3D CT Images Research Articles

Comparing assisting technologies for proficiency in cardiac morphology: 3D printing and mixed reality versus CT slice images for morphological understanding of congenital heart defects by medical students

AbstractLearning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students′ understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images.

Entry point and screw path study of second sacral alar-iliac screw trajectory in spinal pelvic fixation under O-arm navigation

Objective: To explore the feasibility of using the entry point and screw path parameters of sacroiliac (S2AI) screws inserted under O-arm 3D computer navigation as a reference for freehand screw insertion in patients with degenerative spinal deformities. Methods: A retrospective analysis was conducted on the clinical data of 66 patients with degenerative spinal deformities who received S2AI screw fixation assisted by the O-arm 3D computer navigation system at Nanjing Drum Tower Hospital Affiliated with Nanjing University Medical School from January 2017 to April 2022. The patients included 6 males and 60 females, with a mean age of (64.3±5.9) years. Preoperatively, the entry point was set as the intersection of 1 mm from the outer and lower edges of the S1 foramen, adjusted intraoperatively by navigation, and verified postoperatively by full spinal CT scans and 3D reconstructions. Using the S1 screw entry point as the origin, the position of the bilateral S2AI screw entry points, tailward angulation (SA), outward angulation (TA), and vertical distance from the entry point to the skin (SD) were measured on postoperative CT 3D images. The accuracy of S2AI screw placement and any intraoperative and postoperative complications were recorded. Differences in entry point coordinates and screw path parameters between the left and right sides were compared. The intraclass correlation coefficient (ICC) was used to assess intra-observer and inter-observer agreement. Results: The coordinates and screw path parameters for the S2AI screws placed using the O-arm navigation demonstrated good intra-observer and inter-observer consistency (ICC>0.75). The left entry point was located (8.08±1.39) mm laterally and (24.47±2.20) mm caudally from the S1 entry point, while the right entry point was (8.09±1.41) mm laterally and (24.40±2.54) mm caudally, with no significant difference between the left and right sides (both P>0.05). The left TA was 46.33°±3.44°, SA was 39.14°±6.12°, and SD was (60.38±13.37) mm; the right TA was 46.37°±3.41°, SA was 39.59°±5.89°, and SD was (60.30±12.24) mm, with no significant differences between the left and right parameters (all P>0.05). There was no significant neurovascular complications intraoperatively or two weeks postoperatively, with a screw insertion accuracy of 97.7% (129/132). Conclusions: In the fixation of the pelvis in degenerative spinal deformities, the use of O-arm navigation-assisted S2AI screw insertion has a high accuracy rate. It is recommended that for freehand screw insertion, the entry point should be located 24 mm caudally and 8 mm laterally from the outer and lower edges of the superior articular process of S1, with an TA of approximately 46° and a SA of approximately 39°.

Semantics and instance interactive learning for labeling and segmentation of vertebrae in CT images

Automatically labeling and segmenting vertebrae in 3D CT images compose a complex multi-task problem. Current methods progressively conduct vertebra labeling and semantic segmentation, which typically include two separate models and may ignore feature interaction among different tasks. Although instance segmentation approaches with multi-channel prediction have been proposed to alleviate such issues, their utilization of semantic information remains insufficient. Additionally, another challenge for an accurate model is how to effectively distinguish similar adjacent vertebrae and model their sequential attribute. In this paper, we propose a Semantics and Instance Interactive Learning (SIIL) paradigm for synchronous labeling and segmentation of vertebrae in CT images. SIIL models semantic feature learning and instance feature learning, in which the former extracts spinal semantics and the latter distinguishes vertebral instances. Interactive learning involves semantic features to improve the separability of vertebral instances and instance features to help learn position and contour information, during which a Morphological Instance Localization Learning (MILL) module is introduced to align semantic and instance features and facilitate their interaction. Furthermore, an Ordinal Contrastive Prototype Learning (OCPL) module is devised to differentiate adjacent vertebrae with high similarity (via cross-image contrastive learning), and simultaneously model their sequential attribute (via a temporal unit). Extensive experiments on several datasets demonstrate that our method significantly outperforms other approaches in labeling and segmenting vertebrae. Our code is available at https://github.com/YuZhang-SMU/Vertebrae-Labeling-Segmentation

Deep learning enables the fully automated quantification of high-resolution cardiac CT images: human in loop for model development

Abstract Introduction Cardiac computed tomography (CCT) images provide vast spatial and temporal information. Segmentation of the structure of interest is necessary to provide quantitative information. However, manual segmentation of cardiac substructures is highly time-consuming. Objective This study aims to develop a robust deep-learning segmentation model for cardiac substructure segmentation in 3D CCT images. Method We initially developed a 3D U-Net architecture, incorporating extensive augmentation, for the segmentation of the Left Ventricle Myocardium (LVM), the blood cavities of the Left and Right Ventricles (LV, RV), and the Left and Right Atria (LA, RA). This model utilized CT images from 20 patients who had available manual segmentation. Subsequently, we applied this initial model to datasets from two centers, including different generations of scanners. We then visually inspected the segmentation, selected the highest-quality segmentations, and modify it if necessary, and excluded the low-quality data. We continued fine-tuning the model with the new dataset until we reached a count of 456 patients. As there were multiple images from one patient during this model's development, we generated datasets to develop a new model using the entire new dataset to avoid any data leakage in the reported metrics. The model was developed using data from Center 1 (5-fold cross-validation) and then evaluated on an external test set from Center 2 (126 patients) and also 4D images from Center 1. Various metrics were calculated to assess the model's performance. Results In the internal test set, utilizing 5-fold CV, the model demonstrated high segmentation accuracy, achieving an overall Dice coefficient of 0.98, with individual scores of 0.96 for LVM, 0.98 for LV, 0.98 for RV, 0.99 for RA, and 0.98 for LA. We assessed the automatic quantification capability of the segmentation model from 256 patients, resulting in an R2 value greater than 0.92 for both LV end-diastolic and systolic volumes. In contrast, the model maintained robust performance on the external dataset, achieving an overall Dice coefficient of 0.96, with individual scores of 0.95 for LVM, 0.98 for LV, 0.97 for RV, 0.98 for RA, and 0.94 for LA. Conclusion We provided a large cohort of ground truth segmentations for different cardiac CT substructures using a human-in-the-loop strategy. Subsequently, we developed a deep learning model for highly accurate automated segmentation of these substructures. This model allows for fast and fully automatic segmentation and quantification of 3D and 4D cardiac CT images, from which various quantitative metrics can be extracted from different cardiac substructures.

Interactive segmentation with curve-based template deformation for spatiotemporal computed tomography of swallowing motion.

Repeating X-ray computed tomography (CT) measurements over a short period of time allows for obtaining a spatiotemporal four-dimensional (4D) volume image. This study presents an interactive method for segmenting a 4DCT image by fitting a template model to a target organ. The template consists of a three-dimensional (3D) mesh model and free-form-deformation (FFD) cage enclosing the mesh. The user deforms the template by placing multiple curve constraints that specify the boundary shape of the template in 3D space. We also present curve constraints shared over all time frames and interpolated along the time axis to facilitate efficient curve specification. Our method formulates the template deformation using the FFD cage modification, allowing the user to switch between our curve-based method and traditional FFD at any time. To illustrate the feasibility of our method, we show segmentation results in which we could accurately segment three organs from a 4DCT image capturing a swallowing motion. To evaluate the usability of our method, we conducted a user study comparing our curve-based method with the cage-based FFD. We found that the participants finished segmentation in approximately 20% interaction time periods on average with our method.

Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network.

Deep learning-based computed tomography (CT) ventilation imaging (CTVI) is a promising technique for guiding functional lung avoidance radiotherapy (FLART). However, conventional approaches, which rely on anatomical CT data, may overlook important ventilation features due to the lack of motion data integration. This study aims to develop a novel dual-aware CTVI method that integrates both anatomical information from CT images and motional information from Jacobian maps to generate more accurate ventilation images for FLART. A dataset of 66 patients with four-dimensional CT (4DCT) images and reference ventilation images (RefVI) was utilized to develop the dual-path fusion network (DPFN) for synthesizing ventilation images (CTVIDual). The DPFN model was specifically designed to integrate motion data from 4DCT-generated Jacobian maps with anatomical data from average 4DCT images. The DPFN utilized two specialized feature extraction pathways, along with encoders and decoders, designed to handle both 3D average CT images and Jacobian map data. This dual-processing approach enabled the comprehensive extraction of lung ventilation-related features. The performance of DPFN was assessed by comparing CTVIDual to RefVI using various metrics, including Spearman's correlation coefficients (R), Dice similarity coefficients of high-functional region (DSCh), and low-functional region (DSCl). Additionally, CTVIDual was benchmarked against other CTVI methods, including a dual-phase CT-based deep learning method (CTVIDLCT), a radiomics-based method (CTVIFM), a super voxel-based method (CTVISVD), a Unet-based method (CTVIUnet), and two deformable registration-based methods (CTVIJac and CTVIHU). In the test group, the mean R between CTVIDual and RefVI was 0.70, significantly outperforming CTVIDLCT (0.68), CTVIFM (0.58), CTVISVD (0.62), and CTVIUnet (0.66), with p<0.05. Furthermore, the DSCh and DSCl values of CTVIDual were 0.64 and 0.80, respectively, outperforming CTVISVD (0.63; 0.73) and CTVIUnet (0.62; 0.77). The performance of CTVIDual was also significantly better than that of CTVIJac and CTVIHU. A novel dual-aware CTVI model that integrates anatomical and motion information was developed to synthesize lung ventilation images. It was shown that the accuracy of lung ventilation estimation could be significantly enhanced by incorporating motional information, particularly in patients with tumor-induced blockages. This approach has the potential to improve the accuracy of CTVI, enabling more effective FLART.

Optimization of image reconstruction technique for respiratory-gated lung stereotactic body radiotherapy treatment planning using four-dimensional CT: a phantom study.

Patient respiration is characterized by respiratory parameters, such as cycle, amplitude, and baseline drift. In treatment planning using four-dimensional computed tomography (4DCT) images, the target dose may be affected by variations in image reconstruction techniques and respiratory parameters. This study aimed to optimize 4DCT image reconstruction techniques for the treatment planning of lung stereotactic body radiotherapy (SBRT) based on respiratory parameters using respiratory motion phantom. We quantified respiratory parameters using 30 respiratory motion datasets. The 4DCT images were acquired, and the phase- and amplitude-based reconstruction images (RI) were created. The target dose was calculated based on these reconstructed images. Statistical analysis was performed using Pearson's correlation coefficient (r) to determine the relationship between respiratory parameters and target dose in each reconstructed technique and respiratory region. In the inhalation region of phase-based RI, r of the target dose and baseline drift was -0.52. In particular, the target dose was significantly reduced for respiratory parameters with a baseline drift of 0.8mm/s and above. No other respiratory parameters or respiratory regions were significantly correlated with target dose in phase-based RI. In amplitude-based RI, there were no significant differences in the correlation between all respiratory parameters and target dose in the exhalation or inhalation regions. These results showed that the target dose of the amplitude-based RI did not depend on changes in respiratory parameters or respiratory regions, compared to the phase-based RI. However, it is possible to guarantee the target dose by considering respiratory parameters during the inhalation region of the phase-based RI.

Preservation of the MMPH and Status of the Notch Configurations Are Important Factors for Successful Revision ACL Reconstruction.

The aim of this study was to identify the differences in anatomical factors between the patients who underwent revision and primary anterior cruciate ligament reconstructions (ACLR) and compare the outcomes between the 2 surgeries. Retrospective cohort study. Single institution tertiary academic center between 2014 and 2020. A total of 84 matched patients who underwent revision (group I) or primary ACLR (group II). Radiological evaluations were performed using side-to-side differences (SS-D), femorotibial angle, and posterior tibial slope. The medial meniscus posterior horn status as width (MMPHW), medial tibial plateau width (MTPW), notch configurations, tunnel positions, and sagittal graft angle were measured using 3D-CT and magnetic resonance imaging (MRI). Graft signals were assessed using MRI. The clinical outcomes were assessed. Group I showed larger SS-D and higher mean Howell grade of anterior cruciate ligament (ACL) graft than group II (P = 0.002 and P = 0.004, respectively). Multivariate regression analyses showed that the decreased MMPHW/MTPW ratio (P = 0.010) and notch width index (P < 0.007) were significantly independent factors associated with the higher Howell grade of ACL grafts. The decreased MMPHW/MTPW ratio (P < 0.001) was a significantly independent factor associated with larger SS-D. In the subgroup analysis, all patients in the notchplasty group showed wider notches postoperatively (P < 0.001). Revision ACLRs resulted in worse ACL signal intensity and stability outcome. The results of this study suggest that it may be important to preserve the MMPHW as much as possible and check notch configurations, especially during a revision ACLR.

Fast reconstruction 3D computed tomography image of stacked cell under faster scanning by dual-branch cross-fusion flat bottom network

Abstract Cone beam computed tomography (CBCT) fast scanning and reconstruction is a key step to achieve rapid detection of internal defects in batteries. In this work, we have achieved a faster CT scanning just in 5 seconds by reducing the X-ray exposure time in sparse view CT. However, the CT data is extremely incomplete by faster scanning; the existing reconstruction methods are difficult to reconstruct a high quality three-dimensional (3D) CT image of stacked cells. To address this issue, we propose a 3D CT image reconstruction network, which can reconstruct higher quality CT images from low quality 3D volume data. The input data of the reconstruction network is not 2D projection data, but 3D volume data. In this network, a high and low resolution dual-branch cross-fusion flat bottom structure is designed. The high resolution flat bottom branch aims to preserve detailed information, while the low resolution flat bottom branch focuses on capturing more semantic information. Cross-fusion between these branches mitigates the loss of semantic details. Additionally, the auxiliary loss function, the main loss function, and the 3D attention module are designed to enhance semantic accuracy and the learning performance of the network. The 3D training data is collected under a fast scanning strategy spanning 5-60 seconds. During the training phase, we use clipping block technology to cut the 3D volume data, enabling direct training on the 3D volume data. Our experimental results demonstrate that our 3D reconstruction network outperforms mainstream algorithms under this faster scanning strategy, which is able to reconstruct higher quality 3D CT images just in 15 seconds. Ablation experiments confirm the positive impact of the dual-branch cross-fusion flat bottom structure, attention module, and loss functions on improving the quality of 3D CT images.

Searching Method for Three-Dimensional Puncture Route to Support Computed Tomography-Guided Percutaneous Puncture.

In CT-guided percutaneous punctures-an image-guided puncture method using CT images-physicians treat targets such as lung tumors, liver tumors, renal tumors, and intervertebral abscesses by inserting a puncture needle into the body from the exterior while viewing images. By recognizing two-dimensional CT images prior to a procedure, a physician determines the least invasive puncture route for the patient. Therefore, the candidate puncture route is limited to a two-dimensional region along the cross section of the human body. In this paper, we aim to construct a three-dimensional puncture space based on multiple two-dimensional CT images to search for a safer and shorter puncture route for a given patient. If all puncture routes starting from a target in the three-dimensional space were examined from all directions (the brute-force method), the processing time to derive the puncture route would be very long. We propose a more efficient method for three-dimensional puncture route selection in CT-guided percutaneous punctures. The proposed method extends the ray-tracing method, which quickly derives a line segment from a given start point to an end point on a two-dimensional plane, and applies it to three-dimensional space. During actual puncture route selection, a physician can use CT images to derive a three-dimensional puncture route that is safe for the patient and minimizes the puncture time. The main novelty is that we propose a method for deriving a three-dimensional puncture route within the allowed time in an actual puncture. The main goal is for physicians to select the puncture route they will use in the actual surgery from among the multiple three-dimensional puncture route candidates derived using the proposed method. The proposed method derives a three-dimensional puncture route within the allowed time in an actual puncture. Physicians can use the proposed method to derive a new puncture route, reducing the burden on patients and improving physician skills. In the evaluation results of a computer simulation, for a 3D CT image created by combining 170 two-dimensional CT images, the processing time for deriving the puncture route using the proposed method was approximately 59.4 s. The shortest length of the puncture route from the starting point to the target was between 20 mm and 22 mm. The search time for a three-dimensional human body consisting of 15 CT images was 4.77 s for the proposed method and 2599.0 s for a brute-force method. In a questionnaire, physicians who actually perform puncture treatments evaluated the candidate puncture routes derived by the proposed method. We confirmed that physicians could actually use these candidates as a puncture route.

Refining the role of presurgical PET/4D-CT in a large series of patients with primary hyperparathyroidism undergoing [18F]Fluorocholine PET/CT.

4D-CT has garnered attention as complementary imaging for patients with primary hyperparathyroidism (pHPT). Herein we evaluated a diagnostic strategy using [18F]Fluorocholine Positron Emission Tomography/Computed Tomography (PET/CT), followed by 4D-CT integrated into PET/4D-CT after negative/inconclusive PET/CT results in a single-center retrospective cohort of 166 pHPT patients who underwent parathyroidectomy after [18F]Fluorocholine PET/4D-CT. PET/CT and 4D-CT images were interpreted by three nuclear medicine physicians and one expert radiologist. Pathological findings were documented, and concordance rates were assessed. PET/CT results were categorized as positive/negative, with positive cases rated on a 3-level certitude scale: low, moderate, high. Inconclusive cases included low/moderate positivity. The added value of PET/4D-CT was assessed for negative/inconclusive cases through joint reading. PET/CT lesion-based analysis showed almost perfect interobserver concordance (Cohen's kappa >.8). Across the cohort, PET/CT had a sensitivity of 83%, specificity of 97%, PPV of 90% and NPV of 94%. For 4D-CT, these values were sensitivity: 53%, specificity: 84%, PPV: 56% and NPV: 82%. PET/CT was significantly more accurate than 4D-CT. Among 44 patients with negative/inconclusive results, PET/CT had sensitivity: 60%, specificity: 91%, PPV: 71% and NPV: 86%. In the same patients, sensitivity and specificity of the sequential diagnostic algorithm increased to 80% and 97%, showing significantly better global accuracy (92% vs. 83%) than standard PET/CT. We support a personalized imaging algorithm for pHPT, placing [18F]Fluorocholine PET/CT at the forefront, followed by 4D-CT integrated into PET/4D-CT in the same imaging session for negative/inconclusive results. When PET/CT results are clearly positive, the additional sensitivity benefit of 4D-CT is minimal.

Prospective study on the impact of different antithrombotic therapies on subclinical leaflet thickening and its temporal dynamics in transcatheter aortic valves—The NOTION-4 trial

Transcatheter aortic valve replacement (TAVR) has become the standard-of-care treatment for a majority of patients with severe, symptomatic aortic stenosis. The post-procedural anti-thrombotic therapeutic management is still a topic of debate and could affect the incidence of HALT, a phenomenon which can be assessed by four-dimensional computed tomography (4DCT). The NOTION-4 trial is a randomized controlled trial comprising TAVR patients with no indication for oral anticoagulant (OAC) therapy, comparing lifelong single anti-platelet therapy (standard arm) versus early 3-month direct oral anticoagulant (DOAC) therapy followed by single anti-plateletet therapy (experimental arm). The incidence of HALT and clinical endpoints will be evaluated in both groups at 3 months, 1 year and 5 years after randomization. The primary endpoint is the number of patients with at least one bioprosthetic aortic valve leaflet with HALT as assessed by cardiac 4DCT imaging at 1 year. The trial is powered for superiority testing and started enrollment in 2021. In total, 324 patients will be included. The last patient is expected to be enrolled by the end of 2024 and the primary endpoint is to be presented in 2026. The NOTION-4 trial aims to study whether an early 3-month DOAC therapy after TAVR can result in a sustained lower incidence of HALT in transcatheter aortic valves. This trial holds the potential to give valuable insights into whether early OAC therapy should be integrated in future guidelines for post-TAVR anti-thrombotic therapeutic management. NOTION-4, ClinicalTrials.gov ID NCT06449469, https://clinicaltrials.gov/study/NCT06449469.

A Hybrid Trio-Deep Feature Fusion Model for Improved Skin Cancer Classification: Merging Dermoscopic and DCT Images

A Hybrid Trio-Deep Feature Fusion Model for Improved Skin Cancer Classification: Merging Dermoscopic and DCT Images

Development of a novel 3D-printed dynamic anthropomorphic thorax phantom for evaluation of four-dimensional computed tomography

Background and purposeIn radiotherapy, the image quality of four-dimensional computed tomography (4DCT) is often degraded by artifacts resulting from breathing irregularities. Quality assurance mostly employ simplistic phantoms, not fully representing complexities and dynamics in patients. 3D-printing allows for design of highly customized phantoms. This study aims to validate the proof-of-concept of a realistic dynamic thorax phantom and its 4DCT application. Materials and methodsUsing 3D-printing, a realistic thorax phantom was produced with tissue-equivalent materials for soft tissue, bone, and compressible lungs, including bronchi and tumors. Lung compression was facilitated by motors simulating customized breathing curves with an added platform for application of monitoring systems. The phantom contained three tumors which were assessed in terms of tumor motion amplitude. Three 4DCT sequences and repeated static images for different lung compression levels were acquired to evaluate the reproducibility. Moreover, more complex patient-specific breathing patterns with irregularities were simulated. ResultsThe phantom showed a reproducibility of ±0.2 mm and ±0.4 mm in all directions for static 3DCT images and 4DCT images, respectively. Furthermore, the tumor close to the diaphragm showed higher amplitudes in the inferior/superior direction (13.9 mm) than lesions higher in the lungs (8.1 mm) as observed in patients. The more complex breathing patterns demonstrated commonly seen 4DCT artifacts. ConclusionThis study developed a dynamic 3D-printed thorax phantom, which simulated customized breathing patterns. The phantom represented a realistic anatomy and 4DCT scanning of it could create realistic artifacts, making it beneficial for 4DCT quality assurance or protocol optimization.

MedLSAM: Localize and segment anything model for 3D CT images

Recent advancements in foundation models have shown significant potential in medical image analysis. However, there is still a gap in models specifically designed for medical image localization. To address this, we introduce MedLAM, a 3D medical foundation localization model that accurately identifies any anatomical part within the body using only a few template scans. MedLAM employs two self-supervision tasks: unified anatomical mapping (UAM) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. Furthermore, we developed MedLSAM by integrating MedLAM with the Segment Anything Model (SAM). This innovative framework requires extreme point annotations across three directions on several templates to enable MedLAM to locate the target anatomical structure in the image, with SAM performing the segmentation. It significantly reduces the amount of manual annotation required by SAM in 3D medical imaging scenarios. We conducted extensive experiments on two 3D datasets covering 38 distinct organs. Our findings are twofold: (1) MedLAM can directly localize anatomical structures using just a few template scans, achieving performance comparable to fully supervised models; (2) MedLSAM closely matches the performance of SAM and its specialized medical adaptations with manual prompts, while minimizing the need for extensive point annotations across the entire dataset. Moreover, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced segmentation performance. Our code is public at https://github.com/openmedlab/MedLSAM.

Impact of breathing signal-guided dose modulation on step-and-shoot 4DCT image reconstruction.

Breathing signal-guided 4DCT sequence scanning such as the intelligent 4DCT (i4DCT) approach reduces imaging artifacts compared to conventional 4DCT. By design, i4DCT captures entire breathing cycles during beam-on periods, leading to redundant projection data and increased radiation exposure to patients exhibiting prolonged exhalation phases. A recently proposed breathing-guided dose modulation (DM) algorithm promises to lower the imaging dose by temporarily reducing the CT tube current, but the impact on image reconstruction and the resulting images have not beeninvestigated. We evaluate the impact of breathing signal-guided DM on 4DCT image reconstruction and correspondingimages. This study is designed as a comparative and retrospective analysis based on 104 4DCT datasets. Each dataset underwent retrospective reconstruction twice: (a) utilizing the acquired clinical projection data for reconstruction, which yields reference image data, and (b) excluding projections acquired during potential DM phases from image reconstruction, resulting in DM-affected image data. Resulting images underwent automatic organ segmentation (lung/liver). (Dis)Similarity of reference and DM-affected images were quantified by the Dice coefficient of the entire organ masks and the organ overlaps within the DM-affected slices. Further, for lung cases, (a) and (b) were deformably registered and median magnitudes of the obtained displacement field were computed. Eventually, for 17 lung cases, gross tumor volumes (GTV) were recontoured on both (a) and (b). Target volume similarity was quantified by the Hausdorffdistance. DM resulted in a median imaging dose reduction of 15.4% (interquartile range [IQR]: 11.3%-19.9%) for the present patient cohort. Dice coefficients for lung ( ) and liver ( ) patients were consistently high for both the entire organs and the DM-affected slices (IQR lung: [entire lung/DM-affected slices only] to ; IQR liver: to ), demonstrating that DM did not cause organ distortions or alterations. Median displacements for DM-affected to reference image registration varied; however, only two out of 73 cases exhibited a median displacement larger than one isotropic 1 voxel size. The impact on GTV definition for the end-exhalation phase was also minor (median Hausdorff distance: 0.38mm, IQR: 0.15-0.46mm). This study demonstrates that breathing signal-guided DM has a minimal impact on image reconstruction and image appearance while improving patient safety by reducing doseexposure.

A novel internal target volume definition based on velocity and time of respiratory target motion for external beam radiotherapy.

This study aimed to develop a novel internal target volume (ITV) definition for respiratory motion targets, considering target motion velocity and time. The proposed ITV was evaluated in respiratory-gated radiotherapy. An ITV modified with target motion velocity and time (ITVvt) was defined as an ITV that includes a target motion based on target motion velocity and time. The target motion velocity was calculated using four-dimensional computed tomography (4DCT) images. The ITVvts were created from phantom and clinical 4DCT images. The phantom 4DCT images were acquired using a solid phantom that moved with a sinusoidal waveform (peak-to-peak amplitudes of 10 and 20mm and cycles of 2-6s). The clinical 4DCT images were obtained from eight lung cancer cases. In respiratory-gated radiotherapy, the ITVvt was compared with conventional ITVs for beam times of 0.5-2s within the gating window. The conventional ITV was created by adding a uniform margin as the maximum motion within the gating window. In the phantom images, the maximum volume difference between the ITVvt and conventional ITV was -81.9%. In the clinical images, the maximum volume difference was -53.6%. Shorter respiratory cycles and longer BTs resulted in smaller ITVvt compared with the conventional ITV. Therefore, the proposed ITVvt plan could be used to reduce treatment volumes and doses to normal tissues.

Evaluation of the geometric and dosimetric accuracies of deformable image registration of targets and critical organs in prostate CBCT-guided adaptive radiotherapy.

Kilovoltage cone beam computed tomography (kVCBCT)-guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART. We evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity-based deformable registration, (2) hybrid deformable registration, and (3) a two-step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD95) and Dice similarity coefficient (DSC), and each method was compared with the intensity-based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal-to-noise ratio (SNR), uniformity, and contrast-to-noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose-volume histogram (DVH) and 3.0%/0.3mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference. The dCT images generated by the hybrid (dCTH) and two-step (dCTC) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCTH and dCTC methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCTH and dCTC methods, respectively. For the geometric evaluation, only the two-step registration method improved registration accuracy. The dCTH method yielded an average HD95 of 12mm and average DSC of 0.73, whereas dCTC yielded an average HD95 of 2.9mm and average DSC of 0.902. The DVH showed that the dCTC-based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCTC-based treatment plans were >95% at all points, whereas they were <95% for kVCBCT-based treatment plans. The two-step registration method outperforms the intensity-based and hybrid registration methods. While the hybrid and two-step-based methods improved the image quality of kVCBCT in a linear accelerator, only the two-step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two-step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer.

Impact of interplay effects on spot scanning proton therapy with motion mitigation techniques for lung cancer: SFUD versus robustly optimized IMPT plans utilizing a four-dimensional dynamic dose simulation tool

BackgroundThe interaction between breathing motion and scanning beams causes interplay effects in spot-scanning proton therapy for lung cancer, resulting in compromised treatment quality. This study investigated the effects and clinical robustness of two types of spot-scanning proton therapy with motion-mitigation techniques for locally advanced non-small cell lung cancer (NSCLC) using a new simulation tool (4DCT-based dose reconstruction).MethodsThree-field single-field uniform dose (SFUD) and robustly optimized intensity-modulated proton therapy (IMPT) plans combined with gating and re-scanning techniques were created using a VQA treatment planning system for 15 patients with locally advanced NSCLC (70 GyRBE/35 fractions). In addition, gating windows of three or five phases around the end-of-expiration phase and two internal gross tumor volumes (iGTVs) were created, and a re-scanning number of four was used. First, the static dose (SD) was calculated using the end-of-expiration computed tomography (CT) images. The four-dimensional dynamic dose (4DDD) was then calculated using the SD plans, 4D-CT images, and the deformable image registration technique on end-of-expiration CT. The target coverage (V98%, V100%), homogeneity index (HI), and conformation number (CN) for the iGTVs and organ-at-risk (OAR) doses were calculated for the SD and 4DDD groups and statistically compared between the SD, 4DDD, SFUD, and IMPT treatment plans using paired t-test.ResultsIn the 3- and 5-phase SFUD, statistically significant differences between the SD and 4DDD groups were observed for V100%, HI, and CN. In addition, statistically significant differences were observed for V98%, V100%, and HI in phases 3 and 5 of IMPT. The mean V98% and V100% in both 3-phase plans were within clinical limits (> 95%) when interplay effects were considered; however, V100% decreased to 89.3% and 94.0% for the 5-phase SFUD and IMPT, respectively. Regarding the significant differences in the deterioration rates of the dose volume histogram (DVH) indices, the 3-phase SFUD plans had lower V98% and CN values and higher V100% values than the IMPT plans. In the 5-phase plans, SFUD had higher deterioration rates for V100% and HI than IMPT.ConclusionsInterplay effects minimally impacted target coverage and OAR doses in SFUD and robustly optimized IMPT with 3-phase gating and re-scanning for locally advanced NSCLC. However, target coverage significantly declined with an increased gating window. Robustly optimized IMPT showed superior resilience to interplay effects, ensuring better target coverage, prescription dose adherence, and homogeneity than SFUD.Trial registration: None.

A 3D U-NET BASED ON EARLY FUSION MODEL: IMPROVEMENT, COMPARATIVE ANALYSIS WITH STATE-OF-THE-ART MODELS AND FINE-TUNING

Multi-organ segmentation is the process of identifying and separating multiple organs in medical images. This segmentation allows for the detection of structural abnormalities by examining the morphological structure of organs. Carrying out the process quickly and precisely has become an important issue in today's conditions. In recent years, researchers have used various technologies for the automatic segmentation of multiple organs. In this study, improvements were made to increase the multi-organ segmentation performance of the 3D U-Net based fusion model combining HSV and grayscale color spaces and compared with state-of-the-art models. Training and testing were performed on the MICCAI 2015 dataset published at Vanderbilt University, which contains 3D abdominal CT images in NIfTI format. The model's performance was evaluated using the Dice similarity coefficient. In the tests, the liver organ showed the highest Dice score. Considering the average Dice score of all organs, and comparing it with other models, it has been observed that the fusion approach model yields promising results.