Four-dimensional Computed Tomography Research Articles

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.

CT in musculoskeletal imaging: still helpful and for what?

Computed tomography (CT) is a common modality employed for musculoskeletal imaging. Conventional CT techniques are useful for the assessment of trauma in detection, characterization and surgical planning of complex fractures. CT arthrography can depict internal derangement lesions and impact medical decision making of orthopedic providers. In oncology, CT can have a role in the characterization of bone tumors and may elucidate soft tissue mineralization patterns. Several advances in CT technology have led to a variety of acquisition techniques with distinct clinical applications. These include four-dimensional CT, which allows examination of joints during motion; cone-beam CT, which allows examination during physiological weight-bearing conditions; dual-energy CT, which allows material decomposition useful in musculoskeletal deposition disorders (e.g., gout) and bone marrow edema detection; and photon-counting CT, which provides increased spatial resolution, decreased radiation, and material decomposition compared to standard multi-detector CT systems due to its ability to directly translate X-ray photon energies into electrical signals. Advanced acquisition techniques provide higher spatial resolution scans capable of enhanced bony microarchitecture and bone mineral density assessment. Together, these CT acquisition techniques will continue to play a substantial role in the practices of orthopedics, rheumatology, metabolic bone, oncology, and interventional radiology.

Four-dimensional dose evaluation for the liver stereotactic body radiotherapy with lipiodol retention after transcatheter arterial chemoembolization

PurposeTo evaluate the effect of lipiodol on the four-dimensional accumulated dose of liver stereotactic body radiotherapy (SBRT) after transcatheter arterial chemoembolization (TACE). Material and methodsTen patients who underwent liver SBRT with lipiodol retention after TACE were retrospectively analyzed. Free-breathing three-dimensional (3D) computed tomography (CT) and respiration-based four-dimensional (4D) CT scans were used. The tumor tissue and lipiodol region were delineated within the gross tumor volume (GTV). Volumetric modulated arc therapy plans were created using the X-ray Voxel Monte Carlo (XVMC), a fast MC algorithm. For 4D accumulated dose assessments, the dose was recalculated after copying the plan into each CT phase, and a deformable registration was used with the 3D CT as a reference. Additionally, dose variation due to tumor irradiation at the lipiodol location through motion was simulated by density override of planning target volume using tumor tissue density followed by dose recalculation. ResultsDose to the lipiodol region increased after density override with tumor density. In eight out of ten patients, 4D doses covering 98% of the GTV and tumor tissue increased compared to 3D doses. Notably, for both 3D and 4D cases, doses to the tumor tissue exceeded those of the GTV. Attention to GTV coverage in 3D planning for liver SBRT without considering lipiodol was deemed sufficient. The similar 19.2 Gy-liver volume for 3D and 4D allowed for liver evaluation using the 3D plan as a quick substitute. ConclusionsIn liver SBRT cases involving lipiodol after TACE, the XVMC planning system revealed higher 4D doses to tumor tissue compared to GTV, with 4D doses surpassing 3D. However, results were limited by XVMC accuracy, unable to reflect lipiodol's dose enhancement. For precise CT-based dose calculations involving lipiodol in the GTV, incorporating lipiodol's material information in the MC calculations is crucial.

Computed tomography-current status and future directions for arthritis imaging.

Applications of computed tomography (CT) in arthritis imaging have rapidly expanded in recent years due to ongoing technical developments. Dual-energy CT (DECT) has become indispensable in clinical practice, particularly for diagnosing gouty arthritis and assessing bony structural changes. Technological innovations such as low-dose CT and state-of-the-art reconstruction algorithms reduce radiation exposure while maintaining image quality and short acquisition times. This review explores the growing role of CT in arthritis imaging. Recent innovations have extended DECT's utility beyond gout diagnosis to the detection of inflammatory changes in various arthritic conditions. Postprocessing techniques such as the generation of subtraction images and iodine maps provide valuable insights into tissue perfusion and inflammatory activity, crucial for arthritis management. DECT can distinguish calcium from uric acid crystals, facilitating the differential diagnosis of various crystal arthropathies in a variety of clinical settings. This ability is particularly valuable in distinguishing between different clinical conditions in patients with inflammatory joint changes within a single imaging examination. Moreover, the advent of four-dimensional CT promises a better assessment of dynamic joint instabilities and ligament injuries, especially in the wrist. Overall, DECT offers a comprehensive approach to arthritis imaging, from the detection of structural changes to the assessment of active inflammation in joints and tendons. Continuous advances in CT technology, including photon-counting CT, hold promise for further improving diagnostic accuracy and expanding the role of CT in arthritis imaging and therapy monitoring.

Heart Dose Uncertainty Caused by Heartbeat during Adjuvant Breast Radiotherapy

Adjuvant radiotherapy is a crucial treatment option for breast cancer. Owing to the long survival after treatment, heart toxicity has become a critical issue in those who receive radiotherapy. The heart dose is calculated using the computed tomography (CT) images acquired during simulation. The heart volume is quite different between the systolic and diastolic phases. This study used four-dimensional (4D) CT to capture the heart image and determine the ranges of the heart dose in different phases of the heart. Patients with left-sided breast cancer who underwent breast conservative surgery followed by adjuvant radiotherapy to the entire breast between January 2016 and December 2021 were identified. The three-dimensional (3D) CT and 4DCT datasets of the patients were acquired to evaluate the variation in the heart dose. The heart volume in each respiratory phase was depicted in a box plot along with the dice similarity coefficient (DCS). A total of 32 patients met the inclusion criteria. The DCS of the heart in each respiration phase was within the range of 0.91 to 0.95. Variations in the heart volume and morphology were not evident. However, these variations were not influenced by the respiratory cycle. The median relative variations in the 4DCT volume, mean dose, V20 (≥20 Gy), and V40 (40 Gy) were 6%, 14%, 19%, and 32%, respectively. All parameters showed significant variation between the maximum values in 4DCT and planning 3DCT. When parameters in planning 3DCT were compared to minimum values in 4DCT, the heart volume and V20 still showed significant differences, whereas the mean dose (p = .052) and V40 (p = .151) did not. We found that the median variation in the heart volume during the respiratory cycle was 6%, which led to a 14% variation in the mean heart dose, 19% in V20, and 32% in V40. Dose variation should not be ignored. A more precise method for estimating the heart dose during treatment should be investigated.

Robust quantification of CT-ventilation biomarker techniques and repeatability in a porcine model.

Biomarkers estimating local lung ventilation have been derived from computed tomography (CT) imaging using various image acquisition and post-processing techniques. CT-ventilation biomarkers have potential clinical use in functional avoidance radiation therapy (RT), in which RT treatment plans are optimized to reduce dose delivered to highly ventilated lung. Widespread clinical implementation of CT-ventilation biomarkers necessitates understanding of biomarker repeatability. Performing imaging within a highly controlled experimental design enables quantification of error associated with remainingvariables. To characterize CT-ventilation biomarker repeatability and dependence on image acquisition and post-processing methodology in anesthetized and mechanically ventilatedpigs. Five mechanically ventilated Wisconsin Miniature Swine (WMS) received multiple consecutive four-dimensional CT (4DCT) and maximum inhale and exhale breath-hold CT (BH-CT) scans on five dates to generate CT-ventilation biomarkers. Breathing maneuvers were controlled with an average tidal volume difference <200 cc. As surrogates for ventilation, multiple local expansion ratios (LERs) were calculated from the acquired CT scans using Jacobian-based post-processing techniques. measured local expansion between an image pair using either inhale and exhale BH-CT images or two 4DCT breathing phase images. measured the maximum local expansion across the 4DCT breathing phase images. Breathing maneuver consistency, intra- and interday biomarker repeatability, image acquisition and post-processing technique dependence were quantitatively analyzed. Biomarkers showed strong agreement with voxel-wise Spearman correlation for intraday repeatability and for all other comparisons, including between image acquisition techniques. Intra- and interday repeatability were significantly different (p < 0.01). LER2 and LERN post-processing did not significantly affect intraday repeatability. 4DCT and BH-CT ventilation biomarkers derived from consecutive scans show strong agreement in controlled experiments with nonhumansubjects.

Imaging Recommendations for Diagnosis, Staging, and Management of Cancer of the Thyroid, Parathyroid, and Salivary Glands

AbstractThyroid cancer ranks as the leading endocrine malignancy in adults. The foundation for primary diagnosis of thyroid cancer is a high-resolution ultrasound (US) of the thyroid gland including US-guided fine-needle biopsy (FNB) of suspected thyroid nodules. Advanced cross-sectional imaging, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography, can be useful in selected patients. The mainstay of treatment of thyroid cancer is surgery. It may be supplemented by radioactive iodine ablation/therapy in high-risk differentiated thyroid cancer. Radiology plays a crucial role in both diagnostic and posttreatment follow-up imaging. Primary hyperparathyroidism (PHPT) is the third most common endocrine disorder with single parathyroid adenoma being its most common cause. The radiologist's aim in parathyroid imaging is to provide the clinician with an illustrative picture of the neck, locating lesions with respect to landmarks. Imaging helps in the detection of solitary versus multiglandular disease, ectopic and supernumerary glands with precise localization. US, nuclear imaging, and four-dimensional CT are the most commonly used imaging modalities for the preoperative localization of the parathyroid disease. Salivary gland tumors account for approximately 0.5% of all neoplasms, the most common location being the parotid gland (70%). Imaging is crucial in salivary gland tumors by defining its location, detecting malignant features, assessing local extension and invasion, staging the tumors according to the tumor-node-metastasis classification, and assessing the feasibility of surgery.

Endovascular Repair for Abdominal Aneurysm with Concomitant Aortoiliac Vein Fistula Diagnosed by Four-Dimensional Computed Tomography.

A 78-year-old man complaining of left leg swelling was diagnosed with an abdominal aortic aneurysm with an irregular margin. A four-dimensional computed tomography (CT) showed an aortoiliac vein fistula. An AFX stent graft was urgently implanted, and a Viabahn VBX was inserted into the left iliac vein. The aneurysmal sac was embolized. After the procedure, enhanced CT confirmed a patent stent graft without any endoleak or fistula. The patient was discharged ambulatory. An aortoiliac vein fistula is a differential diagnosis for leg edema, and a four-dimensional CT is beneficial in diagnosing the condition.

Deterministic small-scale undulations of image-based risk predictions from the deep learning of lung tumors in motion.

Deep learning (DL) models that use medical images to predict clinical outcomes are poised for clinical translation. For tumors that reside in organs that move, however, the impact of motion (i.e., degenerated object appearance or blur) on DL model accuracy remains unclear. We examine the impact of tumor motion on an image-based DL framework that predicts local failure risk after lung stereotactic body radiotherapy (SBRT). We input pre-therapy free breathing (FB) computed tomography (CT) images from 849 patients treated with lung SBRT into a multitask deep neural network to generate an image fingerprint signature (or DL score) that predicts time-to-event local failure outcomes. The network includes a convolutional neural network encoder for extracting imaging features and building a task-specific fingerprint, a decoder for estimating handcrafted radiomic features, and a task-specific network for generating image signature for radiotherapy outcome prediction. The impact of tumor motion on the DL scores was then examined for a holdout set of 468 images from 39 patients comprising: (1) FB CT, (2) four-dimensional (4D) CT, and (3) maximum-intensity projection (MIP) images. Tumor motion was estimated using a 3D vector of the maximum distance traveled, and its association with DL score variance was assessed by linear regression. The variance and amplitude in 4D CT image-derived DL scores were associated with tumor motion (R2 =0.48 and 0.46, respectively). Specifically, DL score variance was deterministic and represented by sinusoidal undulations in phase with the respiratory cycle. DL scores, but not tumor volumes, peaked near end-exhalation. The mean of the scores derived from 4D CT images and the score obtained from FB CT images were highly associated (Pearson r=0.99). MIP-derived DL scores were significantly higher than 4D- or FB-derived risk scores (p<0.0001). An image-based DL risk score derived from a series of 4D CT images varies in a deterministic, sinusoidal trajectory in a phase with the respiratory cycle. These results indicate that DL models of tumors in motion can be robust to fluctuations in object appearance due to movement and can guide standardization processes in the clinical translation of DL models for patients with lung cancer.

Effective lung ventilation estimation based on 4D CT image registration and supervoxels

Most computed tomography (CT)-derived ventilation estimation methods rely on deformation fields of image registration to track the volume variations of each voxel, which are easily affected by image registration results and motion artifacts of four-dimensional (4D) CT images. To attack these problems, a lung ventilation estimation method based on 4D CT image registration and supervoxels is proposed in this paper. In order to avoid estimation errors caused by image artifacts and noises in the methods of directly estimating voxel-wise ventilation, images corresponding to maximum exhale phase are represented by multi-level supervoxels. Then, according to the relationship between registered CT values in Hounsfield units (HU) and regional volume change, a ventilation estimation method is designed to calculate the whole ventilation of each supervoxel. To accurately recover the ventilation of each voxel from the supervoxel region, a linear programming model is established to obtain the ventilation of each voxel, and the final estimated lung ventilation image is obtained by averaging the recovered results of all supervoxels layers. To evaluate our proposed ventilation estimation method, we calculate voxel-wise Spearman coefficient rs and Dice similarity coefficient (DSC for low function lung DSClow and high function lung DSChigh) of various CT-derived ventilation methods on VAMPIRE dataset. The experimental results on VAMPIRE dataset show that the average rs values between our proposed method can achieve 0.38 (-0.17–0.70). Accordingly, the average DSClow and DSChigh values of our proposed method are 0.40 (0.17–0.67) and 0.36 (0.08–0.61) respectively.

Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans.

Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated. The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015-2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool's output. Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2Gy, range -1.6-0.3Gy) and maximum (median 0.4Gy, range -2.2-0.9Gy) doses to substructures were generally small. Nearly all structures (99.5%) were deemed to be appropriate for clinical use without further editing. Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement.

The measurements of the right ventricle (RV) and pulmonary artery (PA), for selecting the optimal prosthesis size for transcatheter pulmonary valve replacement (TPVR), vary considerably. Three-dimensional (3D) computed tomography (CT) imaging for device size prediction is insufficient to assess the displacement of the right ventricular outflow tract (RVOT) and PA, which could increase the risk of stent misplacement and paravalvular leak. The aim of this study is to provide a dynamic model to visualize and quantify the anatomy of the RVOT to PA over the entire cardiac cycle by four-dimensional (4D) cardiac CT reconstruction to obtain an accurate quantitative evaluation of the required valve size. In this pilot study, cardiac CT from sheep J was chosen to illustrate the procedures. 3D cardiac CT was imported into 3D reconstruction software to build a 4D sequence which was divided into eleven frames over the cardiac cycle to visualize the deformation of the heart. Diameter, cross-sectional area, and circumference of five imaging planes at the main PA, sinotubular junction, sinus, basal plane of the pulmonary valve (BPV), and RVOT were measured at each frame in 4D straightened models prior to valve implantation to predict the valve size. Meanwhile, dynamic changes in the RV volume were also measured to evaluate right ventricular ejection fraction (RVEF). 3D measurements at the end of the diastole were obtained for comparison with the 4D measurements. In sheep J, 4D CT measurements from the straightened model resulted in the same choice of valve size for TPVR (30 mm) as 3D measurements. The RVEF of sheep J from pre-CT was 62.1 %. In contrast with 3D CT, the straightened 4D reconstruction model not only enabled accurate prediction for valve size selection for TPVR but also provided an ideal virtual reality, thus presenting a promising method for TPVR and the innovation of TPVR devices.

Quantitative assessment of intra- and inter-modality deformable image registration of the heart, left ventricle, and thoracic aorta on longitudinal 4D-CT and MR images.

PurposeMagnetic resonance imaging (MRI)‐based investigations into radiotherapy (RT)‐induced cardiotoxicity require reliable registrations of magnetic resonance (MR) imaging to planning computed tomography (CT) for correlation to regional dose. In this study, the accuracy of intra‐ and inter‐modality deformable image registration (DIR) of longitudinal four‐dimensional CT (4D‐CT) and MR images were evaluated for heart, left ventricle (LV), and thoracic aorta (TA).Methods and materialsNon‐cardiac‐gated 4D‐CT and T1 volumetric interpolated breath‐hold examination (T1‐VIBE) MRI datasets from five lung cancer patients were obtained at two breathing phases (inspiration/expiration) and two time points (before treatment and 5 weeks after initiating RT). Heart, LV, and TA were manually contoured. Each organ underwent three intramodal DIRs ((A) CT modality over time, (B) MR modality over time, and (C) MR contrast effect at the same time) and two intermodal DIRs ((D) CT/MR multimodality at same time and (E) CT/MR multimodality over time). Hausdorff distance (HD), mean distance to agreement (MDA), and Dice were evaluated and assessed for compliance with American Association of Physicists in Medicine (AAPM) Task Group (TG)‐132 recommendations.ResultsMean values of HD, MDA, and Dice under all registration scenarios for each region of interest ranged between 8.7 and 16.8 mm, 1.0 and 2.6 mm, and 0.85 and 0.95, respectively, and were within the TG‐132 recommended range (MDA < 3 mm, Dice > 0.8). Intramodal DIR showed slightly better results compared to intermodal DIR. Heart and TA demonstrated higher registration accuracy compared to LV for all scenarios except for HD and Dice values in Group A. Significant differences for each metric and tissue of interest were noted between Groups B and D and between Groups B and E. MDA and Dice significantly differed between LV and heart in all registrations except for MDA in Group E.ConclusionsDIR of the heart, LV, and TA between non‐cardiac‐gated longitudinal 4D‐CT and MRI across two modalities, breathing phases, and pre/post‐contrast is acceptably accurate per AAPM TG‐132 guidelines. This study paves the way for future evaluation of RT‐induced cardiotoxicity and its related factors using multimodality DIR.

Tailored stereotactic radiotherapy technique using deep inspiration breath-hold to reduce stomach dose for cardiac radioablation.

PurposeTo provide a new insight on a novel safe cardiac radioablation using deep inspiration breath-hold (DIBH) to reduce gastrointestinal dose.Materials and MethodsFor treating incessant ventricular tachycardia (VT) originated from left ventricle inferior scar abutting the stomach, a target delineation and treatment planning for cardiac radioablation was performed. With four different computed tomography (CT) scan protocols—DIBH, full expiration breath-hold, four-dimensional (4D) CT without and with abdominal compression, the distances between the target and the stomach were compared.ResultsAmong the protocols, the CT scan with DIBH showed largest distance between the target and the stomach and selected for the treatment planning. The prescribed dose was 25 Gy in a single fraction, and satisfactory dosimetric parameters were achieved with the DIBH. The patient was successfully treated with the DIBH, and experienced no acute toxicity. ConclusionTo gain the best benefit from cardiac radioablation, understanding the possible toxicity in the adjacent organs is crucial. By moving the heart with thoraco-diaphragmatic movement by DIBH, the target could be physically separated from the stomach.

Four-dimensional dose calculations for dynamic tumour tracking with a gimbal-mounted linear accelerator.

PurposeIn this study we present a novel method for re‐calculating a treatment plan on different respiratory phases by accurately modeling the panning and tilting beam motion during DTT (the “rotation method”). This method is used to re‐calculate the dose distribution of a plan on multiple breathing phases to accurately assess the dosimetry.MethodssIMRT plans were optimized on a breath hold computed tomography (CT) image taken at exhale (BHexhale) for 10 previous liver stereotactic ablative radiotherapy patients. Our method was used to re‐calculate the plan on the inhale (0%) and exhale (50%) phases of the four‐dimensional CT (4DCT) image set. The dose distributions were deformed to the BHexhale CT and summed together with proper weighting calculated from the patient’s breathing trace. Subsequently, the plan was re‐calculated on all ten phases using our method and the dose distributions were deformed to the BHexhale CT and accumulated together. The maximum dose for certain organs at risk (OARs) was compared between calculating on two phases and all ten phases.ResultsIn total, 26 OARs were examined from 10 patients. When the dose was calculated on the inhale and exhale phases six OARs exceeded their dose limit, and when all 10 phases were used five OARs exceeded their limit.ConclusionDynamic tumor tracking plans optimized for a single respiratory phase leave an OAR vulnerable to exceeding its dose constraint during other respiratory phases. The rotation method accurately models the beam’s geometry. Using deformable image registration to accumulate dose from all 10 breathing phases provides the most accurate results, however it is a time consuming procedure. Accumulating the dose from two extreme breathing phases (exhale and inhale) and weighting them properly provides accurate results while requiring less time. This approach should be used to confirm the safety of a DTT treatment plan prior to delivery.

Evaluation of Velopharyngeal Closure Function With 4-Dimensional Computed Tomography and Assessment of Radiation Exposure in Pediatric Patients: A Cross-Sectional Study

Some patients with cleft palate (CP) need secondary surgery to improve functionality. Although 4-dimensional assessment of velopharyngeal closure function (VPF) in patients with CP using computed tomography (CT) has been existed, the knowledge about quantitative evaluation and radiation exposure dose is limited. We performed a qualitative and quantitative assessment of VPF using CT and estimated the exposure doses. Cross-sectional. Computed tomography images from 5 preoperative patients with submucous CP (SMCP) and 10 postoperative patients with a history of CP (8 boys and 7 girls, aged 4-7 years) were evaluated. Five patients had undergone primary surgery for SMCP; 10 received secondary surgery for hypernasality. The presence of velopharyngeal insufficiency (VPI), patterns of velopharyngeal closure (VPC), and cross-sectional area (CSA) of VPI was evaluated via CT findings. Organ-absorbed radiation doses were estimated in 5 of 15 patients. The differences between cleft type and VPI, VPC patterns, and CSA of VPI were evaluated. All patients had VPI. The VPC patterns (SMCP/CP) were evaluated as coronal (1/4), sagittal (0/1), circular (1/2), and circular with Passavant's ridge (2/2); 2 patients (1/1) were unevaluable because of poor VPF. The CSA of VPI was statistically larger in the SMCP group (P = .0027). The organ-absorbed radiation doses were relatively lower than those previously reported. Four-dimensional CT can provide the detailed findings of VPF that are not possible with conventional CT, and the exposure dose was considered medically acceptable.

Clinical predictors and sequelae of computed tomography defined leaflet thrombosis following transcatheter aortic valve replacement at medium-term follow-up.

The clinical predictors and sequelae of leaflet thrombosis (LT) following transcatheter aortic valve replacement (TAVR) is still unclear. Therefore, our aim was to determine the clinical predictors and sequelae at mid-term follow-up of computed tomography (CT)-defined LT following TAVR. We performed a prospective evaluation with a 320-multislice CT following TAVR for the presence of LT, defined as hypo-attenuated leaflet thickening (HALT). Four-dimensional CT image-rendering was performed to determine the presence of reduced leaflet motion (RELM). 172 patients [89 (51.7%) male, mean age 82.8 ± 5.7years] treated with commercially available TAVR device (Lotus 54%, CoreValve 32% and Sapien 3 14%) were included, with median CT-scan at 6.0weeks post-TAVR. Prevalence of HALT was 14.0% (24 cases) and RELM was 9.8% (17 cases). On multivariate analysis, patients with HALT were less prescribed oral anticoagulation (OAC) (OR 9.9), received larger TAVR prostheses (OR 5.7) and higher rates of moderate-severe para-valvular regurgitation (PVR) (OR 16.3). There was no difference in clinical outcomes at a median follow-up of 2.3years. Patients with RELM had significantly higher transvalvular gradients after discharge when compared to those without RELM. Absence of OAC, large TAVR prostheses and moderate-severe PVR were predictors for LT. Transvalvular gradients were higher in patients that developed RELM but not HALT. Further studies are warranted to determine the long-term impact of LT on TAVR durability. Prevalence of different sub-types of CT-defined LT (HALT and RELM) and the clinical predictors of developing LT following TAVR. CT computed tomography, HALT hypo-attenuated leaflet thickening, LT leaflet thrombosis, RELM reduced leaflet motion, TAVR transcatheter aortic valve replacement.

Estimation of liver elasticity using the finite element method and four-dimensional computed tomography images as a biomarker of liver fibrosis.

Current radiotherapy planning procedures are generally designed based on anatomical information only and use computed tomography (CT) images that do not incorporate organ-functional information. In this study, we developed a method for estimating liver elasticity using the finite element method (FEM) and four-dimensional CT (4DCT) images acquired during radiotherapy planning, and we subsequently evaluated its feasibility as a biomarker for liver fibrosis. Twenty patients who underwent 4DCT and ultrasound-based transient elastography (UTE) were enrolled. All patients had chronic liver disease or cirrhosis. Liver elasticity measurements of the UTE were performed on the right lobe of the patient's liver in 20 patients. The serum biomarkers of the aspartate aminotransferase (AST)-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) were available in 18 of the 20 total patients, which were measured within 1 week after undergoing 4DCT. The displacement between the 4DCT images obtained at the endpoints of exhalation and inspiration was determined using the actual (via deformable image registration) and simulated (via FEM) respiration-induced displacement. The elasticity of each element of the liver model was optimized by minimizing the error between the actual and simulated respiration-induced displacement. Then, each patient's estimated liver elasticity was defined as the mean Young's modulus of the liver's right lobe and that of the whole liver using the estimated elasticity map. The estimated liver elasticity was evaluated for correlations with the elasticity obtained via UTE and with two serum biomarkers (APRI and FIB-4). The mean±standard deviation (SD) of the errors between the actual and simulated respiration-induced displacement in the liver model was 0.54±0.33mm. The estimated liver's right lobe elasticity was statistically significantly correlated with the UTE (r=0.87, P<0.001). Furthermore, the estimated whole liver elasticity was statistically significantly correlated with the UTE (r=0.84, P<0.001), APRI score (r=0.62, P=0.005), and FIB-4 score (r=0.54, P=0.021). In this study, liver elasticity was estimated through FEM-based simulation and actual respiratory-induced liver displacement obtained from 4DCT images. Furthermore, we assessed that the estimated elasticity of the liver's right lobe was strongly correlated with the UTE. Therefore, the estimated elasticity has the potential to be a feasible imaging biomarker for assessing liver fibrosis using only 4DCT images without additional inspection or equipment costs. Because our results were derived from a limited sample of 20 patients, it is necessary to evaluate the accuracy of elasticity estimation for each liver segment on larger groups of biopsied patients to utilize liver elasticity information for radiotherapy planning.

Residual Set Up Errors of the Surrogate-guided Registration Using Four-dimensional CT Images and Breath Holding Ones in Respiratory Gated Radiotherapy for Liver Cancer.

To evaluate the surrogate-guided registration accuracy of two computed tomography (CT) image sets, expiratory phase four-dimensional (Ex4D) CT and breath-holding CT (BHCT), in respiratory-gated radiotherapy for liver cancer. The surrogate-guided registration errors were defined as the differences between the diaphragm- and fiducial-guided registrations or the differences between upper and lower fiducial registrations in three directions: left-right (LR), anterior-posterior (AP), and cranio-caudal (CC). The mean±SDs of the absolute errors for diaphragm-guided registration were 1.9±1.3, 2.7±1.8, and 2.6±1.7 mm with Ex4D and 1.8±1.8, 2.6±1.9, and 1.8±1.7 mm with BHCT in the LR, AP and CC directions, respectively (CC direction, p<0.01). In the fiducial-guided registration, there were no significant differences in any direction. In registration with Ex4D, there were positive correlations between registration errors and the respiratory irregularity during 4D scanning (correlation coefficient; diaphragm: 0.65, fiducial: 0.54). BHCT has the advantage of accurate surrogate-guided registration compared with Ex4D.

CT images with expert manual contours of thoracic cancer for benchmarking auto‐segmentation accuracy

Purpose Automatic segmentation offers many benefits for radiotherapy treatment planning; however, the lack of publicly available benchmark datasets limits the clinical use of automatic segmentation. In this work, we present a well-curated computed tomography (CT) dataset of high-quality manually drawn contours from patients with thoracic cancer that can be used to evaluate the accuracy of thoracic normal tissue auto-segmentation systems. Acquisition and validation methods Computed tomography scans of 60 patients undergoing treatment simulation for thoracic radiotherapy were acquired from three institutions: MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, and the MAASTRO clinic. Each institution provided CT scans from 20 patients, including mean intensity projection four-dimensional CT (4D CT), exhale phase (4D CT), or free-breathing CT scans depending on their clinical practice. All CT scans covered the entire thoracic region with a 50-cm field of view and slice spacing of 1, 2.5, or 3 mm. Manual contours of left/right lungs, esophagus, heart, and spinal cord were retrieved from the clinical treatment plans. These contours were checked for quality and edited if necessary to ensure adherence to RTOG 1106 contouring guidelines. Data format and usage notes The CT images and RTSTRUCT files are available in DICOM format. The regions of interest were named according to the nomenclature recommended by American Association of Physicists in Medicine Task Group 263 as Lung_L, Lung_R, Esophagus, Heart, and SpinalCord. This dataset is available on The Cancer Imaging Archive (funded by the National Cancer Institute) under Lung CT Segmentation Challenge 2017 (http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08). Potential applications This dataset provides CT scans with well-delineated manually drawn contours from patients with thoracic cancer that can be used to evaluate auto-segmentation systems. Additional anatomies could be supplied in the future to enhance the existing library of contours.