is known as "very sparse-view CT," and it gets its name from the fact that it only requires a limited number of under-sampled projections to reconstruct each phase of a phase-resolved picture. The strategy's name derives from the fact that it employs a limited number of predictions to achieve its objectives. Customers can select between two specialized Convolution Neural Network (CNN) models: N-Net and the proposed model. These models were developed to boost our confidence in the quality of the 4D CBCT images we provide. To execute this assignment, it was critical to have access to a large dataset. This is now achievable because of research that resulted in an advancement in 4D CBCT that made it practicable. Each phase-resolved picture produced by the N-Net network, which is presently under development, is expected to outperform the image produced before it in the production chain. This earlier picture was rebuilt using U-Net using the complete projection data. This was done to demonstrate the effectiveness of the N-Net technique. This is done to ensure that the reconstructed picture has the maximum possible level of accuracy. The proposed model network is based on the N-Net foundation and takes into consideration the temporal correlations visible in phase-resolved pictures. After assessing the validity of the suggested CNNs using XCAT simulation data, further testing was performed utilizing real patient 4D CBCT datasets to corroborate the findings. We went through all of this difficulty in order to assist CNN. Each of these networks exceeds the most powerful CNN models currently available as well as the two most complex iterative strategies in terms of their ability to minimize streaking artefacts and noise while also obtaining distinguishing features. The proposed approach's ability to tackle difficult tasks requiring a diverse mix of patient datasets and imaging technologies demonstrates its broad applicability. This was one of the first things we learned about the problem.

The purpose of this study was to determine interfractional variation of the centroid position and volume of internal target volume (ITV) during stereotactic body radiation therapy (SBRT) of lung cancer. From January 2014 to August 2014, a total of 32 patients with 37 primary or metastatic lung tumors were enrolled in our study. All patients received SBRT treatment in 4–5 fractions to a median dose of 48 Gy. Both 3D CT and 4D CT scans were used for radiotherapy treatment planning. 3D CBCT was acquired prior to treatment delivery to verify patient positioning. A total of 163 3D CBCT images were available for evaluation. 3D CBCT scans acquired for verification were registered with simulation CT scans. The ITVs were contoured on all verification 3D CBCT scans and compared to the initial gross target volume (GTV) or ITV in treatment planning system. GTV was based on 3D CT while ITV was based on both 3D CT and 4D CT. To assess the interfractional variation of ITV centroid position, we used vertebrae body adjacent to the tumor as reference point when performing the registration procedure. To eliminate the effect of time on tumor volume between simulation CT scan and the first fraction, the interfractional variation of ITV was evaluated from the first fraction to the last fraction. The overall 3D vector shift was 4.4±2.5 mm (range: 0.4−13.8 mm). The interfractional variation of ITV centroid position in superior‐inferior, anterior‐posterior, and left‐right directions were −0.7±2.7 mm, −1.4±3.4 mm, and −0.5±2.2 mm, respectively. No significant difference was observed between three directions (p=0.147). Large interfractional variations (≥5 mm) were observed in 12 fractions (9.3%) in superior‐inferior direction, 24 fractions (18.6%) in anterior‐posterior direction, and 5 fractions (3.9%) in left‐right direction. No time trend of tumor volume change measured in 3D CBCT was detected during four fractions (p=0.074). A significant (p=0.010) time trend was detected when evaluating the time trend of ITV change during 5 fractions and diameter was found to be significantly correlated with the ITV change (p=0.000). ITV did not show significant regression during SBRT treatment, but interfractional variation in the ITV centroid position was observed, especially in anterior‐posterior direction. An isotropic margin of 7 mm around ITV might be necessary for adequate coverage of interfractional variation of ITV centroid position, but only in case no soft tissue‐based setup is performed during SBRT treatment.PACS number(s): 87.55.dk

Dental CBCT and panoramic imaging play a pivotal role in dental diagnosis and treatment planning, alongside the indispensable use of computed tomography (CT) and X-ray imaging in dentistry, particularly for surgical planning. Given the widespread adoption of dental cone beam CT in clinics today, we explore a novel approach in this study—utilizing CT’s three-dimensional (3D) data to reconstruct a two-dimensional (2D) panoramic dental image. This method eliminates the requirement for an extra panoramic scan. In this work, we propose a novel framework to generate an enhanced and extended 2D panoramic view by using the dental arch extracted from 3D CBCT. Our method involves segmenting the patient’s dental arch from their 3D CBCT image by identifying horizontal slices with above-average intensity, followed by morphological operations, including dilation, Gaussian filtering, and skeletonization, to delineate the dental arch line. Additionally, we extend the dental arch beyond the wisdom teeth using quadratic curve fitting. Finally, we employ Maximum Intensity Projection on rotated cubic segments aligned with the dental arch curve to produce captivating panoramic images. The panoramic view produced using our proposed method, when compared to the results obtained from BlueSky and OpenInventor, exhibited superior enhancements and greater accuracy in panoramic visualization.

2.02 - Physical Parameters of Image Quality

In order to determine an objective measure of a system's image quality, we developed a simple, non-invasive measurement procedure to determine the detective quantum efficiency of digital radiographic systems, especially image intensifier-tv systems. Therefore we set up measurement procedures for the quantities intensity transfer function (ITF) (also called characteristic curve), modulation transfer function (MTF), noise power spectrum (NPS) and the low frequency drop (LFD). The quantities ITF, MTF, NPS and LFD are determined by the analysis of images of simple, standardized test objects (a slit, Al-filters of different thickness and a lead disk). The images are automatically evaluated by means of an Apple Macintosh workstation and the program NIH image with some special extensions. The resulting quantities MTF, NPS, LFD are combined to determine the noise equivalent quanta (NEQ) and the detective quantum efficiency (DQE). By means of this measurement procedure quantities, that describe the objective image quality like NEQ and DQE, can be determined in a simple way. Only a set of 45 images is needed for diagnosis of a system. This method provides a powerful analysis tool for image quality, that is applicable in the field and can be done from a remote location. It may be used in a clinical environment (e.g. in constancy testing).

Purpose:To develop an iterative reconstruction algorithm using a compressed sensing with adaptive prior image constraints to solve 4D CBCT reconstruction problem.Methods:The images reconstructed by the FDK algorithm with a full set of unsorted projections are served as prior images for partial projections in each phase group and are utilized as an initial guess. Additionally, the prior images are clustered into several regions by applying intensity‐based thresholding, which is referred to as the segmented prior images. The segmented prior images are employed to detect any possible mismatched areas compared with the target images generated by partial projection data. With these two prior images, our algorithm alternately performs the simultaneous algebraic reconstruction technique and anisotropic total variation regularization while adjusting a weighted relaxation map during the iterative reconstruction process. The weighted relaxation map depends on binary images created by the voxel‐dependent comparison between the segmented prior and segmented target images. For the segmented target images, the k‐means clustering with a geometric weighting is applied on the reconstruction images generated in each iteration step. The inverse values of the distance map converted from binary images are assigned to be the values of the relaxation map. Evaluations using Catphan504 phantom with a motion platform were carried out.Results:Qualitative and quantitative analyses showed that the method provides high‐quality CBCT reconstruction images when compared with those generated by the FDK, CS, and PICCS algorithms, with higher contrast‐to‐noise ratio and faster convergence caused by minimizing data fidelity. Especially, the proposed method was superior to PICCS in the aspect of updating locally‐mismatched region.Conclusion:The proposed method not only improves the image quality of 4D CBCT by adaptive updates during the reconstruction process, but also leads to a lower imaging dose and faster acquisition time by using a regular 3D CBCT scan.

To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

The image quality (IQ) of imaging systems must be sufficiently high for image‐guided radiation therapy (IGRT). Hence, users should implement a quality assurance program to maintain IQ. In our routine IQ tests of the kV cone‐beam CT system (Elekta XVI), image noise was quantified by noise standard deviation (NSD), which was the standard deviation of CT numbers measured in a small area in an image of an IQ test phantom (Catphan), and the high spatial resolution (HSR) was evaluated by the number of line‐pairs (LPN) visually recognizable in the image. We also measured the image uniformity, the low contrast resolution, and the distances of two points for geometrical accuracy. For this study, we did an additional evaluation of the XVI data for 12 monthly IQ tests by using noise power spectrum (NPS) for noise, modulation transfer function (MTF) for HSR, and CT number‐to‐density relationship. NPS was obtained by applying Fourier analysis in a small area on the uniformity test section of Catphan. The MTF analysis was performed by applying the Droege‐Morin (D‐M) method to the line‐pair bar regions in the phantom. The CT number‐to‐density relationship was obtained for insert materials in the low‐contrast test section of the phantom. All the quantities showed a noticeable change over the one‐year period. Especially the noise level improved significantly after a repair of the imager. NPS was more sensitive to the IQ change than NSD. MTF could provide more quantitative and objective evaluation of HSR. The CT number was very different from the expected CT number, but the CT number‐to‐density curves were constant within 5% except for two months. Since the D‐M method is easy to implement, we recommend using MTF instead of LPN even for routine QA. The IQ of the imaging systems was constantly changing; hence, IQ tests should be periodically performed. Additionally, we found the importance of IQ tests after every service work, including detector calibration as well as preventive maintenance.PACS number(s): 87.56Da, 87.57.C‐, 87.57.N‐, 8757.Q‐

Purpose: Liver SBRT patients unable to tolerate breath-hold for radiotherapy are treated free-breathing with image guidance. Target localization using 3D CBCT requires extra margins to accommodate the respiratory motion. The purpose of this study is to evaluate the accuracy and reproducibility of 4D CT-on-rails in target localization for free-breathing liver SBRT. Methods: A Siemens SOMATOM CT-on-Rails 4D with Anzai Pressure Belt system was used both as the simulation and the localization CT. Fiducial marker was placed close to the center of the target prior to the simulation. Amplitude based sorting was used in the scan. Eight or sixteen phases of reconstructed CT sets (depends on breathing pattern) can be sent to Velocity to create the maximum intensity projection (MIP) image set. Target ITV and fiducial ITV were drawn based on the MIP image. In patient localization, a 4D scan was taken with the same settings as the sim scan. Images were registered to match fiducial ITVs. Results: Ten liver cancer patients treated for 50Gy over 5 fractions, with amplitudes of breathing motion ranging from 4.3–14.5 mm, were analyzed in this study. Results show that the Intra & inter fraction variability in liver motion amplitude significantly less than the baseline inter-fraction shifts in liver position. 90% of amplitude change is less than 3 mm. The differences in the D99 and D95 GTV dose coverage between the 4D CT-on-Rails and the CBCT plan were small (within 5%) for all the selected cases. However, the average PTV volume by using the 4D CT-on-Rails is 37% less than the CBCT PTV volume. Conclusion: Simulation and Registration using 4D CT-on-Rails provides accurate target localization and is unaffected by larger breathing amplitudes as seen with 3D CBCT image registration. Localization with 4D CT-on-Rails can significantly reduce the PTV volume with sufficient tumor

The optimization of dual-energy computed tomography (DE-CT) is challenged by the lack of a theoretical foundation for image quality. This work reports a cascaded systems analysis model that was used to derive signal and noise propagation in DE-CBCT in prevalent Fourier metrics such as the noise-power spectrum (NPS) and noise-equivalent quanta (NEQ). The model was validated in comparison to measurements of the 3D NPS and NEQ in DE-CBCT images acquired using an experimental imaging bench. Task-based detectability index was derived using DE-NPS and NEQ as an objective function in optimizing DE imaging parameters such as the dose allocation factor (DA) and kVp pair. The resulting dose allocation optimization is in agreement with the practice of assigning more dose to the high-energy image (DA < 0.5), and the model provides a quantitative basis for examining the optimal dose allocation as a function of total dose, kVp pair, the presence of electronics noise, and the imaging task. An example optimization is shown for a breast tumor detection task. Using DE decomposition to cancel fibroglandular tissue (rendering a DE-CBCT image of breast tumor against an adipose tissue background) and assuming a total dose of 15mGy, the optimal kVp pair is identified at [45, 105]kVp with DA=0.46. The model is sufficiently general for applications beyond this example, demonstrating utility in the optimization in a broad range of imaging parameters. The model provides a new, valuable framework for understanding the theoretical limits of DE-CBCT imaging performance and maximizing image quality while minimizing radiation dose.

Purpose: The purpose of this study was to develop a practical method for estimating organdoses from kilovoltage cone‐beam CT(CBCT) that can be performed with readily available phantoms and dosimeters. The accuracy of organdose estimates made using the ImPACT patient dose calculator was also evaluated. Method and Materials: A 100 mm pencil chamber and standard CTdose index (CTDI) phantoms were used to measure the cone‐beam dose index (CBDI). A weighted CBDI (CBDIw) was then calculated from these measurements to represent the average volumetric dose in the CTDI phantom. By comparing CBDIw to previously published organdoses [J Appl Clin Med Phys., 2010], organdose conversion coefficients were developed. The measured CBDI values were also used as inputs for the ImPACT calculator to estimate organdoses. All CBDI dose measurements were performed on both the Elekta XVI and Varian OBI using factory installed protocols at three clinically relevant locations: head, chest, and pelvis. Results: The head, chest, and pelvis protocols yielded CBDIw values of 0.98, 16.62, and 24.13 mGy for the XVI system and 5.17, 6.14, and 21.57 mGy for the OBI system, respectively. Organdoses estimated with the ImPACT CTdose calculator showed a large range of variation from previously measured organdoses, demonstrating its limitations for use with CBCT.Conclusion: The organdose conversion coefficients developed in this work relate CBDIw values to organdoses previously measured using the same factory installed clinical protocols. Ultimately, these coefficients will allow for the quick estimation of organdoses from routine measurements performed using standard CTDI phantoms and pencil chambers.

Different dental imaging technologies are now daily used in clinical practice to evaluate oral anatomy. These new techniques allow to replace dental plaster casts with digital models that are easier to manage and store. Such models can be acquired with optical methods like laser scanner, stereophotogrammetry and intraoral scanner or reconstructed by 3D CT or CBCT images [1]. Since these digital casts are used in clinical routine, it is important to evaluate accuracy and reliability of measurements taken from them, in relation to traditional methods [2]. We wanted to compare linear measurements taken on digital models obtained from CBCT images and laser scanner surfaces, with direct measurements obtained with digital calliper on dental plaster casts. Data from 6 adult Caucasian subjects with full dentition, no history of implant surgery and without dental filling were obtained. The absence of implants and metal fillings was selected as inclusion criterion to reduce the presence of metal artefacts that can affect the measurement process. All patients were retrospectively selected from a clinical database and underwent CBCT examination for clinical reasons uncorrelated with this study. Six dental distances in the upper and six in the lower jaw were examined: the mesio-distal distance of teeth 21, 23, 24 and 26, the palatal-vestibular distance of teeth 24 and 26, and the corresponding distances on teeth 41, 43, 44 and 46. All measurements were performed using: 1) a digital calliper on dental plaster casts; 2) a virtual calliper on digital models obtained from CBCT images; and 3) a virtual calliper on laser scanner surfaces. Kruskal-Wallis test compared measurements performed with the 3 different techniques. There was no statistical significant difference among different techniques for all measurements (p&gt;0.05) except for one distance, the mesio-distal distance of tooth 24 (p

Photon counting CT versus multi-slice CT for radiographic evaluation of postoperative cochlear implantation: Electrode characteristics and image-quality analysis.

This study aimed to evaluate the image quality-dose trade-off in pregnant patients imaged with long-axial field-of-view [18F]FDG PET/CT and to identify the most predictive body composition metric for image quality to develop a pregnancy-tailored dosage model. Patients imaged with [18F]FDG PET/CT according to local pregnancy protocols were included in this study. Using raw PET data, images of various degrees of image quality were reconstructed. Acceptable image quality was identified using signal-to-noise ratio (SNR) in the liver and Likert scores. The minimum required scan statistics was modelled based on SNR and patient body composition. F-tests were used to find the best-fitting model parameter out of weight, weight-to-height-ratio, body-mass-index, and body surface area (BSA). Foetal dose was estimated with PET conversion factors and size-specific CT dose index values. Eleven patients were included in image quality analysis and dosage model optimization. SNR strongly correlated with Likert scores (R² = 0.80), with 10.72 SNR indicating acceptable image quality. BSA best predicted image quality (R² = 0.85), outperforming weight (R² = 0.78), weight-to-height ratio (R² = 0.63), and body mass index (R² = 0.38). The proposed dosage model reduces activity by 41-96% compared to current local pregnancy and adult protocols, resulting in estimated foetal radiation doses of 0.066 mGy (PET) and 0.31 mGy (CT). BSA accurately predicted [18F]FDG PET/CT image quality in pregnant patients. The proposed dosage regimen allows significant dose reduction and can be used as a foundation for the development of pregnancy dosage protocols.