Principles and Applications of Dual-Layer Spectral CT in Gastrointestinal Imaging.

The advance in technology allows for the development of different CT scanners in the field of dual-energy computed tomography (DECT). In particular, a recently developed detector-based technology can collect data from different energy levels, thanks to its layers. The use of this system is suited for material decomposition with perfect spatial and temporal registration. Thanks to post-processing techniques, these scanners can generate conventional, material decomposition (including virtual non-contrast (VNC), iodine maps, Z-effective imaging, and uric acid pair images) and virtual monoenergetic images (VMIs). In recent years, different studies have been published regarding the use of DECT in clinical practice. On these bases, considering that different papers have been published using the DECT technology, a review regarding its clinical application can be useful. We focused on the usefulness of DECT technology in gastrointestinal imaging, where DECT plays an important role.

Potential and pitfalls of 1.5T MRI imaging for target volume definition in ocular proton therapy.

INTRODUCTION: Ocular proton therapy (OPT) for the treatment of uveal melanoma has a long and remarkably successful history. This is despite that, for the majority of patients treated, the definition of the eye anatomy is based on a simplified geometrical model embedded in the treatment planning system EyePlan. In this study, differences in anatomical and tumor structures from EyePlan, and those based on 1.5T magnetic resonance imaging (MRI) are assessed. MATERIALS AND METHODS: Thirty-three uveal melanoma patients treated with OPT at our institution were subject to eye MRI. The target volumes were manually delineated on those images by two radiation oncologists. The resulting volumes were geometrically compared to the clinical standard. In addition, the dosimetric impact of using different models for treatment planning were evaluated. RESULTS: Two patients (6%) presented lesions too small to be visible on MRI. Target volumes identified on MRI scans were on average smaller than EyePlan with discrepancies arising mostly from the definition of the tumor base. Clip-to-tumor base distances measured on MRI models exhibited higher discrepancy to ophthalmological measurements than EyePlan. For 53% of cases, treatment plans optimized for lesions identified on MRI only, failed to achieve sufficient target coverage for EyePlan volumes. DISCUSSION: The analysis has shown that 1.5T MRI might be more susceptible to misses of flat tumor extension of the clinical target volume than the current clinical standard. Thus, a proper integration of ancillary imaging modalities, leading to a better characterization of the full lesion, is required.

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia.

OBJECTIVES: To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes. METHODS: All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated. RESULTS: A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05). CONCLUSION: The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation. KEY POINTS: • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Anthropomorphic phantom for deformable lung and liver CT and MR imaging for radiotherapy.

In this study, a functioning and ventilated anthropomorphic phantom was further enhanced for the purpose of CT and MR imaging of the lung and liver. A deformable lung, including respiratory tract was 3D printed. Within the lung's inner structures is a solid region shaped from a patient's lung tumour and six nitro-glycerine capsules as reference landmarks. The full internal mesh was coated, and the tumour filled, with polyorganosiloxane based gel. A moulded liver was created with an external casing of silicon filled with polyorganosiloxane gel and flexible plastic internal structures. The liver, fitted to the inferior portion of the right lung, moves along with the lung's ventilation. In the contralateral side, a cavity is designed to host a dosimeter, whose motion is correlated to the lung pressure. A 4DCT of the phantom was performed along with static and 4D T1 weighted MR images. The CT Hounsfield units (HU) for the flexible 3D printed material were -600-100 HU (lung and liver structures), for the polyorganosiloxane gel 30-120 HU (lung coating and liver filling) and for the silicon 650-800 HU (liver casing). The MR image intensity units were 0-40, 210-280 and 80-130, respectively. The maximum range of motion in the 4D imaging for the superior lung was 1-3.5 mm and 3.5-8 mm in the inferior portion. The liver motion was 5.5-8.0 mm at the tip and 5.7-10.0 mm at the dome. No measurable drift in motion was observed over a 2 h session and motion was reproducible over three different sessions for sin2(t), sin4(t) and a patient-like breathing curve with the interquartile range of amplitudes for all breathing cycles within 0.5 mm. The addition of features within the lung and of a deformable liver will allow the phantom to be used for imaging studies such as validation of 4DMRI and pseudo CT methods.

Personalized Anatomic Eye Model From T1-Weighted Volume Interpolated Gradient Echo Magnetic Resonance Imaging of Patients With Uveal Melanoma.

PURPOSE: We present a 3-dimensional patient-specific eye model from magnetic resonance imaging (MRI) for proton therapy treatment planning of uveal melanoma (UM). During MRI acquisition of UM patients, the point fixation can be difficult and, together with physiological blinking, can introduce motion artifacts in the images, thus challenging the model creation. Furthermore, the unclear boundary of the small objects (eg, lens, optic nerve) near the muscle or of the tumors with hemorrhage and tantalum clips can limit model accuracy. METHODS AND MATERIALS: A dataset of 37 subjects, including 30 healthy eyes of volunteers and 7 eyes of UM patients, was investigated. In our previous work, active shape model was successfully applied to retinoblastoma eye segmentation in T1-weighted 3T MRI. Here, we evaluate this method in a more challenging setting, based on 1.5T MRI acquisition and different datasets of awake adult eyes with UM. The lens and cornea together with the sclera, vitreous humor, and optic nerve were automatically segmented and validated against manual delineations of a senior ocular radiation oncologist, in terms of the Dice similarity coefficient and Hausdorff distance. RESULTS: Leave-one-out cross validation (mixing both volunteers and UM patients) yielded median Dice similarity coefficient values (respective of Hausdorff distance) of 94.5% (1.64 mm) for the sclera, 92.2% (1.73 mm) for the vitreous humor, 88.3% (1.09 mm) for the lens, and 81.9% (1.86 mm) for the optic nerve. The average computation time for an eye was 10 seconds. CONCLUSIONS: To our knowledge, our work is the first attempt to automatically segment adult eyes, including patients with UM. Our results show that automated active shape model segmentation can succeed in the presence of motion, tumors, and tantalum clips. These results are promising for inclusion in clinical practice.

Practice patterns of image guided particle therapy in Europe: A 2016 survey of the European Particle Therapy Network (EPTN).

BACKGROUND AND PURPOSE: Image guidance is critical in achieving accurate and precise radiation delivery in particle therapy, even more than in photon therapy. However, equipment, quality assurance procedures and clinical workflows for image-guided particle therapy (IGPT) may vary substantially between centres due to a lack of standardization. A survey was conducted to evaluate the current practice of IGPT in European particle therapy centres. MATERIAL AND METHODS: In 2016, a questionnaire was distributed among 19 particle therapy centres in 12 European countries. The questionnaire consisted of 30 open and 37 closed questions related to image guidance in the general clinical workflow, for moving targets, current research activities and future perspectives of IGPT. RESULTS: All centres completed the questionnaire. The IGPT methods used by the 10 treating centres varied substantially. The 9 non-treating centres were in the process to introduce IGPT. Most centres have developed their own IGPT strategies, being tightly connected to their specific technical implementation and dose delivery methods. CONCLUSIONS: Insight into the current clinical practice of IGPT in European particle therapy centres was obtained. A variety in IGPT practices and procedures was confirmed, which underlines the need for harmonisation of practice parameters and consensus guidelines.

Monitoring of breathing motion in image-guided PBS proton therapy: comparative analysis of optical and electromagnetic technologies.

BACKGROUND: Motion monitoring is essential when treating non-static tumours with pencil beam scanned protons. 4D medical imaging typically relies on the detected body surface displacement, considered as a surrogate of the patient's anatomical changes, a concept similarly applied by most motion mitigation techniques. In this study, we investigate benefits and pitfalls of optical and electromagnetic tracking, key technologies for non-invasive surface motion monitoring, in the specific environment of image-guided, gantry-based proton therapy. METHODS: Polaris SPECTRA optical tracking system and the Aurora V3 electromagnetic tracking system from Northern Digital Inc. (NDI, Waterloo, CA) have been compared both technically, by measuring tracking errors and system latencies under laboratory conditions, and clinically, by assessing their practicalities and sensitivities when used with imaging devices and PBS treatment gantries. Additionally, we investigated the impact of using different surrogate signals, from different systems, on the reconstructed 4D CT images. RESULTS: Even though in controlled laboratory conditions both technologies allow for the localization of static fiducials with sub-millimetre jitter and low latency (31.6 ± 1 msec worst case), significant dynamic and environmental distortions limit the potential of the electromagnetic approach in a clinical setting. The measurement error in case of close proximity to a CT scanner is up to 10.5 mm and precludes its use for the monitoring of respiratory motion during 4DCT acquisitions. Similarly, the motion of the treatment gantry distorts up to 22 mm the tracking result. CONCLUSIONS: Despite the line of sight requirement, the optical solution offers the best potential, being the most robust against environmental factors and providing the highest spatial accuracy. The significant difference in the temporal location of the reconstructed phase points is used to speculate on the need to apply the same monitoring system for imaging and treatment to ensure the consistency of detected phases.

Respiratory motion-management in stereotactic body radiation therapy for lung cancer - A dosimetric comparison in an anthropomorphic lung phantom (LuCa).

BACKGROUND AND PURPOSE: The objective of this study was to compare the latest respiratory motion-management strategies, namely the internal-target-volume (ITV) concept, the mid-ventilation (MidV) principle, respiratory gating and dynamic couch tracking. MATERIALS AND METHODS: An anthropomorphic, deformable and dynamic lung phantom was used for the dosimetric validation of these techniques. Stereotactic treatments were adapted to match the techniques and five distinct respiration patterns, and delivered to the phantom while radiographic film measurements were taken inside the tumor. To report on tumor coverage, these dose distributions were used to calculate mean doses (Dmean), changes in homogeneity indices (ΔH2-98), gamma agreement, and areas covered by the planned minimum dose (A>Dmin). RESULTS: All techniques achieved good tumor coverage (A>Dmin>99.0%) and minor changes in Dmean (±3.2%). Gating and tracking strategies showed superior results in gamma agreement and ΔH2-98 compared to ITV and MidV concepts, which seem to be more influenced by the interplay and the gradient effect. For lung, heart and spinal cord, significant dose differences between the four techniques were found (p<0.05), with lowest doses for gating and tracking strategies. CONCLUSION: Active motion-management techniques, such as gating or tracking, showed superior tumor dose coverage and better organ dose sparing than the passive techniques based on tumor margins.

Four-Dimensional Dose Reconstruction for Scanned Proton Therapy Using Liver 4DCT-MRI.

PURPOSE: Four-dimensional computed tomography-magnetic resonance imaging (4DCT-MRI) is an image-processing technique for simulating many 4DCT data sets from a static reference CT and motions extracted from 4DMRI studies performed using either volunteers or patients. In this work, different motion extraction approaches were tested using 6 liver cases, and a detailed comparison between 4DCT-MRI and 4DCT was performed. METHODS AND MATERIALS: 4DCT-MRI has been generated using 2 approaches. The first approach used motion extracted from 4DMRI as being "most similar" to that of 4DCT from the same patient (subject-specific), and the second approach used the most similar motion obtained from a motion library derived from 4DMRI liver studies of 13 healthy volunteers (population-based). The resulting 4DCT-MRI and 4DCTs were compared using scanned proton 4D dose calculations (4DDC). RESULTS: Dosimetric analysis showed that 93% ± 8% of points inside the clinical target volume (CTV) agreed between 4DCT and subject-specific 4DCT-MRI (gamma analysis: 3%/3 mm). The population-based approach however showed lower dosimetric agreement with only 79% ± 14% points in the CTV reaching the 3%/3 mm criteria. CONCLUSIONS: 4D CT-MRI extends the capabilities of motion modeling for dose calculations by accounting for realistic and variable motion patterns, which can be directly employed in clinical research studies. We have found that the subject-specific liver modeling appears more accurate than the population-based approach. The former is particularly interesting for clinical applications, such as improved target delineation and 4D dose reconstruction for patient-specific QA to allow for inter- and/or intra-fractional plan corrections.

Vision 20/20: perspectives on automated image segmentation for radiotherapy.

Due to rapid advances in radiation therapy (RT), especially image guidance and treatment adaptation, a fast and accurate segmentation of medical images is a very important part of the treatment. Manual delineation of target volumes and organs at risk is still the standard routine for most clinics, even though it is time consuming and prone to intra- and interobserver variations. Automated segmentation methods seek to reduce delineation workload and unify the organ boundary definition. In this paper, the authors review the current autosegmentation methods particularly relevant for applications in RT. The authors outline the methods' strengths and limitations and propose strategies that could lead to wider acceptance of autosegmentation in routine clinical practice. The authors conclude that currently, autosegmentation technology in RT planning is an efficient tool for the clinicians to provide them with a good starting point for review and adjustment. Modern hardware platforms including GPUs allow most of the autosegmentation tasks to be done in a range of a few minutes. In the nearest future, improvements in CT-based autosegmentation tools will be achieved through standardization of imaging and contouring protocols. In the longer term, the authors expect a wider use of multimodality approaches and better understanding of correlation of imaging with biology and pathology.

Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours.

PURPOSE: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. METHODS: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. RESULTS: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. CONCLUSIONS: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.

Quantification of organ motion based on an adaptive image-based scale invariant feature method.

PURPOSE: The availability of corresponding landmarks in IGRT image series allows quantifying the inter and intrafractional motion of internal organs. In this study, an approach for the automatic localization of anatomical landmarks is presented, with the aim of describing the nonrigid motion of anatomo-pathological structures in radiotherapy treatments according to local image contrast. METHODS: An adaptive scale invariant feature transform (SIFT) was developed from the integration of a standard 3D SIFT approach with a local image-based contrast definition. The robustness and invariance of the proposed method to shape-preserving and deformable transforms were analyzed in a CT phantom study. The application of contrast transforms to the phantom images was also tested, in order to verify the variation of the local adaptive measure in relation to the modification of image contrast. The method was also applied to a lung 4D CT dataset, relying on manual feature identification by an expert user as ground truth. The 3D residual distance between matches obtained in adaptive-SIFT was then computed to verify the internal motion quantification with respect to the expert user. Extracted corresponding features in the lungs were used as regularization landmarks in a multistage deformable image registration (DIR) mapping the inhale vs exhale phase. The residual distances between the warped manual landmarks and their reference position in the inhale phase were evaluated, in order to provide a quantitative indication of the registration performed with the three different point sets. RESULTS: The phantom study confirmed the method invariance and robustness properties to shape-preserving and deformable transforms, showing residual matching errors below the voxel dimension. The adapted SIFT algorithm on the 4D CT dataset provided automated and accurate motion detection of peak to peak breathing motion. The proposed method resulted in reduced residual errors with respect to standard SIFT, providing a motion description comparable to expert manual identification, as confirmed by DIR. CONCLUSIONS: The application of the method to a 4D lung CT patient dataset demonstrated adaptive-SIFT potential as an automatic tool to detect landmarks for DIR regularization and internal motion quantification. Future works should include the optimization of the computational cost and the application of the method to other anatomical sites and image modalities.

Scale invariant feature transform in adaptive radiation therapy: a tool for deformable image registration assessment and re-planning indication.

Adaptive radiation therapy (ART) aims at compensating for anatomic and pathological changes to improve delivery along a treatment fraction sequence. Current ART protocols require time-consuming manual updating of all volumes of interest on the images acquired during treatment. Deformable image registration (DIR) and contour propagation stand as a state of the ART method to automate the process, but the lack of DIR quality control methods hinder an introduction into clinical practice. We investigated the scale invariant feature transform (SIFT) method as a quantitative automated tool (1) for DIR evaluation and (2) for re-planning decision-making in the framework of ART treatments. As a preliminary test, SIFT invariance properties at shape-preserving and deformable transformations were studied on a computational phantom, granting residual matching errors below the voxel dimension. Then a clinical dataset composed of 19 head and neck ART patients was used to quantify the performance in ART treatments. For the goal (1) results demonstrated SIFT potential as an operator-independent DIR quality assessment metric. We measured DIR group systematic residual errors up to 0.66 mm against 1.35 mm provided by rigid registration. The group systematic errors of both bony and all other structures were also analyzed, attesting the presence of anatomical deformations. The correct automated identification of 18 patients who might benefit from ART out of the total 22 cases using SIFT demonstrated its capabilities toward goal (2) achievement.

Automatic segmentation and online virtualCT in head-and-neck adaptive radiation therapy.

PURPOSE: The purpose of this work was to develop and validate an efficient and automatic strategy to generate online virtual computed tomography (CT) scans for adaptive radiation therapy (ART) in head-and-neck (HN) cancer treatment. METHOD: We retrospectively analyzed 20 patients, treated with intensity modulated radiation therapy (IMRT), for an HN malignancy. Different anatomical structures were considered: mandible, parotid glands, and nodal gross tumor volume (nGTV). We generated 28 virtualCT scans by means of nonrigid registration of simulation computed tomography (CTsim) and cone beam CT images (CBCTs), acquired for patient setup. We validated our approach by considering the real replanning CT (CTrepl) as ground truth. We computed the Dice coefficient (DSC), center of mass (COM) distance, and root mean square error (RMSE) between correspondent points located on the automatically segmented structures on CBCT and virtualCT. RESULTS: Residual deformation between CTrepl and CBCT was below one voxel. Median DSC was around 0.8 for mandible and parotid glands, but only 0.55 for nGTV, because of the fairly homogeneous surrounding soft tissues and of its small volume. Median COM distance and RMSE were comparable with image resolution. No significant correlation between RMSE and initial or final deformation was found. CONCLUSION: The analysis provides evidence that deformable image registration may contribute significantly in reducing the need of full CT-based replanning in HN radiation therapy by supporting swift and objective decision-making in clinical practice. Further work is needed to strengthen algorithm potential in nGTV localization.

Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge.

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

Uncertainties in lung motion prediction relying on external surrogate: a 4DCT study in regular vs. irregular breathers.

This paper examines the uncertainty in estimating lung motion from external surrogates for lung cancer patients with regular and irregular breathing. 4DCT data sets were analyzed using a template matching algorithm to track the spatial movement of vessel bifurcations in 12 patients. The detected internal movement of features in 3D was retrospectively synchronized with the RPM surrogate signal, and the correlation index R(2) and the prediction error were computed. Patients were classified into two groups depending on the presence or not of irregularities in their breathing pattern. Peak-to-peak values of feature motion in the SI direction ranged from 0.8 mm (upper lung) to 25.3 mm (lower lung). Some patients exhibited large motion also in the latero-lateral (10.6 mm) and anterior-posterior (12.2 mm) directions. The median +/- quartile of R(2) in SI direction was 0.89 +/- 0.09. Prediction error values were up to 4.2 mm (95th percentile) with a maximum value of 4.9 mm. Statistical differences between regular and irregular breathers were found for R(2), while prediction error depended only on the range of motion. This study is relevant for image guided radiotherapy methods that rely on external surrogates to monitor motion.