Clinical and radiological documentation of complete remodeling of the mandibular bone after the enucleation of a large odontogenic keratocyst: 15 years follow-up of a unique case - A case report.

INTRODUCTION AND IMPORTANCE: Odontogenic keratocysts (OKC) are benign intraosseous cysts with expansive growth. They account for approximately 7.8 % of all jaw cysts and have a high recurrence rate. Herein, we present a minimally invasive approach for the surgical treatment of a remarkable variation of OKC with a 15-year radiological and clinical follow-up. PRESENTATION OF THE CASE: We present the case of a 42-year-old female patient with a large cyst in the mandible between teeth 35 and 45, who reported spontaneous swelling and paresthesia of the lower lip. Radiological imaging is crucial for treatment planning. The cyst was surgically treated with a single enucleation combined with adjuvant therapy to minimise recurrence. A titanium plate was inserted because of the size of the defect. Recurrence was observed one year later and treated with single enucleation and adjuvant therapy. After 15 years, complete healing, no signs of recurrence, and complete remodeling of the mandible were observed. CLINICAL DISCUSSION: The treatment of OKC remains the subject of varying approaches in the literature due to the lack of established general guidelines. One treatment option is single enucleation combined with adjuvant therapy to minimise recurrence, which can result in complete clinical and radiological remodeling of the bone. CONCLUSION: Direct enucleation combined with adjuvant therapy is a practical approach for treating large OKCs. It is associated with less morbidity and burden on the patient than enucleation with prior decompression or radical resection. Additionally, it shows no deficits in bone defect healing.

Intra-fractional lung tumor motion monitoring using arbitrary gantry angles during radiotherapy treatment.

Intensity-based 2D/3D registration using kilo-voltage (kV) and mega-voltage (MV) on-board imaging is a promising approach for real-time tumor motion tracking. So far, the performance of the kV images as well as kV-MV image pairs for 2D/3D registration using only one gantry angle (in anterior-posterior (AP) direction) has been investigated on patient data. In stereotactic body radiation therapy (SBRT), however, various gantry angles are typically used. This study attempts to answer the question of whether automatic 2D/3D registration is possible using kV images as well as kV-MV image pairs for gantry angles other than the AP direction. We also investigated the effect of additional portal MV images paired with kV images to improve 2D/3D registration in extracting cranio-caudal (CC) and AP displacement at arbitrary gantry angles and different fractions. The kV and MV image sequences as well as 3D volume data from five patients suffering from non-small cell lung cancer undergoing SBRT were used. Diaphragm motion served as the reference signal. The CC and AP displacements resulting from the registration results were compared with the corresponding reference motion signal. Pearson correlation coefficients (R value) was used to calculate the similarity measure between reference signal and the extracted displacements resulting from the registration. Signals we found that using 2D/3D registration tumor motion in 5 degrees of freedom (DOF) with kV images and in 6 degrees of freedom with kV-MV image pairs can be extracted for most gantry angles in all patients. Furthermore, our results have shown that the use of kV-MV image pairs increases the overall chance of tumor visibility and therefore leads to more successful extraction of CC as well as AP displacements for almost all gantry angles in all patients. We observed an improvement in registration of at least 0.29% more gantry angle for all patients when we used kV-MV images compared to kV images alone. In addition, an improvement in the R-value was observed in up to 16 fractions in various patients.

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior segmentation performances compared to classical machine learning and image processing techniques. However, these models need fully annotated datasets for training which is challenging to acquire, especially in the medical domain. In this work, we release one of the biggest fully manually annotated datasets of nuclei in Hematoxylin and Eosin (H&E)-stained histological images, called NuInsSeg. This dataset contains 665 image patches with more than 30,000 manually segmented nuclei from 31 human and mouse organs. Moreover, for the first time, we provide additional ambiguous area masks for the entire dataset. These vague areas represent the parts of the images where precise and deterministic manual annotations are impossible, even for human experts. The dataset and detailed step-by-step instructions to generate related segmentation masks are publicly available on the respective repositories.

Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images. Among different histopathological image analysis tasks, nuclei instance segmentation plays a fundamental role in a wide range of clinical and research applications. While many semi- and fully-automatic computerized methods have been proposed for nuclei instance segmentation, deep learning (DL)-based approaches have been shown to deliver the best performances. However, the performance of such approaches usually degrades when tested on unseen datasets. In this work, we propose a novel method to improve the generalization capability of a DL-based automatic segmentation approach. Besides utilizing one of the state-of-the-art DL-based models as a baseline, our method incorporates non-deterministic train time and deterministic test time stain normalization, and ensembling to boost the segmentation performance. We trained the model with one single training set and evaluated its segmentation performance on seven test datasets. Our results show that the proposed method provides up to 4.9%, 5.4%, and 5.9% better average performance in segmenting nuclei based on Dice score, aggregated Jaccard index, and panoptic quality score, respectively, compared to the baseline segmentation model.

Breast MRI radiomics and machine learning-based predictions of response to neoadjuvant chemotherapy - How are they affected by variations in tumor delineation?

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affect different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.

Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.

High-grade serous ovarian cancer is the most lethal gynaecological malignancy. Detailed molecular studies have revealed marked intra-patient heterogeneity at the tumour microenvironment level, likely contributing to poor prognosis. Despite large quantities of clinical, molecular and imaging data on ovarian cancer being accumulated worldwide and the rise of high-throughput computing, data frequently remain siloed and are thus inaccessible for integrated analyses. Only a minority of studies on ovarian cancer have set out to harness artificial intelligence (AI) for the integration of multiomics data and for developing powerful algorithms that capture the characteristics of ovarian cancer at multiple scales and levels. Clinical data, serum markers, and imaging data were most frequently used, followed by genomics and transcriptomics. The current literature proves that integrative multiomics approaches outperform models based on single data types and indicates that imaging can be used for the longitudinal tracking of tumour heterogeneity in space and potentially over time. This review presents an overview of studies that integrated two or more data types to develop AI-based classifiers or prediction models.Relevance statement Integrative multiomics models for ovarian cancer outperform models using single data types for classification, prognostication, and predictive tasks.Key points• This review presents studies using multiomics and artificial intelligence in ovarian cancer.• Current literature proves that integrative multiomics outperform models using single data types.• Around 60% of studies used a combination of imaging with clinical data.• The combination of genomics and transcriptomics with imaging data was infrequently used.

Silicone phantoms fabricated with multi-material extrusion 3D printing technology mimicking imaging properties of soft tissues in CT.

Recently, 3D printing has been widely used to fabricate medical imaging phantoms. So far, various rigid 3D printable materials have been investigated for their radiological properties and efficiency in imaging phantom fabrication. However, flexible, soft tissue materials are also needed for imaging phantoms for simulating several clinical scenarios where anatomical deformations is important. Recently, various additive manufacturing technologies have been used to produce anatomical models based on extrusion techniques that allow the fabrication of soft tissue materials. To date, there is no systematic study in the literature investigating the radiological properties of silicone rubber materials/fluids for imaging phantoms fabricated directly by extrusion using 3D printing techniques. The aim of this study was to investigate the radiological properties of 3D printed phantoms made of silicone in CT imaging. To achieve this goal, the radiodensity as described as Hounsfield Units (HUs) of several samples composed of three different silicone printing materials were evaluated by changing the infill density to adjust their radiological properties. A comparison of HU values with a Gammex Tissue Characterization Phantom was performed. In addition, a reproducibility analysis was performed by creating several replicas for specific infill densities. A scaled down anatomical model derived from an abdominal CT was also fabricated and the resulting HU values were evaluated. For the three different silicone materials, a spectrum ranging from -639 to +780 HU was obtained on CT at a scan setting of 120 kVp. In addition, using different infill densities, the printed materials were able to achieve a similar radiodensity range as obtained in different tissue-equivalent inserts in the Gammex phantom (238 HU to -673 HU). The reproducibility results showed good agreement between the HU values of the replicas compared to the original samples, confirming the reproducibility of the printed materials. A good agreement was observed between the HU target values in abdominal CT and the HU values of the 3D-printed anatomical phantom in all tissues.

3D-Printed multi-material liver model with simultaneous mechanical and radiological tissue-mimicking features for improved realism.

89Anatomic models have an important role in the medical domain. However, soft tissue mechanical properties' representation is limited in mass-produced and 3D-printed models. In this study, a multi-material 3D printer was used to print a human liver model featuring tuned mechanical and radiological properties, with the goal of comparing the printed model with its printing material and real liver tissue. The main target was mechanical realism, while radiological similarity was a secondary objective. Materials and internal structure were selected such that the printed model would resemble liver tissue in terms of tensile properties. The model was printed at 33% scaling and 40% gyroid infill with a soft silicone rubber, and silicone oil as a filler fluid. After printing, the liver model underwent CT scanning. Since the shape of the liver is incompatible with tensile testing, tensile testing specimens were also printed. Three replicates were printed with the same internal structure as the liver model and three more out of silicone rubber with 100% rectilinear infill to allow a comparison. All specimens were tested in a four-step cyclic loading test protocol to compare elastic moduli and dissipated energy ratios. The fluid-filled and full-silicone specimens had initial elastic moduli of 0.26 MPa and 0.37 MPa, respectively, and featured dissipated energy ratios of 0.140, 0.167, 0.183, and 0.118, 0.093, 0.081, respectively, in the second, third, and fourth loading cycles. The liver model showed 225 ± 30 Hounsfield units (HU) in CT, which is closer to real human liver (70 ± 30 HU) than the printing silicone (340 ± 50 HU). Results suggest that the liver model became more realistic in terms of mechanical and radiological properties with the proposed printing approach as opposed to printing only with silicone rubber. Thus, it has been demonstrated that this printing method enables new customization opportunities in the field of anatomic models.

On Krylov methods for large-scale CBCT reconstruction.

Krylov subspace methods are a powerful family of iterative solvers for linear systems of equations, which are commonly used for inverse problems due to their intrinsic regularization properties. Moreover, these methods are naturally suited to solve large-scale problems, as they only require matrix-vector products with the system matrix (and its adjoint) to compute approximate solutions, and they display a very fast convergence. Even if this class of methods has been widely researched and studied in the numerical linear algebra community, its use in applied medical physics and applied engineering is still very limited. e.g. in realistic large-scale computed tomography (CT) problems, and more specifically in cone beam CT (CBCT). This work attempts to breach this gap by providing a general framework for the most relevant Krylov subspace methods applied to 3D CT problems, including the most well-known Krylov solvers for non-square systems (CGLS, LSQR, LSMR), possibly in combination with Tikhonov regularization, and methods that incorporate total variation regularization. This is provided within an open source framework: the tomographic iterative GPU-based reconstruction toolbox, with the idea of promoting accessibility and reproducibility of the results for the algorithms presented. Finally, numerical results in synthetic and real-world 3D CT applications (medical CBCT andµ-CT datasets) are provided to showcase and compare the different Krylov subspace methods presented in the paper, as well as their suitability for different kinds of problems.

Source-detector trajectory optimization for CBCT metal artifact reduction based on PICCS reconstruction.

Precise instrument placement plays a critical role in all interventional procedures, especially percutaneous procedures such as needle biopsies, to achieve successful tumor targeting and increased diagnostic accuracy. C-arm cone beam computed tomography (CBCT) has the potential to precisely visualize the anatomy in direct vicinity of the needle and evaluate the adequacy of needle placement during the intervention, allowing for instantaneous adjustment in case of misplacement. However, even with the most advanced C-arm CBCT devices, it can be difficult to identify the exact needle position on CBCT images due to the strong metal artifacts around the needle. In this study, we proposed a framework for customized trajectory design in CBCT imaging based on Prior Image Constrained Compressed Sensing (PICCS) reconstruction with the goal of reducing metal artifacts in needle-based procedures. We proposed to optimize out-of-plane rotations in three-dimensional (3D) space and minimize projection views while reducing metal artifacts at specific volume of interests (VOIs). An anthropomorphic thorax phantom with a needle inserted inside and two tumor models as the imaging targets were used to validate the proposed approach. The performance of the proposed approach was also evaluated for CBCT imaging under kinematic constraints by simulating some collision areas on the geometry of the C-arm. We compared the result of optimized 3D trajectories using the PICCS algorithm and 20 projections with the result of a circular trajectory with sparse view using PICCS and Feldkamp, Davis, and Kress (FDK), both using 20 projections, and the circular FDK method with 313 projections. For imaging targets 1 and 2, the highest values of structural similarity index measure (SSIM) and universal quality index (UQI) between the reconstructed image from the optimized trajectories and the initial CBCT image at the VOI was calculated 0.7521, 0.7308 and 0.7308, 0.7248 respectively. These results significantly outperformed the FDK method (with 20 and 313 projections) and the PICCS method (20 projections) both using the circular trajectory. Our results showed that the proposed optimized trajectories not only significantly reduce metal artifacts but also suggest a dose reduction for needle-based CBCT interventions, considering the small number of projections used. Furthermore, our results showed that the optimized trajectories are compatible with spatially constrained situations and enable CBCT imaging under kinematic constraints when the standard circular trajectory is not feasible.

Technical note: Extended longitudinal and lateral 3D imaging with a continuous dual-isocenter CBCT scan.

BACKGROUND: The clinical benefits of intraoperative cone beam CT (CBCT) during orthopedic procedures include (1) improved accuracy for procedures involving the placement of hardware and (2) providing immediate surgical verification. PURPOSE: Orthopedic interventions often involve long and wide anatomical sites (e.g., lower extremities). Therefore, in order to ensure that the clinical benefits are available to all orthopedic procedures, we investigate the feasibility of a novel imaging trajectory to simultaneously expand the CBCT field-of-view longitudinally and laterally. METHODS: A continuous dual-isocenter imaging trajectory was implemented on a clinical robotic CBCT system using additional real-time control hardware. The trajectory consisted of 200° circular arcs separated by alternating lateral and longitudinal table translations. Due to hardware constraints, the direction of rotation (clockwise/anticlockwise) and lateral table translation (left/right) was reversed every 400°. X-ray projections were continuously acquired at 15 frames/s throughout all movements. A whole-body phantom was used to verify the trajectory. As comparator, a series of conventional large volume acquisitions were stitched together. Image quality was quantified using Root Mean Square Deviation (RMSD), Mean Absolute Percentage Deviation (MAPD), Structural Similarity Index Metric (SSIM) and Contrast-to-Noise Ratio (CNR). RESULTS: The imaging volume produced by the continuous dual-isocenter trajectory had dimensions of L = 95 cm × W = 45 cm × H = 45 cm. This enabled the hips to the feet of the whole-body phantom to be captured in approximately half the imaging dose and acquisition time of the 11 stitched conventional acquisitions required to match the longitudinal and lateral imaging dimensions. Compared to the stitched conventional images, the continuous dual-isocenter acquisition had RMSD of 4.84, MAPD of 6.58% and SSIM of 0.99. The CNR of the continuous dual-isocenter and stitched conventional acquisitions were 1.998 and 1.999, respectively. CONCLUSION: Extended longitudinal and lateral intraoperative volumetric imaging is feasible on clinical robotic CBCT systems.

Additively manufactured test phantoms for mimicking soft tissue radiation attenuation in CBCT using Polyjet technology.

OBJECTIVES: To develop and validate a simple approach for building cost-effective imaging phantoms for Cone Beam Computed Tomography (CBCT) using a modified Polyjet additive manufacturing technology where a single material can mimic a range of human soft-tissue radiation attenuation. MATERIALS AND METHODS: Single material test phantoms using a cubic lattice were designed in 3-Matic 15.0 software . Keeping the individual cubic lattice volume constant, eight different percentage ratio (R) of air: material from 0% to 70% with a 10% increment were assigned to each sample. The phantoms were printed in three materials, namely Vero PureWhite, VeroClear and TangoPlus using Polyjet technology. The CT value analysis, non-contact profile measurement and microCT-based volumetric analysis was performed for all the samples. RESULTS: The printed test phantoms produced a grey value spectrum equivalent to the radiation attenuation of human soft tissues in the range of -757 to +286 HU on CT. The results from dimensional comparison analysis of the printed phantoms with the digital test phantoms using non-contact profile measurement showed a mean accuracy of 99.07 % and that of micro-CT volumetric analysis showed mean volumetric accuracy of 84.80-94.91%. The material and printing costs of developing 24 test phantoms was 83.00 Euro. CONCLUSIONS: The study shows that additive manufacturing-guided macrostructure manipulation modifies successfully the radiographic visibility of a material in CBCT imaging with 1 mm3 resolution, helping customization of imaging phantoms.

Realistic 3D printed CT imaging tumor phantoms for validation of image processing algorithms.

Medical imaging phantoms are widely used for validation and verification of imaging systems and algorithms in surgical guidance and radiation oncology procedures. Especially, for the performance evaluation of new algorithms in the field of medical imaging, manufactured phantoms need to replicate specific properties of the human body, e.g., tissue morphology and radiological properties. Additive manufacturing (AM) technology provides an inexpensive opportunity for accurate anatomical replication with customization capabilities. In this study, we proposed a simple and cheap protocol using Fused Deposition Modeling (FDM) technology to manufacture realistic tumor phantoms based on the filament 3D printing technology. Tumor phantoms with both homogenous and heterogeneous radiodensity were fabricated. The radiodensity similarity between the printed tumor models and real tumor data from CT images of lung cancer patients was evaluated. Additionally, it was investigated whether a heterogeneity in the 3D printed tumor phantoms as observed in the tumor patient data had an influence on the validation of image registration algorithms. A radiodensity range between -217 to 226 HUs was achieved for 3D printed phantoms using different filament materials; this range of radiation attenuation is also observed in the human lung tumor tissue. The resulted HU range could serve as a lookup-table for researchers and phantom manufactures to create realistic CT tumor phantoms with the desired range of radiodensities. The 3D printed tumor phantoms also precisely replicated real lung tumor patient data regarding morphology and could also include life-like heterogeneity of the radiodensity inside the tumor models. An influence of the heterogeneity on accuracy and robustness of the image registration algorithms was not found.

Extension of the open-source TIGRE toolbox for proton imaging.

Proton irradiation is a well-established method to treat deep-seated tumors in radio oncology. Usually, an X-ray computed tomography (CT) scan is used for treatment planning. Since proton therapy is based on the precise knowledge of the stopping power describing the energy loss of protons in the patient tissues, the Hounsfield units of the planning CT have to be converted. This conversion introduces range errors in the treatment plan, which could be reduced, if the stopping power values were extracted directly from an image obtained using protons instead of X-rays. Since protons are affected by multiple Coulomb scattering, reconstruction of the 3D stopping power map results in limited image quality if the curved proton path is not considered. This work presents a substantial code extension of the open-source toolbox TIGRE for proton CT (pCT) image reconstruction based on proton radiographs including a curved proton path estimate. The code extension and the reconstruction algorithms are GPU-based, allowing to achieve reconstruction results within minutes. The performance of the pCT code extension was tested with Monte Carlo simulated data using three phantoms (Catphan® high resolution and sensitometry modules and a CIRS patient phantom). In the simulations, ideal and non-ideal conditions for a pCT setup were assumed. The obtained mean absolute percentage error was found to be below 1% and up to 8 lp/cm could be resolved using an idealized setup. These findings demonstrate that the presented code extension to the TIGRE toolbox offers the possibility for other research groups to use a fast and accurate open-source pCT reconstruction.

A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images.

Even in the era of precision medicine, with various molecular tests based on omics technologies available to improve the diagnosis process, microscopic analysis of images derived from stained tissue sections remains crucial for diagnostic and treatment decisions. Among other cellular features, both nuclei number and shape provide essential diagnostic information. With the advent of digital pathology and emerging computerized methods to analyze the digitized images, nuclei detection, their instance segmentation and classification can be performed automatically. These computerized methods support human experts and allow for faster and more objective image analysis. While methods ranging from conventional image processing techniques to machine learning-based algorithms have been proposed, supervised convolutional neural network (CNN)-based techniques have delivered the best results. In this paper, we propose a CNN-based dual decoder U-Net-based model to perform nuclei instance segmentation in hematoxylin and eosin (H&E)-stained histological images. While the encoder path of the model is developed to perform standard feature extraction, the two decoder heads are designed to predict the foreground and distance maps of all nuclei. The outputs of the two decoder branches are then merged through a watershed algorithm, followed by post-processing refinements to generate the final instance segmentation results. Moreover, to additionally perform nuclei classification, we develop an independent U-Net-based model to classify the nuclei predicted by the dual decoder model. When applied to three publicly available datasets, our method achieves excellent segmentation performance, leading to average panoptic quality values of 50.8%, 51.3%, and 62.1% for the CryoNuSeg, NuInsSeg, and MoNuSAC datasets, respectively. Moreover, our model is the top-ranked method in the MoNuSAC post-challenge leaderboard.

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits.

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.

3D printed patient-specific thorax phantom with realistic heterogenous bone radiopacity using filament printer technology.

Current medical imaging phantoms are usually limited by simplified geometry and radiographic skeletal homogeneity, which confines their usage for image quality assessment. In order to fabricate realistic imaging phantoms, replication of the entire tissue morphology and the associated CT numbers, defined as Hounsfield Unit (HU) is required. 3D printing is a promising technology for the production of medical imaging phantoms with accurate anatomical replication. So far, the majority of the imaging phantoms using 3D printing technologies tried to mimic the average HU of soft tissue human organs. One important aspect of the anthropomorphic imaging phantoms is also the replication of realistic radiodensities for bone tissues. In this study, we used filament printing technology to develop a CT-derived 3D printed thorax phantom with realistic bone-equivalent radiodensity using only one single commercially available filament. The generated thorax phantom geometry closely resembles a patient and includes direct manufacturing of bone structures while creating life-like heterogeneity within bone tissues. A HU analysis as well as a physical dimensional comparison were performed in order to evaluate the density and geometry agreement between the proposed phantom and the corresponding CT data. With the achieved density range (-482 to 968 HU) we could successfully mimic the realistic radiodensity of the bone marrow as well as the cortical bone for the ribs, vertebral body and dorsal vertebral column in the thorax skeleton. In addition, considering the large radiodensity range achieved a full thorax imaging phantom mimicking also soft tissues can become feasible. The physical dimensional comparison using both Extrema Analysis and Collision Detection methods confirmed a mean surface overlap of 90% and a mean volumetric overlap of 84,56% between the patient and phantom model. Furthermore, the reproducibility analyses revealed a good geometry and radiodensity duplicability in 24 printed cylinder replicas. Thus, according to our results, the proposed additively manufactured anthropomorphic thorax phantom has the potential to be efficiently used for validation of imaging- and radiation-based procedures in precision medicine.

First application of the GPU-based software framework TIGRE for proton CT image reconstruction.

In proton therapy, the knowledge of the proton stopping power, i.e. the energy deposition per unit length within human tissue, is essential for accurate treatment planning. One suitable method to directly measure the stopping power is proton computed tomography (pCT). Due to the proton interaction mechanisms in matter, pCT image reconstruction faces some challenges: the unique path of each proton has to be considered separately in the reconstruction process adding complexity to the reconstruction problem. This study shows that the GPU-based open-source software toolkit TIGRE, which was initially intended for X-ray CT reconstruction, can be applied to the pCT image reconstruction problem using a straight line approach for the proton path. This simplified approach allows for reconstructions within seconds. To validate the applicability of TIGRE to pCT, several Monte Carlo simulations modeling a pCT setup with two Catphan® modules as phantoms were performed. Ordered-Subset Simultaneous Algebraic Reconstruction Technique (OS-SART) and Adaptive-Steepest-Descent Projection Onto Convex Sets (ASD-POCS) were used for image reconstruction. Since the accuracy of the approach is limited by the straight line approximation of the proton path, requirements for further improvement of TIGRE for pCT are addressed.

Toward on-the-fly trajectory optimization for C-arm CBCT under strong kinematic constraints.

Cone beam computed tomography (CBCT) has become a vital tool in interventional radiology. Usually, a circular source-detector trajectory is used to acquire a three-dimensional (3D) image. Kinematic constraints due to the patient size or additional medical equipment often cause collisions with the imager while performing a full circular rotation. In a previous study, we developed a framework to design collision-free, patient-specific trajectories for the cases in which circular CBCT is not feasible. Our proposed trajectories included enough information to appropriately reconstruct a particular volume of interest (VOI), but the constraints had to be defined before the intervention. As most collisions are unpredictable, performing an on-the-fly trajectory optimization is desirable. In this study, we propose a search strategy that explores a set of trajectories that cover the whole collision-free area and subsequently performs a search locally in the areas with the highest image quality. Selecting the best trajectories is performed using simulations on a prior diagnostic CT volume which serves as a digital phantom for simulations. In our simulations, the Feature SIMilarity Index (FSIM) is used as the objective function to evaluate the imaging quality provided by different trajectories. We investigated the performance of our methods using three different anatomical targets inside the Alderson-Rando phantom. We used FSIM and Universal Quality Image (UQI) to evaluate the final reconstruction results. Our experiments showed that our proposed trajectories could achieve a comparable image quality in the VOI compared to the standard C-arm circular CBCT. We achieved a relative deviation less than 10% for both FSIM and UQI metrics between the reconstructed images from the optimized trajectories and the standard C-arm CBCT for all three targets. The whole trajectory optimization took approximately three to four minutes.