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To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm

Introduction: Numerous classifications have been developed to assess tibial plateau fractures (TPF). Of these, the Schatzker system is the most widely reported in the literature yet this system is limited in its characterisation of morphological fracture features underlying the fracture location. The purpose of this study was to compare 3D morphological features of TPFs across different Schatzker types. Methods: This study retrospectively analysed preoperative TPF imaging data to reconstruct 3D models of the fractures. Ninety-one fractures (29 female, 62 male) were analysed and classified using Schatzker. Fracture location across Schatzker types was compared based on division of the articular surface into six ‘zones’. Additionally, morphological characteristics of the fractures were compared based on fracture type, including; the number, volume and shape of the fragments. Results: Schatzker II, IV and VI fractures were most common, making up 41%, 16% and 20%, respectively. Type II fractures commonly involved both the lateral and central aspect of the tibial plateau, similarly, type IV fractures incorporated the lateral condyle in most cases. Considering the morphological metrics, statistically significant differences were observed between Schatzker types for the number of; total, articular, cortical and volumetrically significant (all P < 0.001) fragments along with the volume of both primary (P < 0.001) and secondary (P = 0.02) fragments. Discussion: Assessment of underlying fracture characteristics in addition to fracture location can serve to provide greater detail relating to fracture morphology, which has the potential to assist with both surgical decision making and assessment of postoperative outcomes. Incorporating this information as part of a hierarchical or multifactorial framework for classifying fractures may help distinguish subtle differences between fracture types that are classifiable using the most current systems. Refereed/Peer-reviewed

Background To surgically remove osteoma and to keep an optimal cosmetic profile would be very challenging. To solve the difficulty, we utilized the three-dimensional (3D) printing technologies in generating a patient-specific instrument guide (PSIG) for the safe removal of a skull bone tumor. Methods The preoperational brain computed tomography (CT) provided the digital imaging with thin slices, and then images were reconstructed into a 3D skull model. Based on the model, we designed a PSIG to make landmarks on the osteoma to avoid excessive removal of the skull bone. During the operation, the surgeons could remove the osteoma piece by piece by using the landmark as a reference point. Results The PSIG was successfully applied to remove an osteoma that measured 60 × 48 × 40 mm over the left frontoparietal skull of a female patient. The 3D CT reconstruction taken both before and 4 months after surgery showed a significant change in the appearance of the osteoma. Conclusion The PSIG was able to guide the surgeon in the safe removal of the skull osteoma, as well as in maintaining the cosmetic skull profile.

We investigate the suitability of statistical and model-based iterative reconstruction (IR) algorithm strengths and their influence on image quality and diagnostic performance in low-dose computer tomography (CT) protocols for lung-cancer screening procedures. We evaluate the inter- and intra-observer performance for the assessment of iterative CT reconstruction. Artificial lung foci shaped as spheres and spicules made from material with calibrated Hounsfield units were pressed within layered granules in lung lobes of an anthropomorphic phantom. Adaptively, a soft-tissue- and fat- extension ring were attached. The phantom with foci was scanned using standard high contrast, low-dose and ultra lowdose protocols. For reconstruction the IR algorithm ADMIRE at four different strength levels were used. Two ranking tests and Friedman statistics were performed. Fleiss k and modified Cohen’s kneywere used to quantify inter- and intra-observer performance. In conjunction with the standard lung kernel BL75 radiologists evaluated medium to high IR strength, with preference to S4, as suitable for lung foci detection. When varying reconstruction kernels the ranking became more random than with varying phantom diameter. The inter-observer reliability shows poor to slight agreement expressed by kney=0-0.20and moderate agreement with kney=0.60-0.79 for the first ranking test, and almost perfect agreement with kney>0.90 for the second ranking test was observed. In conclusion, our validation suggests radiological preference of medium to high iteration strengths, especially S4, for lung foci detection. An investigation of the correlation between diagnostic experience and the subjective perception of IR reconstructed CT images still needs to be investigated.

BACKGROUND: The anatomy of the posterior wall of the acetabulum is important for the hip stability. We wanted to know whether differences can be observed. MATERIAL AND METHODS: On lateral 3D pelvic CT reconstruction of the pelvis two types were identified. On axial CT-images acetabular angles were determined. RESULTS: We observed 35 type I acetabular and 61 type II acetabular. Posterior acetabular sector angle was 114° in type I and 94° in type II acetabular (p

The projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the ℓ 1 / 2 {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the ℓ 1 / 2 {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.

The total variation (TV) regularized reconstruction methods for computed tomography (CT) may lead to staircase effects in the reconstructed images because of using the TV regularization. This paper develops a total fractional-order variation regularized CT reconstruction method, aiming at overcoming the weakness of the reconstruction methods based on the TV. Specifically, we propose an optimization model for CT reconstruction, including a fidelity term, a regularization term, and a constraint term. Here, the regularization is a total fractional-order variation arising from the fractional derivative of the underlying solution. To address the nondifferentiability of the resulting model, we introduce a fixed-point characterization for its solution through the proximity operators of the nondifferentiable functions. Based on the characterization, we further develop a fixed-point iterative scheme to solve the resulting model and provide convergence analysis of the developed scheme. Numerical experiments are presented to demonstrate that the developed method outperforms the TV regularized reconstruction method in terms of suppressing noise for CT reconstruction.

X-ray computed tomography has been a useful technology in cancer detection and radiation therapy. However, high radiation dose during CT scans may increase the underlying risk of healthy organs. Usually, sparse-view X-ray projection is an effective method to reduce radiation. In this paper, we propose a constrained nonconvex truncated regularization model for this low-dose CT reconstruction. It preserves sharp edges very well. Although this model is quite complicated to analyze, we establish two useful theoretical results for its minimizers. Motivated by them, an iterative support shrinking algorithm is introduced. To handle more nondifferentiable points of the regularization function except zero point, we use a general proximally linearization technique at them, which is helpful to implement our algorithm. For this algorithm, we prove the convergence of the objective function, and give a lower bound theory of the iterative sequence. Numerical experiments and comparisons demonstrate that our model with the proposed algorithm performs good for low-dose CT reconstruction.

This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel, we propose to fuse this available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise-ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyze the use of directional total variation within variational regularization and iterative regularization. Our numerical results on simulated and experimental data show improvements in terms of image quality and in computational speed., Comment: 17 pages, 17 figures

We present a patient with the initial misdiagnosis of a metallic foreign body in the abdominal aorta. Computed tomographic angiography utilizing microanatomical reconstruction technique revealed the structure to instead be a partially calcified abdominal aortic congenital fibrous band. Most congenital fibrous bands spanning the aortic lumen are proximal within the aorta and are thought to be supportive structures of the aortic valve leaflets. Congenital fibrous bands distal to the sinotubular junction are quite rare. Keywords: Aorta, Band, CT angiography, Intravascular foreign body, Postprocessing, Reconstruction

Objective: To assess the quality of applied pelvic binders using three dimensional computer tomography (3D CT). Methods: A local trauma registry was used to identify patients with pelvic fractures after high-energy trauma during 2011-2015. A 3D CT reconstruction was made from the initial trauma computer tomography images to assess the level of application, symmetricity of the binder and achieved fracture reduction. An acceptable application of the pelvic binder was deemed if it was at the trochanteric level, symmetric and minimized residual displacement. Results: We found 73 patients with a pelvic fracture and a pelvic binder on the initial trauma CT-scan. The mean (±SD) age of the patients was 46±17 years and 40% (n=29) were females. The median ISS score was 38 (IQR;29-50), the mean systolic blood pressure on arrival was 106±46 mmHg and the median GCS on arrival was 14 (IQR;7-15). We found that 59% (n=43) of the binders were correctly applied (symmetric at the trochanteric level, symmetrical and with acceptable residual displacement of the fracture). The 30-day mortality was higher in patients with non-correct application 17% (n=5/30) compared to patients with correct application of the pelvic binder 9.3% (n=4/43) however this was not statistically significant ( p =0.562). Conclusion: A substantial number of patients had non-correct application of pelvic binders. Future studies using 3D technique are encouraged to further investigate clinical impacts of non-appropriate application of pelvic binders.

BACKGROUND The lesser trochanter (LT) posterior cortical extension (LTPE) fragment is important for joint stability during reconstruction in unstable pertrochanteric hip fractures. This study aimed to investigate the morphological characteristics of the LT fragments using three-dimensional computed tomography (3-D CT) reconstruction and to compare clinical outcomes in different subgroups of 31A2 fractures. MATERIAL AND METHODS A retrospective study included CT images of 58 cases of 31A2 type unstable pertrochanteric fractures, using the Arbeitsgemeinschaft für Osteosynthesefragen and Orthopedic Trauma Association (AO/OTA) classification. After 3-D CT reconstruction, all the displaced fragments were reduced and the morphologic parameters of the LT fragments were measured. RESULTS At the mid-level of the LT, the mean cortical extension of the LT fragment was 33.5 mm in the posterior wall (83%), 19.0 mm in the medial wall (53%). The mean distal cortical extension from the lower edge of the LT was 13.0 mm. The LT fragment occupied 38% of the whole cortical circumference. Comparison of the subgroups showed that the LT fragment of A2.3 had a larger cortical extension in the posterior, medial, and distal cortex. The mean fracture collapse was 4.7 mm in A2.2 fractures and 5.4 mm in A2.3 fractures (p=0.311). Despite the increased size of the LT fragment in subtype A2.3 fracture compared with subtype A2.2 fracture, this did not influence the clinical outcome. CONCLUSIONS The 3-D morphology of the LT fragment from CT imaging provided a better understanding of the characteristics of subgroups of unstable pertrochanteric fractures, which may improve and guide implant choice.

Purpose Wide bore CT scanners use extended field-of-view (eFOV) reconstruction algorithms to attempt to recreate tissue truncated due to large patient habitus. Radiation therapy planning systems rely on accurate CT numbers in order to correctly plan and calculate radiation dose. This study looks at the impact of eFOV reconstructions on CT numbers and radiation dose calculations in real patient geometries. Methods A large modular phantom based on real patient geometries was created to surround a CIRS Model 062M phantom. The modular sections included a smooth patient surface, a skin fold in the patient surface, and the addition of arms for simulation of the patient in arms up or arms down position. This phantom was used to evaluate the accuracy of CT numbers for three extended FOV algorithms implemented on Siemens CT scanners: eFOV, HDFOV, and HDProFOV. Six different configurations of the phantoms were scanned and images were reconstructed for the three different extended FOV algorithms. The CIRS phantom inserts and overall phantom geometry were contoured in each image, and the Hounsfield units (HU) numbers were compared to an image of the phantom within the standard scan FOV (sFOV) without the modular sections. To evaluate the effect on dose calculations, six radiotherapy patients previously treated at our institution (three head and neck and three chest/pelvis) whose body circumferences extended past the 50 cm sFOV in the treatment planning CT were used. Images acquired on a Siemens Sensation Open scanner were reconstructed using sFOV, eFOV and HDFOV algorithms. A physician and dosimetrist identified the radiation target, critical organs, and external patient contour. A benchmark CT was created for each patient, consisting of an average of the 3 CT reconstructions with a density override applied to regions containing truncation artifacts. The benchmark CT was used to create an optimal radiation treatment plan. The plan was copied onto each CT reconstruction without density override and dose was recalculated. Results Tissue extending past the sFOV impacts the HU numbers for tissues inside and outside the sFOV when using extended FOV reconstructions. On average, the HU for all CIRS density inserts in the arms up (arms down) position varied by 43 HU (67 HU), 39 HU (73 HU), and 18 HU (51 HU) for the eFOV, HDFOV, and HDProFOV scans, respectively. In the patient dose calculations, patients with a smooth patient contour had the least deviation from the benchmark in the HDFOV (0.1-0.5%) compared to eFOV (0.4-1.8%) reconstructions. In cases with large amounts of tissue and irregular skin folds, the eFOV deviated the least from the benchmark (range 0.2-0.6% dose difference) compared to HDFOV (range 1.3-1.8% dose difference). Conclusions All reconstruction algorithms demonstrated good CT number accuracy in the center of the image. Larger artifacts are seen near and extending outside the scan FOV, however, dose calculations performed using typical beam arrangements using the extended FOV reconstructions were still mostly within 2.5% of best estimated reference values.

Background Bone SPECT/CT has been shown to offer superior sensitivity and specificity compared to conventional whole-body planar scanning. Furthermore, bone SPECT/CT allows quantitative imaging, which is challenging with planar methods. In order to gain better quantitative accuracy, Bayesian reconstruction algorithms, including both image derived and anatomically guided priors, have been utilized in reconstruction in PET/CT scanning, but they have not been widely used in SPECT/CT studies. Therefore, the aim of this work was to evaluate the performance of CT-guided reconstruction in quantitative bone SPECT. Methods Three Bayesian reconstruction methods were evaluated against the conventional ordered subsets expectation maximization (OSEM) reconstruction method. One of the studied Bayesian methods was the relative difference prior (RDP), which has recently gained popularity in PET reconstruction. The other two methods, anatomically guided smoothing prior (AMAP-S) and anatomically guided relative difference prior (AMAP-R), utilized anatomical information from the CT scan. The reconstruction methods were evaluated in terms of quantitative accuracy with artificial lesions inserted in clinical patient studies and with 20 real clinical patients. Maximum and mean standardized uptake values (SUVs) of the lesions were defined. Results The analyses showed that all studied Bayesian methods performed better than OSEM and the anatomical priors also outperformed RDP. The average relative error in mean SUV for the artificial lesion study for OSEM, RDP, AMAP-S, and AMAP-R was − 53%, − 35%, − 15%, and − 10%, when the CT study had matching lesions. In the patient study, the RDP method gave 16 ± 9% higher maximum SUV values than OSEM, while AMAP-S and AMAP-R offered increases of 36 ± 8% and 36 ± 9%, respectively. Mean SUV increased for RDP, AMAP-S, and AMAP-R by 18 ± 9%, 26 ± 5%, and 33 ± 5% when compared to OSEM. Conclusions The Bayesian methods with anatomical prior, especially the relative difference prior-based method (AMAP-R), outperformed OSEM and reconstruction without anatomical prior in terms of quantitative accuracy.

Current classifications of midshaft clavicle fractures are based on radiography. The aim of the study was to evaluate the fracture pattern of clavicle fractures using 3-dimensional computed tomography (3D CT). A retrospective analysis was performed on CT scans of 65 acute clavicle fractures. Using quantitative 3D CT reconstruction techniques, the fracture of the clavicle was virtually reduced. Based on these reconstructions, a group-based fracture heat map and small fragment heat map, and the location of the most common fracture line were determined. Also, the direction and amount of displacement were evaluated. Three fracture patterns could be distinguished. The primary fracture line in type 1 is going from posteromedial to anterolateral and located between 50% and 68% of the clavicle’s length. In type 2, a transverse fracture line is located around 55%, and in type 3, a superolateral to inferomedial line is located between 47% and 56%. Wedged fracture fragments can be seen in types 1 and 2 and are mainly situated inferiorly. The displacement is similar in all types, but the main direction of displacement is specific for the different types (posterior, anterior, inferior). We can conclude that several fracture patterns can be seen in clavicle fractures. Most fractures are located laterally at the midshaft of the clavicle. Wedged segments are mainly located inferiorly, and at the posterior part of the clavicle, no comminution is ever seen. The direction of displacement depends on the fracture pattern.

Objective. Owing to the superior soft tissue contrast of MRI, MRI-guided adaptive radiotherapy (ART) is well-suited to managing interfractional changes in anatomy. An MRI-only workflow is desirable, but producing synthetic CT (sCT) data through paired data-driven deep learning (DL) for abdominal dose calculations remains a challenge due to the highly variable presence of intestinal gas. We present the preliminary dosimetric evaluation of our novel approach to sCT reconstruction that is well suited to handling intestinal gas in abdominal MRI-only ART.Approach. We utilize a paired data DL approach enabled by the intensity projection prior, in which well-matching training pairs are created by propagating air from MRI to corresponding CT scans. Evaluations focus on two classes: patients with (1) little involvement of intestinal gas, and (2) notable differences in intestinal gas presence between corresponding scans. Comparisons between sCT-based plans and CT-based clinical plans for both classes are made at the first treatment fraction to highlight the dosimetric impact of the variable presence of intestinal gas.Main results. Class 1 patients (n= 13) demonstrate differences in prescribed dose coverage of the PTV of 1.3 ± 2.1% between clinical plans and sCT-based plans. Mean DVH differences in all structures for Class 1 patients are found to be statistically insignificant. In Class 2 (n= 20), target coverage is 13.3 ± 11.0% higher in the clinical plans and mean DVH differences are found to be statistically significant.Significance. Significant deviations in calculated doses arising from the variable presence of intestinal gas in corresponding CT and MRI scans result in uncertainty in high-dose regions that may limit the effectiveness of adaptive dose escalation efforts. We have proposed a paired data-driven DL approach to sCT reconstruction for accurate dose calculations in abdominal ART enabled by the creation of a clinically unavailable training data set with well-matching representations of intestinal gas.

Background Reliable size measurement of lymph node (LN) metastases is important for the evaluation of cancer treatment. However, image analyses without proper settings may result in inappropriate diagnoses and staging. Purpose To investigate whether reconstruction slice thickness in computed tomography (CT) affects measurements of LN size and reproducibility. Material and Methods We analyzed 48 patients with histological diagnoses of sigmoid colon and rectal cancer who underwent contrast-enhanced CT colonography as part of a surgical treatment preparation. A board-certified radiologist selected 106 LNs whose short-axis diameter was ≥5 mm on 1-mm-thick images; the short-axis diameters were measured on 1- and 5-mm-thick images by the radiologist and residents and compared using Wilcoxon matched-pairs signed rank test. Data variation and reproducibility were evaluated using the F test and Bland–Altman analysis. PResults Short-axis diameters measured on 5-mm-thick images were significantly lower than those measured on 1-mm-thick images ( PConclusion Thick reconstruction slices in CT can result in an underestimation of LN size and reduce data reproducibility. When measuring LN size, a thin reconstruction slice would be recommended based on targeted LN size.

The purpose was to evaluate the spatial resolution in 111In-octreotide single-photom emission computed tomography (SPECT)/computed tomography (CT) imaging following reconstructions with three different ordered subset expectation maximizations (OSEM) reconstruction algorithms; attenuation corrected (AC) OSEM, AC OSEM with resolution recovery (ACRR OSEM) and Monte Carlo-based OSEM reconstruction (MC OSEM). SPECT/CT imaging of a triple line phantom containing 111In in air and water was performed. The spatial resolution, represented by the full width at half maximum (FWHM) of a line profile, was determined for each line, for X and Y direction and for all reconstructions. The mean FWHM was 12.2 mm (±standard deviation [SD] 3.7 mm) for AC OSEM, 9.3 mm (±SD 2.5 mm) for ACRR OSEM and 8.2 mm (±SD 2.0 mm) for MC OSEM. MC-based SPECT/CT reconstruction clearly improves the spatial resolution in 111In-octreotide imaging and since MC simulations can be performed for all photon energies MC OSEM has the potential to improve SPECT/CT imaging overall.

To date, there is no consensus on when and how to perform acromioplasty during rotator cuff repair (RCR). We aimed to determine the volume of impinging bone removed during acromioplasty and whether it influences postoperative range of motion (ROM) and clinical scores after RCR. Preoperative and postoperative computed tomography scans of 57 shoulders that underwent RCR were used to reconstruct scapula models to simulate volumes of impinging acromial bone preoperatively and then compare them to the volumes of bone resected postoperatively to calculate the proportions of desired (ideal) vs. unnecessary (excess) resections. All patients were evaluated preoperatively and at 6 months to assess ROM and functional scores. The volume of impinging bone identified was 3.5 ± 2.3 cm 3 , of which 1.6 ± 1.2 cm 3 (50% ± 27%) was removed during acromioplasty. The volume of impinging bone identified was not correlated with preoperative critical shoulder angle (r = 0.025, P = .853), nor with glenoid inclination (r = -0.024, P = .857). The volume of bone removed was 3.7 ± 2.2 cm 3 , of which 2.1 ± 1.6 cm 3 (53% ± 24%) were unnecessary resections. Multivariable analyses revealed that more extensive removal of impinging bone significantly improved internal rotation with the arm at 90° of abduction (beta, 27.5, P = .048) but did not affect other shoulder movements or clinical scores. Acromioplasty removed only 50% of the estimated volume of impinging acromial bone. More extensive removal of impinging bone significantly improved internal rotation with the arm at 90° of abduction.

In this paper, we consider minimizing the L1/L2 term on the gradient for a limited-angle scanning problem in computed tomography (CT) reconstruction. We design a specific splitting framework for an unconstrained optimization model so that the alternating direction method of multipliers (ADMM) has guaranteed convergence under certain conditions. In addition, we incorporate a box constraint that is reasonable for imaging applications, and the convergence for the additional box constraint can also be established. Numerical results on both synthetic and experimental datasets demonstrate the effectiveness and efficiency of our proposed approaches, showing significant improvements over the state-of-the-art methods in the limited-angle CT reconstruction., 29 pages

Compressed sensing (CS) reconstruction methods employing sparsity regularization and prior constraints are successfully applied in sparse-view computed tomography (CT) reconstruction and yield high-quality images compared with other low-dose imaging methods. In this paper, we proposed an adaptive prior patch size (APPS) strategy in sparse-view CT reconstruction. The method adopts sparse representation (SR) using adaptive patch size instead of a constant one to synthesize prior image of higher quality, because the optimal patch size should vary from each distribution range of local feature. The simulation experiments show that the proposed strategy has the better performance than the method with fixed patch size in terms of artifact reduction and edge preservation.

A thorax phantom was used to assess radiotherapy dose deviations induced by respiratory motion of the target volume. Both intensity modulated and static, non‐modulated treatment plans were planned on CT scans of the phantom. The plans were optimized using various CT reconstructions, to investigate whether they had an impact on robustness to target motion during delivery. During irradiation, the target was programmed to simulate respiration‐induced motion of a lung tumor, using both patient‐specific and sinusoidal motion patterns in three dimensions. Dose was measured in the center of the target using an ion chamber. Differences between reference measurements with a stationary target and dynamic measurements were assessed. Possible correlations between plan complexity metrics and measured dose deviations were investigated. The maximum observed motion‐induced dose differences were 7.8% and 4.5% for single 2 Gy and 15 Gy fractions, respectively. The measurements performed with the largest target motion amplitude in the superior–inferior direction yielded the largest dosimetric deviations. For 2 Gy fractionation schemes, the summed dose deviation after 33 fractions is likely to be less than 2%. Measured motion‐induced dose deviations were significantly larger for one CT reconstruction compared to all the others. Static, non‐modulated plans showed superior robustness to target motion during delivery. Moderate correlations between the modulation complexity score applied to VMAT (MCSv) and measured dose deviations were found for 15 Gy SBRT treatment plans. Correlations between other plan complexity metrics and measured dose deviations were not found.

Orthogonal 2D cervical vertebra (C-vertebra) X-ray images have the advantages of high imaging efficiency, low radiation risk, easy operation and low cost for rapid primary clinical diagnoses. Especially in emergency departments, this technique is known to be significantly useful in triage for trauma patients. However, the technique can only provide overlapping anatomic information from limited projection views and is unable to visually exhibit full-view anatomy and precise stereo structures without further CT examination. To promote “once is enough” for visualizing 3D anatomy & structures and reducing repetitive radiation as much as possible, we proposed X-CTRSNet for 2D X-ray images. This is the first powerful work that simultaneously and accurately enables 3D C-vertebra CT reconstruction and segmentation directly from orthogonally anteroposterior- and lateral- view 2D X-ray images. X-CTRSNet combines the reciprocally coupled SpaDRNet for reconstruction & MulSISNet for segmentation, and a RSC Learning for tasks consistency. The experiment shows that X-CTRSNet successfully reconstructs and segments the 3D C-vertebra CT from the 2D X-ray images with a PSNR of 24.58 dB, an SSIM of 0.749, and an average Dice of 80.44%. All these findings reveal the great potential of X-CTRSNet in clinical imaging and diagnosis to facilitate emergency triage by enabling precise 3D reconstruction and segmentation on 2D X-ray images.

Using a small number of sampling views during a CT (computed tomography) exam is a widely accepted technique for low-dose CT reconstruction, which reduces the risk of inducing cancer or other diseases in patients. In this scenario, total variation (TV) based compressed sensing (CS) methods, which uses a regularization term that penalizes the $\ell_{1}$ norm of the reconstructed image's gradient, outperform the traditional FBP (filtered back-projection) based algorithms in CT reconstruction. Furthermore, in order to reduce well-known artifacts (smoothed edges and texture details) favored by TV-based CS methods, several variants have been proposed, which, in a general context, can be understood as using a regularization term that approximates the $\ell_{0}$ norm of the reconstructed image's gradient. These type of methods yield state-of-the-art reconstruction results. In this paper we exploit a variant of the $\ell_{0}$ gradient minimization problem, which directly penalizes the number of non-zero gradients in the reconstructed image, and propose to solve the low-dose CT reconstruction problem. Extended experiments, based on the ASTRA toolbox, show that the propose method is faster (almost twice as fast) and delivers higher quality reconstructions than TV-based CS methods and alternatives that reduce smooth artifacts.

X-ray computed tomography (XCT)is used regularly at synchrotron light sources to study the internal morphology of materials at high resolution. However, experimental constraints, such as radiation sensitivity, can result in noisy or undersampled measurements. Further, depending on the resolution, sample size and data acquisition rates, the resulting noisy dataset can be terabyte-scale. Advanced iterative reconstruction techniques can produce high-quality images from noisy measurements, but their computational requirements have made their use exception rather than the rule. We propose here a novel memory-centric approach that avoids redundant computations at the expense of additional memory complexity. We develop a system, MemXCT, that uses an optimized SpMV implementation with two-level pseudo-Hilbert ordering and multi-stage input buffering. We evaluate MemXCT on various supercomputer architectures incolving KNL and GPU. MemXCT can reconstruct a large (11K×11K) mouse brain tomogram in ~10 seconds using 4096 KNL nodes (256K cores), the largest iterative reconstruction achieved in near-real time.

Computed tomography (CT) plays an increasingly important role in clinical diagnosis. However, in practical applications of CT, physical limitations on acquisition lead to some blind regions where data cannot be sampled. CT image reconstruction from limited-angle would enable a rapid scanning with a reduced x-ray dose delivered to the patient. As it is known, Generative Adversarial Networks (GAN) can keep the original information and details of the sample very well. In this paper, we propose an end-to-end Generative Adversarial Networks model used for removing artifacts from limited-angle CT reconstruction images. The proposed GAN is based on the conditional GAN with the joint loss function, which .can remove the artifacts while retaining the complete details and sharp edges. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. Compared to several other classic methods, our GAN model shows better consequent, in terms of artifact reduction, feature preservation, and computational efficiency for limited-angle CT reconstruction.

Background: Evaluation and management of osteochondral lesions of the talus (OLTs) often warrant advanced imaging studies, especially in revision or cases with cystic defects. It is possible that orthopedic surgeons may overestimate the size and misinterpret the morphology of OLT from conventional computed tomography (CT), thereby influencing treatment strategies. The purpose of this study was to determine the utility of a novel means to estimate the true-volume of OLTs using 3D reconstructed images and volume analysis. Methods: With Institutional Review Board approval, an institutional radiology database was queried for patients with cystic OLTs that failed previous microfracture, having compatible CT scans and magnetic resonance imaging (MRI) between 2011 and 2016. Fourteen patients met inclusion criteria. Of these, 5 cases were randomly selected for 3D CT reconstruction modeling. Ten orthopedic surgeons independently estimated the volume of these 5 OLTs via standard CT. Then 3D reconstructions were made and morphometric true-volume (MTV) analysis measurements of each OLT were generated. The percent change in volumes from CT were compared to MTVs determined from 3D reconstructive analysis. Results: On average, the volume calculated by conventional CT scanner grossly overestimated the actual size of the OLTs. The volume calculated on conventional CT scanner overestimated the size of OLTs compared to the 3D MTV reconstructed analysis by 285% to 864%. Conclusions: Our results showed that conventional measurements of OLTS with CT grossly overestimated the size of the lesion. The 3D MTV analysis of cystic osteochondral lesions may help clinicians with preoperative planning for graft selection and appropriate volume while avoiding unnecessary costs incurred with overestimation. Level of Evidence: Level IV, case series.

Sparse-view Reconstruction can be used to provide accelerated low dose CT imaging with both accelerated scan and reduced projection/back-projection calculation. Despite the rapid developments, image noise and artifacts still remain a major issue in the low dose protocol. In this paper, a deep learning based method named Improved GoogLeNet is proposed to remove streak artifacts due to projection missing in sparse-view CT reconstruction. Residual learning is used in GoogLeNet to study the artifacts of sparse-view CT reconstruction, and then subtracts the artifacts obtained by learning from the sparse reconstructed images, finally recovers a clear correction image. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. The results indicate that the proposed method is practical and effective for reducing the artifacts and preserving the quality of the reconstructed image.

In some practical applications of computed tomography (CT) imaging, the projections of an object are obtained within a limited-angle range due to the restriction of the scanning environment. In this situation, conventional analytic algorithms, such as filtered backprojection (FBP), will not work because the projections are incomplete. An image reconstruction algorithm based on total variation minimization (TVM) can significantly reduce streak artifacts in sparse-view reconstruction, but it will not effectively suppress slope artifacts when dealing with limited-angle reconstruction problems. To solve this problem, we consider a family of image reconstruction model based on \begin{document} $\ell_{0}$ \end{document} and \begin{document} $\ell_{2}$ \end{document} regularizations for limited-angle CT and prove the existence of a solution for two CT reconstruction models. The Alternating Direction Method of Multipliers (ADMM)-like method is utilized to solve our model. Furthermore, we prove the convergence of our algorithm under certain conditions. Some numerical experiments are used to evaluate the performance of our algorithm and the results indicate that our algorithm has advantage in suppressing slope artifacts.

Technetium-99m (99mTc)-sestamibi single-photon emission computed tomography/computed tomography (SPECT/CT) has previously been shown to allow for the accurate differentiation of benign renal oncocytomas and hybrid oncocytic/chromophobe tumors (HOCTs) apart from other malignant renal tumor histologies, with oncocytomas/HOCTs showing high uptake and renal cell carcinoma (RCC) showing low uptake based on uptake ratios from non-quantitative single-photon emission computed tomography (SPECT) reconstructions. However, in this study, several tumors fell close to the uptake ratio cutoff, likely due to limitations in conventional SPECT/CT reconstruction methods. We hypothesized that application of quantitative SPECT/CT (QSPECT) reconstruction methods developed by our group would provide more robust separation of hot and cold lesions, serving as an imaging framework on which quantitative biomarkers can be validated for evaluation of renal masses with 99mTc-sestamibi. Single-photon emission computed tomography data were reconstructed using the clinical Flash 3D reconstruction and QSPECT methods. Two blinded readers then characterized each tumor as hot or cold. Semi-quantitative uptake ratios were calculated by dividing lesion activity by background renal activity for both Flash 3D and QSPECT reconstructions. The difference between median (mean) hot and cold tumor uptake ratios measured 0.655 (0.73) with the QSPECT method and 0.624 (0.67) with the conventional method, resulting in increased separation between hot and cold tumors. Sub-analysis of 7 lesions near the separation point showed a higher absolute difference (0.16) between QPSECT and Flash 3D mean uptake ratios compared to the remaining lesions. Our finding of improved separation between uptake ratios of hot and cold lesions using QSPECT reconstruction lays the foundation for additional quantitative SPECT techniques such as SPECT-UV in the setting of renal 99mTc-sestamibi and other SPECT/CT exams. With robust quantitative image reconstruction and biomarker analysis, there may be an expanded role for SPECT/CT imaging in renal masses and other pathologic conditions.

Purpose It is accepted that a donor's predicted total lung capacity (pTLC) can be 75-125% of the recipient's pTLC in lung transplantation; however, ‘optimal graft size’ is not clearly defined, especially in restricted lung disease (RD) patients. These disagreements exist as it is difficult to accurately measure lung size before and after transplantation and it is also difficult to predict and evaluate the extent of re-expansion of the restricted thoracic cavity after transplantation. This study examined changes in lung volume after lung transplantation using a 3D lung volume measurement from CT scans before and after surgery under the assumption that the patient's thoracic cavity would return to the size of the patient without underlying lung disease after surgery. Methods A total of 113 patients, who underwent lung transplantation between September 2012 and April 2019 in our institute, and met the inclusion criteria were enrolled in the study. This included 88 patients who were diagnosed with RD and 25 patients who were diagnosed with non-RD. Patients were further divided into oversized and undersized groups according to the donor and recipient pTLC ratio, and a total of 4 groups were studied. Preoperative and postoperative lung volume on CT scans were measured using the synapse 3D program (Fuji film, Japan). Results RD patients showed a significant increase in total lung volume (TLV) after surgery, while non-RD patients showed a decrease in TLV. These changes showed the greatest increase in the RD group within 1 year and continuously increased up to 5 years. In the non-RD group, TLV decreased within 3 months of surgery. These changes were not related to graft size. Conclusion In lung transplant patients, the thoracic cavity re-expanded in RD patients and reduced in the non-RD patients, returning to healthy values without lung disease status in terms of TLV.

Low-dose computed tomography (LdCT) imaging can greatly reduce the radiation dose imposed to patient, however it leads to the low signal-to-noise ratio (SNR) measured projection data. Using conventional analytical reconstruction method (e.g., filtered back-projection method), the reconstruction results usually suffer from serious noise in LdCT. To obtain high-quality CT images, iterative reconstruction method combined with prior knowledge of the object is of great importance. In this work, both structural group sparsity and gradient prior sparsity are jointed as a novel regularization constraint in the proposed CT reconstruction model. To solve the optimization-based CT reconstruction problem, original problem was transformed into a series of sub-problems based on alternating direction method of multipliers framework. The merit of the proposed joint regularization method is that global and local sparsity are both utilized. To valid the performance of proposed reconstruction algorithm, we did simulated experiments with different noise levels and real data studies. The qualitative and quantitative analyses show that the proposed reconstruction algorithm has better performance than other iterative reconstruction algorithms. What's more, compared to the existing iterative reconstruction methods, the proposed reconstruction algorithm can well reconstructed important structure features and effectively suppress the noise and artifacts.

In recent computed tomography (CT) reconstruction, iterative methods have been often used owing to the potential to provide three-dimensional (3D) images of superior reconstruction quality to common filtered-backprojection (FBP)-based methods. However, these methods require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, to overcome these difficulties, we propose a new cone-beam CT reconstruction with a dual-resolution voxelization strategy for a small region-of-interest (ROI) reconstruction in which the voxels outside the target ROI are binned by, for example, 2 × 2 × 2, 4 × 4 × 4, 8 × 8 × 8, etc., while the other voxels remain unbinned. We considered a compressed-sensing (CS)-based algorithm with a dual regularization strategy, rather than common FBP-based methods, for more accurate CT reconstruction. We implemented the proposed CS-based algorithm and performed a systematic simulation and experiment. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative CT reconstruction, keeping the reconstruction quality inside the target ROI not much degraded.

Background and purpose We investigated whether automated and reader-based ASPECTS in acute stroke patients are affected by different CT image reconstruction algorithms. Methods ASPECTS were assessed by commercial software and four independent blinded readers (two residents and two consultants) from different CT reconstructions (filtered back projection and two different iterative reconstruction [IR] levels) in 43 acute stroke patients with proximal middle cerebral artery occlusion. Ground truth was provided by an expert with unrestricted data access. Results The residents showed significant variations between IR levels and had a significantly lower internal consistency across different reconstructions compared to the software, which performed similarly to the consultants. The consultant as well as the software also showed different deviations from ground truth with different IR levels, which were least at IR strength level 2. Conclusions CT image postprocessing affects either automated or human ASPECTS in acute stroke patients. This effect was most pronounced in the less experienced readers, while the software had the most robust performance.

Background Results of recent phantom studies show that variation in CT acquisition parameters and reconstruction techniques may make radiomic features largely nonreproduceable and of limited use for prognostic clinical studies. Purpose To investigate the effect of CT radiation dose and reconstruction settings on the reproducibility of radiomic features, as well as to identify correction factors for mitigating these sources of variability. Materials and Methods This was a secondary analysis of a prospective study of metastatic liver lesions in patients who underwent staging with single-energy dual-source contrast material-enhanced staging CT between September 2011 and April 2012. Technique parameters were altered, resulting in 28 CT data sets per patient that included different dose levels, section thicknesses, kernels, and reconstruction algorithm settings. By using a training data set (n = 76), reproducible intensity, shape, and texture radiomic features (reproducibility threshold, R2 ≥ 0.95) were selected and correction factors were calculated by using a linear model to convert each radiomic feature to its estimated value in a reference technique. By using a test data set (n = 75), the reproducibility of hierarchical clustering based on 106 radiomic features measured with different CT techniques was assessed. Results Data in 78 patients (mean age, 60 years ± 10; 33 women) with 151 liver lesions were included. The percentage of radiomic features deemed reproducible for any variation of the different technical parameters was 11% (12 of 106). Of all technical parameters, reconstructed section thickness had the largest impact on the reproducibility of radiomic features (12.3% [13 of 106]) if only one technical parameter was changed while all other technical parameters were kept constant. The results of the hierarchical cluster analysis showed improved clustering reproducibility when reproducible radiomic features with dedicated correction factors were used (ρ = 0.39-0.71 vs ρ = 0.14-0.47). Conclusion Most radiomic features are highly affected by CT acquisition and reconstruction settings, to the point of being nonreproducible. Selecting reproducible radiomic features along with study-specific correction factors offers improved clustering reproducibility. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Limited-angle reconstruction is a very important but challenging problem in the field of computed tomography (CT) and it has been extensively studied for many years. However, some difficulties still remain. Based on the theory of visible and invisible boundary developed by Quinto et.al, we propose a reconstruction model for limited-angle CT, which encodes the visible edges as priors to recover the invisible ones. The new model introduces an anisotropic ep (0

Objectives The accuracy of pedicle screw placement strongly affects the outcome of spinal surgery and has mainly relied on the surgeons’ experience. There is no simple, low-cost, and effective pedicle screw placement system to assist new spinal surgeons with less experience. Methods We designed a localization system with six parameters (starting point height [SP-H], starting point length [SP-L], transverse section angle, sagittal section angle [SSA], pedicle width [W] and height [H]) based on preoperative computed tomography reconstruction and combined it with the Roussouly classification to guide lumbar spine pedicle screw placement and analysed the change patterns of the six parameters in 50 participants. Results Based on the system, we confirmed that combining SP-H and SP-L can localize the entrance of the pedicle screw. Furthermore, we considered that SP-L and transverse section angle would be a new standard for determination of the transverse orientation of the pedicle screw. More importantly, the linear regression equations between H and W and SP-H and H were concealed. In addition, H and W can guide the appropriate selection of pedicle screw. Moreover, change patterns of SSA combined with the Roussouly classification indicate that SSA of L3 can be used as a benchmark to guide the establishment of sagittal alignment of the lumbar spine. Conclusions Understanding and applying the six-parameter localization system are essential for achieving accuracy in lumbar spine pedicle screw placement, and the system is a useful guide in the establishment of sagittal alignment. The translational potential of this article This study provides a new pedicle-screw placement system for accurate lumbar spine pedicle screw placement based on three-dimensional CT reconstruction, requiring six parameters to guide the system.

Background Iterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT). Purpose To examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment. Material and Methods The LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures. Results The image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP ( P ≤ 0.0001). While detectability increased in only 2/16 structures ( P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction ( P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced ( P = 0.003). Conclusion An ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

Background This study aimed to evaluate the safety and feasibility of subaxial cervical pedicle screw (CPS) insertion by comparing the morphological parameters between developmental canal stenosis (DCS) and non-developmental canal stenosis (NDCS) patients. Methods A total of 120 Chinese patients who had undergone cervical spinal multiplanar CT imaging from September 2010 to December 2014 were included in this study. According to the Pavlov ratio (PR), participants were classified into a DCS group (PR

Sparse-view computed tomography (CT) has attracted much attention to reduce the potential radiation risk. However, image reconstruction from insufficient data suffers from distortion. As a discrete form of Radon transform, Mojette transform is exactly invertible even with few samples based on discrete projection and reconstruction lattice. The goal of this paper is to apply Mojette transform to reconstruct images from few views in practice. Due to the fact that its acquisition is incompatible with physical X-ray properties, this paper resorts to deep learning technique to map Radon projection into Mojette domain. Experimental results confirm the effectiveness of the proposed reconstruction scheme based on Mojette transform.

Treatments including selective neurectomy, muscle resections and botulinum toxin A (BTX) injections have been used to improve the stocky appearance of calves. BTX injection has the advantages of high efficiency and is almost noninvasive. However, criterion standards of injection are still missing.We aimed to establish a method to classify the hypertrophic calf for a personalized treatment and set up an injection protocol based on the findings.Three-dimensional CT reconstruction was used to measure the thickness and cross-sectional area of the triceps surae. B-mode ultrasound and palpation were used to evaluate the muscle thickness and determine the dosage. Patients were followed 3 and 6 months after the treatment.A total of 112 legs were classified into three degrees of thickness ( 15 mm, 15-25 mm and 25 mm). Twenty-seven subjects were treated with an individualized BTX (100-300 U). Maximal circumference decreased 0.33 ± 0.00 cm after 3 month (p 0.05) and 0.67 ± 0.11 cm after 6 months (p 0.01). The angulated calf contour was improved. No severe side effects were reported.Localizing and dosage are the key points when applying BTX. Dosage should be decided by muscle thickness instead of circumference. BTX treatment improves the prominent contour of the calf rather than reducing the volume.This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

In this paper, we propose a new iterative algorithm for Computed Tomography (CT) reconstruction when the problem has uncertainty in the view angles. The algorithm models this uncertainty by an additive model-discrepancy term leading to an estimate of the uncertainty in the likelihood function. This means we can combine state-of-the-art regularization priors such as total variation with this likelihood. To achieve a good reconstruction the algorithm alternates between updating the CT image and the uncertainty estimate in the likelihood. In simulated 2D numerical experiments, we show that our method is able to improve the relative reconstruction error and visual quality of the CT image for the uncertain-angle CT problem.

Computed tomography (CT) can provide a 3D view of the patient's internal organs, facilitating disease diagnosis, but it incurs more radiation dose to a patient and a CT scanner is much more cost prohibitive than an X-ray machine too. Traditional CT reconstruction methods require hundreds of X-ray projections through a full rotational scan of the body, which cannot be performed on a typical X-ray machine. In this work, we propose to reconstruct CT from two orthogonal X-rays using the generative adversarial network (GAN) framework. A specially designed generator network is exploited to increase data dimension from 2D (X-rays) to 3D (CT), which is not addressed in previous research of GAN. A novel feature fusion method is proposed to combine information from two X-rays. The mean squared error (MSE) loss and adversarial loss are combined to train the generator, resulting in a high-quality CT volume both visually and quantitatively. Extensive experiments on a publicly available chest CT dataset demonstrate the effectiveness of the proposed method. It could be a nice enhancement of a low-cost X-ray machine to provide physicians a CT-like 3D volume in several niche applications.

We investigated whether the diagnostic performance of machine learning-based radiomics models for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among subsolid nodules (SSNs) was affected by the proportion of images reconstructed with filtered back projection (FBP) and model-based iterative reconstruction (MBIR) in datasets used for feature extraction.This retrospective study included 60 patients (23 men and 37 women; mean age, 61.4 years) with 69 SSNs (54 part-solid and 15 pure ground-glass nodules). Preoperative CT scans were reconstructed with both FBP and MBIR. A total of 860 radiomics features were obtained from the entire nodule volume, and 70 resampled nodule datasets with an increasing proportion of nodules with MBIR-derived features (from 0/69 to 69/69) were prepared. After feature selection using neighborhood component analysis, support vector machines (SVMs) and an ensemble model were used as classifiers for the differentiation of IPAs. The diagnostic performances of all blending proportions of reconstruction algorithms were calculated and analyzed.The ROC AUC and the diagnostic accuracy of the radiomics models decreased significantly as the number of nodules with MBIR-derived features increased, and this relationship followed cubic functions (RInclusion of CT scans reconstructed with MBIR for radiomics modeling can significantly decrease diagnostic performance for the identification of IPAs.

The pore structure and seepage characteristics of coal are of great significance to the study of seepage effect of coal seam water injection. Because the current methods for comprehensively characterizing the pore structure and seepage effect of coal mass are imperfect, a multi-scale and visualization method for exploring coal structure and seepage effect is proposed. Through 3D CT reconstruction, Nuclear Magnetic Resonance (NMR) T2 spectrum and Magnetic Resonance Imaging (MRI), the pore size distribution, connectivity, and seepage characteristics of 4 coal samples are explored. The results show that the pore and fissure structures larger than 47.59 μm can be obtained through 3D reconstruction and the equivalent pore diameters (Deq) of the 4 coal samples are mainly concentrated between 100 and 500 μm. Because the pore size distribution in 0–100 μm is supplemented through NMR, it is found that the pore size of each coal sample is mainly distributed between 0.01 and 0.1 μm. The 3D reconstructed pore structure is basically consistent with the water signal spectrum obtained by NMR in the water saturated state, and both methods show that samples Q-1 and Q-2 have good pore connectivity. In addition, it is indicated from the NMR seepage experiments that Q-1 and Q-2 have more seepage space made of large-scale pores and fissures, and thus better connectivity. Therefore, the seepage effect of water injection for coal mass with different structures can be reflected by combining CT and NMR methods to comprehensively characterize the pore structure at different scales, which eliminates the limitations of a single method.