Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients.

Objective. Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D computed tomography (CT), 4D magnetic resonance imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT.Approach. Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans.Main results. The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for targetD95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on targetD95%up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs.Significance. Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario.

The Apparent Asymmetrical Relationship Between Small Bowel Bacterial Overgrowth, Endotoxemia, and Liver Steatosis and Fibrosis in Cirrhotic and Non-Cirrhotic Patients: A Single-Center Pilot Study.

Introduction: Gut microbiota are a complex ecosystem harboring our intestine. They maintain human body equilibrium, while their derangement, namely, "dysbiosis", has been associated with several gastrointestinal diseases, such as liver steatosis (NAFLD) and liver cirrhosis. Small intestinal bacterial overgrowth (SIBO) is an example of dysbiosis of the upper gastrointestinal (GI) tract. Aim: The aim of this study is to evaluate the relationship between SIBO and levels of endotoxemia and grade of liver steatosis (LS) and liver fibrosis (LF) in hepatologic patients. Materials and Methods: Consecutive outpatients referred to our hepatology clinic were tested for SIBO by the lactulose breath test (LBT) and peripheral blood levels of endotoxemia; LS grading and LF were assessed by abdominal ultrasound and transient elastography, respectively. Results: Fifty-two consecutive patients (17 with alcohol abuse (4.5 ± 0.8 alcohol units per day), 4 with HCV and 2 with HBV infection, 24 of metabolic origin, 2 of autoimmune origin, and 3 with cholangiopathies; mean age 54.7 ± 8.3 years, 31 F, BMI 24.1 ± 1.1 Kg/m2) and 14 healthy volunteers (HV) (mean age 50.1 ± 4.3 years, 9 F, BMI 23.3 ± 1.1 Kg/m2) were enrolled. SIBO prevalence was significantly higher in cirrhotic (LC) vs. non-cirrhotic (LNC) patients and vs. HV (all, p < 0.05), with a significant positive trend according to Child-Pugh status (all, p < 0.05). SIBO prevalence was not correlated with LS stages (all, p = NS). Consensually, endotoxin levels were significantly higher in LC vs. LNC and vs. HV (all, p < 0.05) and significantly correlated with LF in patients with LC, according to Child-Pugh status (all, p < 0.05). Conclusion: This study shows that SIBO prevalence and relative endotoxin blood levels seem to be significantly associated with the grade of LF vs. LS in LC. SIBO is also present under pre-cirrhotic conditions, but its prevalence seems to correlate with liver disease irreversible derangement.

Analysis of tumour microstructure estimation from conventional diffusion MRI and application to skull-base chordoma.

Skull-base chordoma (SBC) is a rare tumour whose molecular and radiological characteristics are still being investigated. In neuro-oncology microstructural imaging techniques, like diffusion-weighted MRI (DW-MRI), have been widely investigated, with the apparent diffusion coefficient (ADC) being one of the most used DW-MRI parameters due to its ease of acquisition and computation. ADC is a potential biomarker without a clear link to microstructure. The aim of this work was to derive microstructural information from conventional ADC, showing its potential for the characterisation of skull-base chordomas. Sixteen patients affected by SBC, who underwent conventional DW-MRI were retrospectively selected. From mono-exponential fits of DW-MRI, ADC maps were estimated using different sets of b-values. DW-MRI signals were simulated from synthetic substrates , which mimic the cellular packing of a tumour tissue with well-defined microstructural features. Starting from a published method, an error-driven procedure was evaluated to improve the estimates of microstructural parameters obtained through the simulated signals. A quantitative description of the tumour microstructure was then obtained from the DW-MRI images. This allowed successfully differentiating patients according to histologically-verified cell proliferation information.Clinical Relevance - The impact on cancer management derives from the expected improvement of radiation treatment quality tailored to a patient-specific non-invasive description of tumour microstructure.

Dosimetric effect of variable rectum and sigmoid colon filling during carbon ion radiotherapy to sacral chordoma.

PURPOSE: Carbon ion radiotherapy (CIRT) is sensitive to anatomical density variations. We examined the dosimetric effect of variable intestinal filling condition during CIRT to ten sacral chordoma patients. METHODS: For each patient, eight virtual computed tomography scans (vCTs) were generated by varying the density distribution within the rectum and the sigmoid in the planning computed tomography (pCT) with a density override approach mimicking a heterogeneous combination of gas and feces. Totally full and empty intestinal preparations were modelled. In addition, five different intestinal filling conditions were modelled by a mixed density pattern derived from two combined and weighted Gaussian distributions simulating gas and feces respectively. Finally, a patient-specific mixing proportion was estimated by evaluating the daily amount of gas detected in the cone beam computed tomography (CBCT). Dose distribution was recalculated on each vCT and dose volume histograms (DVHs) were examined. RESULTS: No target coverage degradation was observed at different vCTs. Rectum and sigma dose degradation ranged respectively between: [-6.7; 21.6]GyE and [-0.7; 15.4]GyE for D50%; [-377.4; 1197.9] and [-95.2; 1027.5] for AUC; [-1.2; 10.7]GyE and [-2.6; 21.5]GyE for D1%. CONCLUSIONS: Variation of intestinal density can greatly influence the penetration depth of charged particle and might compromise dose distribution. In particular cases, with large clinical target volume in very close proximity to rectum and sigmoid colon, it is appropriate to evaluate the amount of gas present in the daily CBCT images even if it is totally included in the reference planning structures.

Development and validation of a new set-up simulator dedicated to ocular proton therapy at CNAO.

An Eye Tracking System (ETS) is used at CNAO for providing a stable and reproducible ocular proton therapy (OPT) set-up, featuring a fixation light (FL) and monitoring stereo-cameras embedded in a rigid case. The aim of this work is to propose an ETS set-up simulation algorithm, that automatically provides the FL positioning in space, according to patient-specific gaze direction and avoiding interferences with patient, beam and collimator. Two configurations are provided: one in the CT room for acquiring images required for treatment planning with the patient lying on a couch, and one related to the treatment room with the patient sitting in front of the beam. Algorithm validation was performed reproducing ETS simulation (CT) and treatment (room) set-up for 30 patients previously treated at CNAO. The positioning accuracy of the device was quantified through a set of 14 control points applied to the ETS case and localizable both in the CT volume and in room X-ray images. Differences between the position of ETS reference points estimated by the algorithm and those measured by imaging systems are reported. The corresponding gaze direction deviation is on average 0.2° polar and 0.3° azimuth for positioning in CT room and 0.1° polar and 0.4° azimuth in the treatment room. The simulation algorithm was embedded in a clinically usable software application, which we assessed as capable of ensuring ETS positioning with an average accuracy of 2 mm in CT room and 1.5 mm in treatment room, corresponding to gaze direction deviations consistently lower than 1°.

Integration of spatial distortion effects in a 4D computational phantom for simulation studies in extra-cranial MRI-guided radiation therapy: Initial results.

PURPOSE: Spatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue-specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image-guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four-dimensional (4D) computational phantom for simulation studies in MRI-guided radiation therapy at extra-cranial sites. METHODS: A geometrical spatial distortion phantom was designed in four modules embedding laser-cut PMMA grids, providing 3520 landmarks in a field of view of (345 × 260 × 480) mm3 . The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra-cranial imaging at 1.5 T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI-guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated. RESULTS: The manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2 mm in the grid planes and 0.5 mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans-axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3 mm in a 1.5 T magnet, whereas static field and susceptibility effects were below 1 mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient-specific measurements was also verified, aiming at increased realism in the simulation. CONCLUSIONS: The implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time-dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra-cranial MRI-guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment.

Inter-fractional monitoring of [Formula: see text]C ions treatments: results from a clinical trial at the CNAO facility.

The high dose conformity and healthy tissue sparing achievable in Particle Therapy when using C ions calls for safety factors in treatment planning, to prevent the tumor under-dosage related to the possible occurrence of inter-fractional morphological changes during a treatment. This limitation could be overcome by a range monitor, still missing in clinical routine, capable of providing on-line feedback. The Dose Profiler (DP) is a detector developed within the INnovative Solution for In-beam Dosimetry in hadronthErapy (INSIDE) collaboration for the monitoring of carbon ion treatments at the CNAO facility (Centro Nazionale di Adroterapia Oncologica) exploiting the detection of charged secondary fragments that escape from the patient. The DP capability to detect inter-fractional changes is demonstrated by comparing the obtained fragment emission maps in different fractions of the treatments enrolled in the first ever clinical trial of such a monitoring system, performed at CNAO. The case of a CNAO patient that underwent a significant morphological change is presented in detail, focusing on the implications that can be drawn for the achievable inter-fractional monitoring DP sensitivity in real clinical conditions. The results have been cross-checked against a simulation study.

A novel framework for spatial normalization of dose distributions in voxel-based analyses of brain irradiation outcomes.

PURPOSE: To devise a novel Spatial Normalization framework for Voxel-based analysis (VBA) in brain radiotherapy. VBAs rely on accurate spatial normalization of different patients' planning CTs on a common coordinate system (CCS). The cerebral anatomy, well characterized by MRI, shows instead poor contrast in CT, resulting in potential inaccuracies in VBAs based on CT alone. METHODS: We analyzed 50 meningioma patients treated with proton-therapy, undergoing planning CT and T1-weighted (T1w) MRI. The spatial normalization pipeline based on MR and CT images consisted in: intra-patient registration of CT to T1w, inter-patient registration of T1w to MNI space chosen as CCS, doses propagation to MNI. The registration quality was compared with that obtained by Statistical Parametric Mapping software (SPM), used as benchmark. To evaluate the accuracy of dose normalization, the dose organ overlap (DOO) score was computed on gray matter, white matter and cerebrospinal fluid before and after normalization. In addition, the trends in the DOOs distribution were investigated by means of cluster analysis. RESULTS: The registration quality was higher for the proposed method compared to SPM (p < 0.001). The DOO scores showed a significant improvement after normalization (p < 0.001). The cluster analysis highlighted 2 clusters, with one of them including the majority of data and exhibiting acceptable DOOs. CONCLUSIONS: Our study presents a robust tool for spatial normalization, specifically tailored for brain dose VBAs. Furthermore, the cluster analysis provides a formal criterion for patient exclusion in case of non-acceptable normalization results. The implemented framework lays the groundwork for future reliable VBAs in brain irradiation studies.

Improving the modelling of susceptibility-induced spatial distortions in MRI-guided extra-cranial radiotherapy.

Magnetic-resonance linear-accelerator (MR-LINAC) systems integrating in-room magnetic-resonance-imaging (MRI) guidance are a currently emerging technology. Such systems address the need to provide frequent imaging at optimal soft-tissue contrast for treatment guidance. However, the use of MRI-guidance in radiotherapy should address imaging-related spatial distortions, which may hinder accurate geometrical characterization of the treatment site. Since spatial encoding relies on well-defined magnetic fields, accurate modeling of the magnetic field alterations due to [Formula: see text]-inhomogeneities, gradient nonlinearities, and susceptibilities is needed. In this work, the modeling of susceptibility induced distortions is considered. Dedicated susceptibility measurements are reported, aiming at extending the characterization of different tissues for MRI-guided extra-cranial radiotherapy applications. A digital 4D anthropomorphic phantom, providing time-resolved anatomical changes due to breathing, is exploited as reference anatomy to quantify spatial distortions due to variations in tissue susceptibility. Sub-millimeter values can be attributed to susceptibility-induced distortions, with maximum values up to 2.3 mm at a gradient strength of 5 mT m-1. Improvements in susceptibility simulation for extra-cranial sites are shown when including specifically the contributions from lung, liver and muscular tissues.

Time-resolved volumetric MRI in MRI-guided radiotherapy: an in silico comparative analysis.

MRI-treatment units enable 2D cine-MRI centred in the tumour for motion detection in radiotherapy, but they lack 3D information due to spatio-temporal limits. To derive time-resolved 3D information, different approaches have been proposed in the literature, but a rigorous comparison among these strategies has not yet been performed. The goal of this study is to quantitatively investigate five published strategies that derive time-resolved volumetric MRI in MRI-guided radiotherapy: Propagation, out-of-plane motion compensation, Fayad model, ROI-based model and Stemkens model. Comparisons were performed using an MRI digital phantom generated with six different patient-derived motion signals and tumour-shapes. An average 4D cycle was generated as well as 2D cine-MRI data with corresponding 3D in-room ground truth. Quantitative analysis was performed by comparing the estimated 3D volume to the ground truth available for each 2D cine-MRI sample. A grouped patient statistical analysis was performed to evaluate the performance of the selected methods, in case of tumour tracking or motion estimation of the whole anatomy. Analyses were also performed based on patient characteristics. Quantitative ranking of the investigated methods highlighted that Propagation and ROI-based model strategies achieved an overall median tumour centre of mass 3D distance from the ground truth of 1.1 mm and 1.3 mm, respectively, and a diaphragm distance below 1.6 mm. Higher errors and variabilities were instead obtained for other methods, which lack the ability to compensate for in-room variations and to account for regional changes. These results were especially evident when further analysing patient characteristics, where errors above 2 mm/5 mm in tumour/diaphragm were found for more irregular breathing patterns in case of out-of-plane motion compensation, Fayad and Stemkens models. These findings suggest the potential of the proposed in silico framework to develop and compare strategies to estimate time-resolved 3DMRI in MRI-guided radiotherapy.

MRI-guidance for motion management in external beam radiotherapy: current status and future challenges.

High precision conformal radiotherapy requires sophisticated imaging techniques to aid in target localisation for planning and treatment, particularly when organ motion due to respiration is involved. X-ray based imaging is a well-established standard for radiotherapy treatments. Over the last few years, the ability of magnetic resonance imaging (MRI) to provide radiation-free images with high-resolution and superb soft tissue contrast has highlighted the potential of this imaging modality for radiotherapy treatment planning and motion management. In addition, these advantageous properties motivated several recent developments towards combined MRI radiation therapy treatment units, enabling in-room MRI-guidance and treatment adaptation. The aim of this review is to provide an overview of the state-of-the-art in MRI-based image guidance for organ motion management in external beam radiotherapy. Methodological aspects of MRI for organ motion management are reviewed and their application in treatment planning, in-room guidance and adaptive radiotherapy described. Finally, a roadmap for an optimal use of MRI-guidance is highlighted and future challenges are discussed.

Monte Carlo simulation tool for online treatment monitoring in hadrontherapy with in-beam PET: A patient study.

Hadrontherapy is a method for treating cancer with very targeted dose distributions and enhanced radiobiological effects. To fully exploit these advantages, in vivo range monitoring systems are required. These devices measure, preferably during the treatment, the secondary radiation generated by the beam-tissue interactions. However, since correlation of the secondary radiation distribution with the dose is not straightforward, Monte Carlo (MC) simulations are very important for treatment quality assessment. The INSIDE project constructed an in-beam PET scanner to detect signals generated by the positron-emitting isotopes resulting from projectile-target fragmentation. In addition, a FLUKA-based simulation tool was developed to predict the corresponding reference PET images using a detailed scanner model. The INSIDE in-beam PET was used to monitor two consecutive proton treatment sessions on a patient at the Italian Center for Oncological Hadrontherapy (CNAO). The reconstructed PET images were updated every 10 s providing a near real-time quality assessment. By half-way through the treatment, the statistics of the measured PET images were already significant enough to be compared with the simulations with average differences in the activity range less than 2.5 mm along the beam direction. Without taking into account any preferential direction, differences within 1 mm were found. In this paper, the INSIDE MC simulation tool is described and the results of the first in vivo agreement evaluation are reported. These results have justified a clinical trial, in which the MC simulation tool will be used on a daily basis to study the compliance tolerances between the measured and simulated PET images.

Thrombocytopenia in high-risk patients with antiphospholipid syndrome.

Essentials The prevalence of thrombocytopenia in patients with antiphospholipid syndrome is not well defined. We studied triple positive patients with antiphospholipid syndrome and its catastrophic variant. Prevalence of thrombocytopenia was 6% and 100% in patients who developed the catastrophic form. In triple positive patients thrombocytopenia is low and platelets drop during the catastrophic form. SUMMARY: Background Thrombocytopenia is the most common non-criteria hematological feature in patients with antiphospholipid syndrome (APS). This condition is more common in patients with catastrophic APS (CAPS). Objectives To evaluate the prevalence of thrombocytopenia in a large series of high-risk patients with APS, and to assess the behavior of the platelet count during CAPS. Methods/Patients This was a cross-sectional study in which we analyzed the platelet counts of a homogeneous group of high-risk APS patients (triple-positive). Six of these patients developed a catastrophic phase of the disease, and the platelet count was recorded before the acute phase, during the acute phase, and at recovery. Results The mean platelet count in 119 high-risk triple-positive patients was 210 × 109 L-1 . With a cut-off value for thrombocytopenia of 100 × 109 L-1 , the prevalence of thrombocytopenia was 6% (seven patients). No difference between primary APS and secondary APS was found. In patients who suffered from CAPS, a significant decrease from the basal count (212 ± 51 × 109 L-1 ) to that at the time of diagnosis (60 ± 33 × 109 L-1 ) was observed. The platelet count became normal again at the time of complete remission (220 ± 57 × 109 L-1 ). A decrease in platelet count always preceded the full clinical picture. Conclusions This study shows that, in high-risk APS patients, the prevalence of thrombocytopenia is low. A decrease in platelet count was observed in all of the patients who developed the catastrophic form of the disease. A decrease in platelet count in high-risk APS patients should be considered a warning signal for disease progression to CAPS.

The role of platelets in antiphospholipid syndrome.

Despite extensive research, the pathogenesis of antiphospholipid syndrome (APS) remains obscure in many aspects. However, it is widely accepted that thrombosis is the result of a hypercoagulable state caused by antibodies directed against ß2-glycoprotein I (ß2-GPI), a protein whose physiological role is unknown. Although underestimated, platelets may be involved in APS and its thrombotic manifestations, especially arterial, in several ways. Thrombocytopenia is the most relevant non-criteria manifestation of APS, possibly caused by direct binding of anti-ß2-GPI antibodies or anti-ß2-GPI-ß2-GPI complexes. On the other hand, platelets may have a key role in APS-related thrombosis due to the presence of multiple receptors that can interact with anti-ß2-GPI antibodies (especially apolipoprotein E receptor 2' (apoER2') and glycoprotein Ibα (GPIbα)) with consequent release of different procoagulant mediators such as thromboxane B2, platelet factor 4 (PF4), and platelet factor 4 variant (CXCL4L1). The aim of this review is to put together evidence on the possible role of platelets in APS and to stimulate further research on the issue.

Hepatocellular carcinoma recurrence in patients with curative resection or ablation: impact of HCV eradication does not depend on the use of interferon.

BACKGROUND: In HCV-infected cirrhotic patients with successfully treated early hepatocellular carcinoma (HCC), the time to HCC recurrence and the effects of sustained viral eradication (SVR) by interferon (IFN)-based or IFN-free regimens on HCC recurrence remain unclear. AIM: To perform an indirect comparison of time to recurrence (TTR) in patients with successfully treated early HCC and active HCV infection with those of patients with SVR by IFN-based and by IFN-free regimens. METHODS: We evaluated 443 patients with HCV-related cirrhosis and Barcelona Clinic Liver Cancer Stage A/0 HCC who had a complete radiological response after curative resection or ablation. Active HCV infection was present in 328, selected from the Italian Liver Cancer group cohort; 58 patients had SVR achieved by IFN-free regimens after HCC cure, and 57 patients had SVR achieved by IFN-based regimens after HCC cure. Individual data of patients in the last two groups were extracted from available publications. RESULTS: TTR by Kaplan-Meier curve was significantly lower in patients with active HCV infection compared with those with SVR both by IFN-free (P = 0.02) and by IFN-based (P < 0.001) treatments. TTR was similar in patients with SVR by IFN-free or by IFN-based (P = 0.49) strategies. CONCLUSION: In HCV-infected, successfully treated patients with early HCC, SVR obtained by IFN-based or IFN-free regimens significantly reduce tumour recurrence without differences related to the anti-viral strategy used.

4D ML reconstruction as a tool for volumetric PET-based treatment verification in ion beam radiotherapy.

PURPOSE: An innovative strategy to improve the sensitivity of positron emission tomography (PET)-based treatment verification in ion beam radiotherapy is proposed. METHODS: Low counting statistics PET images acquired during or shortly after the treatment (Measured PET) and a Monte Carlo estimate of the same PET images derived from the treatment plan (Expected PET) are considered as two frames of a 4D dataset. A 4D maximum likelihood reconstruction strategy was adapted to iteratively estimate the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected PET and Measured PET frames. The outputs generated by the proposed strategy are as follows: (1) an estimate of the Measured PET with an image quality comparable to the Expected PET and (2) an estimate of the motion field mapping Expected PET to Measured PET. The details of the algorithm are presented and the strategy is preliminarily tested on analytically simulated datasets. RESULTS: The algorithm demonstrates (1) robustness against noise, even in the worst conditions where 1.5 × 10(4) true coincidences and a random fraction of 73% are simulated; (2) a proper sensitivity to different kind and grade of mismatches ranging between 1 and 10 mm; (3) robustness against bias due to incorrect washout modeling in the Monte Carlo simulation up to 1/3 of the original signal amplitude; and (4) an ability to describe the mismatch even in presence of complex annihilation distributions such as those induced by two perpendicular superimposed ion fields. CONCLUSIONS: The promising results obtained in this work suggest the applicability of the method as a quantification tool for PET-based treatment verification in ion beam radiotherapy. An extensive assessment of the proposed strategy on real treatment verification data is planned.

Motion prediction in MRI-guided radiotherapy based on interleaved orthogonal cine-MRI.

In-room cine-MRI guidance can provide non-invasive target localization during radiotherapy treatment. However, in order to cope with finite imaging frequency and system latencies between target localization and dose delivery, tumour motion prediction is required. This work proposes a framework for motion prediction dedicated to cine-MRI guidance, aiming at quantifying the geometric uncertainties introduced by this process for both tumour tracking and beam gating. The tumour position, identified through scale invariant features detected in cine-MRI slices, is estimated at high-frequency (25 Hz) using three independent predictors, one for each anatomical coordinate. Linear extrapolation, auto-regressive and support vector machine algorithms are compared against systems that use no prediction or surrogate-based motion estimation. Geometric uncertainties are reported as a function of image acquisition period and system latency. Average results show that the tracking error RMS can be decreased down to a [0.2; 1.2] mm range, for acquisition periods between 250 and 750 ms and system latencies between 50 and 300 ms. Except for the linear extrapolator, tracking and gating prediction errors were, on average, lower than those measured for surrogate-based motion estimation. This finding suggests that cine-MRI guidance, combined with appropriate prediction algorithms, could relevantly decrease geometric uncertainties in motion compensated treatments.

Stopping Criteria for Log-Domain Diffeomorphic Demons Registration: An Experimental Survey for Radiotherapy Application.

A crucial issue in deformable image registration is achieving a robust registration algorithm at a reasonable computational cost. Given the iterative nature of the optimization procedure an algorithm must automatically detect convergence, and stop the iterative process when most appropriate. This paper ranks the performances of three stopping criteria and six stopping value computation strategies for a Log-Domain Demons Deformable registration method simulating both a coarse and a fine registration. The analyzed stopping criteria are: (a) velocity field update magnitude, (b) mean squared error, and (c) harmonic energy. Each stoping condition is formulated so that the user defines a threshold ∊, which quantifies the residual error that is acceptable for the particular problem and calculation strategy. In this work, we did not aim at assigning a value to e, but to give insights in how to evaluate and to set the threshold on a given exit strategy in a very popular registration scheme. Experiments on phantom and patient data demonstrate that comparing the optimization metric minimum over the most recent three iterations with the minimum over the fourth to sixth most recent iterations can be an appropriate algorithm stopping strategy. The harmonic energy was found to provide best trade-off between robustness and speed of convergence for the analyzed registration method at coarse registration, but was outperformed by mean squared error when all the original pixel information is used. This suggests the need of developing mathematically sound new convergence criteria in which both image and vector field information could be used to detect the actual convergence, which could be especially useful when considering multi-resolution registrations. Further work should be also dedicated to study same strategies performances in other deformable registration methods and body districts.

Canine pancreatic islet cell tumours secreting insulin-like growth factor type 2: a rare entity.

Insulin-like growth factor type II (IGF-II) is the main cause of non-islet cell tumour hypoglycaemia (NICTH) and insulin is thought to be the only factor causing hypoglycaemia in insulinomas. However, two case reports of pancreatic neuroendocrine tumours (PNETs) producing IGF-II have been previously published: a human and a canine patient. In this study, we investigated clinical, histopathological, immunohistochemical and ultrastructural features, and biological behaviour of canine pancreatic IGF-II-omas, a subgroup of PNETs that has not been previously characterized. Case records of 58 dogs with confirmed PNETs and hypoglycaemia were reviewed: six patients were affected by IGF-II-omas. Surgery was performed in all cases and two dogs had metastases. Four patients remained alive and in remission at 370, 440, 560 and 890 days post-diagnosis; two died of non-tumour-related causes. IGF-II-omas can be differentiated from insulinomas through hypoinsulinaemia, IGF-II positive and insulin negative immunostaining. The prevalence of this neoplasia is low, accounting for just 6% of PNETs.

Adaptive Mathematical Model of Tumor Response to Radiotherapy Based on CBCT Data.

Mathematical modeling of tumor response to radiotherapy has the potential of enhancing the quality of the treatment plan, which can be even tailored on an individual basis. Lack of extensive in vivo validation has prevented, however, reliable clinical translation of modeling outcomes. Image-guided radiotherapy is a consolidated treatment modality based on computed tomographic (CT) imaging for tumor delineation and volumetric cone beam CT data for periodic checks during treatment. In this study, a macroscopic model of tumor growth and radiation response is proposed, being able to adapt along the treatment course as volumetric tumor data become available. Model parameter learning was based on cone beam CT images in 13 uterine cervical cancer patients, subdivided into three groups (G1, G2, G3) according to tumor type and treatment. Three group-specific parameter sets (PS1, PS2, and PS3) on one general parameter set (PSa) were applied. The corresponding average model fitting errors were 14%, 18%, 13%, and 21%, respectively. The model adaptation testing was performed using volume data of three patients, other than the ones involved in the parameter learning. The extrapolation performance of the general model was improved, while comparable prediction errors were found for the group-specific approach. This suggests that an online parameter tuning can overcome the limitations of a suboptimal patient stratification, which appeared otherwise a critical issue.