Accurate alignment of preoperative planning data with intraoperative conditions is critical for effective surgical navigation. In neurosurgery, this alignment is complicated by brainshift, which occurs when the brain shifts within the skull after the dura mater is opened and cerebrospinal fluid (CSF) is drained. We propose a novel method for intraoperative alignment of MRI-based planning data with photographs taken during awake brain surgery, using a landmark-driven 2D/3D registration technique. Landmarks are interactively selected on both the intraoperative photographs and 3D renderings of the brain's structure and vasculature, derived from T1-weighted MR images with and without contrast enhancement. Registration is performed by minimizing the average distance between corresponding landmark pairs in the two modalities. The precision of this method is evaluated by comparing the positions of physically placed markers on the brain surface through multiple photographs taken from different angles. The landmark projections are reproducible with an average standard deviation of 0.925mm.

Inflammatory white matter brain lesions are a key pathological finding in patients suffering from multiple sclerosis (MS). Image based quantification of different characteristics of these lesions has become an elemental bio-marker in both diagnosis as well as therapy monitoring during treatment of these patients. Whilst it has been shown that the lesion load at a single point in time is only of limited value with respect to explaining clinical symptoms of the patients, a more robust estimate of disease activity can be observed by analyzing the evolution of lesions over time. Here, we propose a system for automated monitoring of temporal lesion evolution in MS. We describe an approach for analysis of lesion correspondence, along with a pipeline for fully automated computation of this model. The pipeline consists of a U-Net based lesion segmentation, a non-linear image registration between multiple studies, computation of temporal lesion correspondences, and finally an analysis module for extracting and visualizing quantitative parameters from the model.

In multiple sclerosis (MS), iron rim lesions (IRLs) are indicators of chronic low-grade inflammation and ongoing tissue destruction. The aim of this study was to assess the relationship of IRLs with clinical measures and magnetic resonance imaging (MRI) markers, in particular brain and cervical cord volume. Clinical and MRI parameters from 102 relapsing MS patients (no relapses for at least 6 months, no contrast-enhancing lesions) were included; follow-up data obtained after 12 months was available in 49 patients. IRLs were identified on susceptibility-weighted images (SWIs). In addition to standard brain and spinal cord MRI parameters, normalised cross-sectional area (nCSA) of the upper cervical cord was calculated. Thirty-eight patients had at least one IRL on SWI MRI. At baseline, patients with IRLs had higher EDSS scores, higher lesion loads (brain and spinal cord), and lower cortical grey matter volumes and a lower nCSA. At follow-up, brain atrophy rates were higher in patients with IRLs. IRLs correlated spatially with T1-hypointense lesions. Relapsing MS patients with IRLs showed more aggressive MRI disease characteristics in both the cross-sectional and longitudinal analyses. • Multiple sclerosis patients with iron rim lesions had higher EDSS scores, higher brain and spinal cord lesion loads, lower cortical grey matter volumes, and a lower normalised cross-sectional area of the upper cervical spinal cord. • Iron rim lesions are a new lesion descriptor obtained from susceptibility-weighted MRI. Our data suggests that further exploration of this lesion characteristic in regard to a poorer prognosis in multiple sclerosis patients is warranted.

BACKGROUND AND PURPOSE:WAKE-UP is a randomized, placebo-controlled trial of thrombolysis in stroke with unknown time of symptom onset using magnetic resonance imaging criteria to determine patients' eligibility. As it is a multicenter trial, homogeneous interpretation of criteria is an important contributor to the trial's success. We describe the investigator image training as well as results of the quality control done by the central image reading board (CIRB). METHODS:Investigators at local centers were given an imaging manual and passed a software-based image training prior to being allowed to judge images in the trial. Throughout the trial, the CIRB gave feedback to recruiting centers in cases of disagreement regarding a patient's randomization. We evaluated the investigators performance in the image training and analyzed results of this quality control from the first 1069 screened patients. Additionally, we obtained feedback from investigators regarding their experiences with the trial. RESULTS:Four-hundred-and-sixty physicians from eight European countries took part in the image training, of whom 436 (95%) successfully completed it. In the trial, agreement rates between the local investigators and members of the CIRB were high for the presence of an acute ischemic lesion (94%, κ = 0.87) as well as for the judgment of infarct extent (93%, κ = 0.87). Agreement for the criterion of DWI-FLAIR mismatch was 74%, κ = 0.60. The majority of investigators reported that the DWI-FLAIR mismatch was the hardest imaging criterion to evaluate. Ninety-one percent of investigators who responded to our survey stated that the image training specifically increased their confidence when assessing the DWI-FLAIR mismatch. CONCLUSIONS:Despite its multicenter design, the WAKE-UP study has demonstrated a high level of homogeneity amongst raters in interpreting the various imaging criteria for patient randomization, including the novel criterion of DWI-FLAIR mismatch. Systematic image training increased the confidence of investigators in applying imaging criteria.

OBJECTIVE:To investigate the longitudinal spinal cord and brain changes in neuromyelitis optica (NMO) and multiple sclerosis (MS) and their associations with disability progression.PATIENTS AND METHODS:We recruited 28 NMO, 22 MS, and 20 healthy controls (HC), who underwent both spinal cord and brain MRI at baseline. Twenty-five NMO and 20 MS completed 1-year follow-up. Baseline spinal cord and brain lesion loads, mean upper cervical cord area (MUCCA), brain, and thalamus volume and their changes during a 1-year follow-up were measured and compared between groups. All the measurements were also compared between progressive and non-progressive groups in NMO and MS.RESULTS:MUCCA decreased significantly during the 1-year follow-up in NMO not in MS. Percentage brain volume changes (PBVC) and thalamus volume changes in MS were significantly higher than NMO. MUCCA changes were significantly different between progressive and non-progressive groups in NMO, while baseline brain lesion volume and PBVC were associated with disability progression in MS. MUCCA changes during 1-year follow-up showed association with clinical disability in NMO.CONCLUSION:Spinal cord atrophy changes were associated with disability progression in NMO, while baseline brain lesion load and whole brain atrophy changes were related to disability progression in MS.KEY POINTS:• Spinal cord atrophy progression was observed in NMO. • Spinal cord atrophy changes were associated with disability progression in NMO. • Brain lesion and atrophy were related to disability progression in MS.

DeepMedic, an open source software library based on a multi-channel multi-resolution 3D convolutional neural network, has recently been made publicly available for brain lesion segmentations. It has already been shown that segmentation tasks on MRI data of patients having traumatic brain injuries, brain tumors, and ischemic stroke lesions can be performed very well. In this paper we describe how it can efficiently be used for the purpose of detecting and segmenting white matter hyperintensity lesions. We examined if it can be applied to single-channel routine 2D FLAIR data. For evaluation, we annotated 197 datasets with different numbers and sizes of white matter hyperintensity lesions. Our experiments have shown that substantial results with respect to the segmentation quality can be achieved. Compared to the original parametrization of the DeepMedic neural network, the timings for training can be drastically reduced if adjusting corresponding training parameters, while at the same time the Dice coefficients remain nearly unchanged. This enables for performing a whole training process within a single day utilizing a NVIDIA GeForce GTX 580 graphics board which makes this library also very interesting for research purposes on low-end GPU hardware.

BACKGROUND AND PURPOSE:Cerebral atrophy has been suggested to be a reliable magnetic resonance imaging (MRI) predictor of subsequent disability in all stages of multiple sclerosis (MS). However, no accepted methodology for routine clinical use exists to date. We sought an easy to apply and fast technique to evaluate cerebral ventricular volume in patients with MS with similar accuracy as a semiautomatic volumetric method.METHODS:The study included 104 patients, 61 diagnosed with MS and 43 with clinically isolated syndrome. In addition, 30 healthy controls were enrolled. Physical disability was assessed with the expanded disability status scale and cognitive disability with the Multiple Sclerosis Inventory Cognition (MUSIC) test. All subjects received standardized 3-dimensional (3D) MR-imaging on a 3 T scanner. Third ventricle volume (3VV) was obtained from 3D T1-weighted images using a semiautomated technique, and compared against planimetric assessment of the width of the third ventricle aligned (a3VW) and unaligned (u3VW) to anatomical landmarks.RESULTS:a3VW was obtained within seconds with excellent intra- and interrater agreement, and outperformed volumetric measurements regarding the differentiation of MS patients from healthy controls. a3VW had the strongest correlations with 3VV (r = .78, P < .001) and showed moderate inverse correlation with MUSIC cognition score (r = -.310, P < .005).CONCLUSIONS:a3VW is a time-effective and robust biomarker that has strong correlations with volumetric measurements and can be established as standard in the MRI quantification of central brain atrophy in patients with early MS.

US guided HIFU/FUS ablation for the therapy of prostate cancer is a clinical established method, while MR guided HIFU/FUS applications for prostate recently started clinical evaluation. Even if MRI examination is an excellent diagnostic tool for prostate cancer, it is a time consuming procedure and not practicable within an MRgFUS therapy session. The aim of our ongoing work is to develop software to support therapy planning and post-therapy follow-up for MRgFUS on localized prostate cancer, based on multi-parametric MR protocols.The clinical workflow of diagnosis, therapy and follow-up of MR guided FUS on prostate cancer was deeply analyzed. Based on this, the image processing workflow was designed and all necessary components, e.g. GUI, viewer, registration tools etc. were defined and implemented. The software bases on MeVisLab with several implemented C++ modules for the image processing tasks.The developed software, called LTC (Local Therapy Control) will register and visualize automatically all images (T1w, T2w, DWI etc.) and ADC or perfusion maps gained from the diagnostic MRI session. This maximum of diagnostic information helps to segment all necessary ROIs, e.g. the tumor, for therapy planning. Final therapy planning will be performed based on these segmentation data in the following MRgFUS therapy session. In addition, the developed software should help to evaluate the therapy success, by synchronization and display of pre-therapeutic, therapy and follow-up image data including the therapy plan and thermal dose information.In this ongoing project, the first stand-alone prototype was completed and will be clinically evaluated.

Background and purposeOur aim was to evaluate the feasibility and potential advantages of dose guided patient positioning based on dose recalculation on scatter corrected cone beam computed tomography (CBCT) image data.Material and methodsA scatter correction approach has been employed to enable dose calculations on CBCT images. A recently proposed tool for interactive multicriterial dose-guided patient positioning which uses interpolation between pre-calculated sample doses has been utilized. The workflow was retrospectively evaluated for two head and neck patients with a total of 39 CBCTs. Dose–volume histogram (DVH) parameters were compared to rigid image registration based isocenter corrections (clinical scenario).ResultsThe accuracy of the dose interpolation was found sufficient, facilitating the implementation of dose guided patient positioning. Compared to the clinical scenario, the mean dose to the parotid glands could be improved for 2 out of 5 fractions for the first patient while other parameters were preserved. For the second patient, the mean coverage over all fractions of the high dose PTV could be improved by 4%. For this patient, coverage improvements had to be traded against organ at risk (OAR) doses within their clinical tolerance limits.ConclusionsDose guided patient positioning using in-room CBCT data is feasible and offers increased control over target coverage and doses to OARs.

Upper cervical cord area (UCCA) atrophy is a prognostic marker for clinical progression in longstanding multiple sclerosis (MS). The objectives of the study were to quantify UCCA atrophy and evaluate its impact in clinically isolated syndrome (CIS) and relapsing–remitting MS (RRMS); to compare converting CIS patients with stable CIS, and to study changes of UCCA and brain white matter (WM) and grey matter (GM) at 2-year follow-up. 110 therapy-naive patients including 53 CIS [6 ± 6 months after symptom onset (SO)] and 57 early RRMS (SO: 12 ± 9 months) underwent sagittal 3D-T1w brain MR (3T). Mean UCCA (C1–C3 level), WM and GM, disability status (EDSS), pyramidal and sensory functional scores, motoric fatigue were assessed at baseline (BL), 12 and 24 months. Volumes were compared with 34 age- and gender-matched healthy controls to assess atrophy. RRMS (78.1 ± 8.7 mm 2 , p = 0.011) and converting CIS (77.3 ± 8.0 mm 2 , p = 0.046) presented with baseline UCCA atrophy, when compared with controls (82.7 ± 5.2 mm 2 ), but not stable CIS (82.6 ± 7.4 mm 2 , p = 0.998). Baseline WM was reduced in RRMS (509.3 ± 25.7 ml vs. controls: 528.4 ± 24.1 ml, p = 0.032). Baseline UCCA correlated negative with muscular weakness and fatigability in all patients and RRMS. EDSS exceeding 3 was associated with lower baseline UCCA. Longitudinal atrophy rates were higher in UCCA than in brain volumes. Early cervical cord atrophy in CIS and RRMS was confirmed and may represent a potential new risk marker for conversion from CIS to MS. Baseline atrophy and atrophy change rates were higher in UCCA compared to WM and GM, suggesting that cervical cord volumetry might become an additional MRI marker relevant in future clinical studies in CIS and early MS.

Increasing evidence suggests a multisystem character of the neuropathology in Huntington’s disease (HD) with different areas of involvement, such as the brainstem, cerebellum or regional cortical areas.1 As recently shown by Muhlau et al , the spinal cord (SC) might be an additional site involved by neurodegenerative processes in HD.2 As such the diverse locations of neuronal degeneration in relevant functional central nervous system (CNS) pathways have contributed to a better understanding of the diversified clinical scene including clinical symptoms such as dysfunctions of two-point discrimination, vibratory sensation and sensation of temperature and pain. However, it has not been studied so far if such degenerative processes can be detected in the preclinical stages of HD, or whether there is a dynamic change of SC atrophy over time. The current study therefore aims at the confirmation of SC atrophy in manifest HD, as well as at the prevalence and longitudinal course SC atrophy in the early premanifest stages of disease. We examined 17 patients with manifest HD (mHD, classified according to the Unified Huntington’s Disease Rating Scale (UHDRS)),3 27 patients with genetically proven premanifest HD (pmHD) and 30 healthy subjects. Subgroups were matched for age and gender. Clinical and paraclinical examinations including UHDRS, tapping task, pegboard tests, CAG repeat length and the disease burden score (DBS) were assessed for all HD subjects. For pmHD, the estimated time to disease onset (years to onset; YTO) was surveyed and a follow-up examination (MRI and clinical assessment, n=23 patients) was performed after approximately 23 months. Refer to online supplementary material for further details. Written informed consent was …

Objectives Cervical cord involvement is common in neuromyelitis optica (NMO) and multiple sclerosis (MS), but its impact on disability in NMO has rarely been studied. Recent publications on NMO examined the periventricular system, areas of high aquaporin-4 expression, but not yet by using ventricle volumetry. Purpose To compare cervical cord atrophy, ventricular widening, and supra- and infratentorial brain measures between NMO and MS, and study their impact on clinical disability. Methods Magnet resonance imaging-based volumetry of upper cervical cord, third and fourth lateral ventricles, grey matter, white matter, brainstem, cerebellum and clinical status of 18 NMO and 20 MS patients, was compared between the groups and with 26 healthy controls. Patterns of ventricular widening relative to healthy controls were inspected by voxel-based morphometry of the cerebrospinal fluid. Results Cervical cord atrophy was similar in NMO and MS (75.2 ± 10.0 mm2, respectively, 76.5 ± 9.5 mm2 vs 84.1 ± 8.6 mm2 in controls).Third ventricle increase in both groups, and specific fourth ventricle widening in MS were detected. Patient groups differed in third to fourth ventricle ratio (P = 0.002). In NMO, white matter correlated inversely with the affected cord segments (P = 0.001) and with cervical cord area (P = 0.043). The disability status was explained by cervical cord area and third ventricle volume (R2=0.524) in NMO, and by grey matter and fourth ventricle volume (R2=0.565) in MS. Conclusion Cervical cord atrophy and third ventricular enlargement are both clinically relevant in NMO. Third and fourth ventricle volumetry shows differences between NMO and MS regarding the involvement of periventricular structures.

A crucial and time-consuming task in adaptive radiotherapy is the propagation of contours from an initial planning CT image to a control image taken during the course of treatment. Precise adaptation of contours for organs at risk, as well as target volumes is necessary in order to calculate an adapted treatment plan. Although several commercially available solutions exist that aim at solving this task, manual editing and correction of such automated mappings is still an inevitable requirement making the overall process tedious and time-consuming in clinical routine. We present a processing pipeline aiming at fast and fully automated propagation of contours between different datasets of an ongoing therapy. The method is based on a non-linear image registration combined with GPU accelerated contour generation. We evaluate our method by calculating Dice similarity coefficients and 3D Hausdorff distances between our results, and manually generated contours which serve as a ground truth. Additionally, we compare our results against contours mapped using a state-of-the-art commercially established contouring software.

Adaptive radiation therapy generates significant amounts of imaging data over the course of a therapy. The full potential of such data with respect to analysis of modifications in radiation or optimization of treatment plans is rarely explored, partly due to the lack of flexibility in existing software systems. Especially in a research context, flexible and modular processing toolchains are a key requirement when analyzing the vast amounts of available data. We present a software toolbox aiming at flexible implementation of specialized workflows for typical image based RT analysis questions. We have implemented a modular research toolbox, which allows for rapid combination of core functional entities such as multimodal, non-linear image registration, contour propagation, dose calculation, dose accumulation or DVH quantification into dynamic workflows, specifically targeted at answering typical research questions in radiation therapy. The system consists of a core platform that manages all therapy related imaging data within a multi-fractional timeline view from which individual workflows can be triggered. Algorithmic modules, such as image registration store their results in a database, providing input for further processing. Third-party algorithmic components can be integrated into the system through a custom command line interface (CLI). This guarantees future flexibility and extensibility for addressing additional user requirements. The provided modules form the basis for a toolbox from which individual workflows can be rapidly configured on demand. During development, regular clinical user and developer workshops were organized, in which clinical and research requirements have been analyzed by usability engineering (UX) experts. Based on this user input a set of basic workflows was identified and implemented as software prototypes on this platform. The system uses modern web technologies, which facilitates access to the software from any network attached client in a hospital environment using standard internet browsers. Two workflows have been implemented using this system so far. First, a non-linear contour-propagation and correction workflow for H&N cases, and second a therapy overview system allowing for quantitative analysis of planned versus delivered dose and temporal volume quantification. The re-contouring tool has been preliminarily evaluated on a set of 10 randomly selected H&N patients with respect to time requirements in comparison to an established atlas based auto-segmentation tool, revealing a significant reduction in required user interaction time. Iterative development in close feedback loops together with computer scientists and clinicians can lead to valuable research tools for radiation therapy. Targeting the RT specific prerequisites of dealing with multiple imaging timepoints in conjunction with flexible algorithmic modules proved to be key elements for such software tools.

Quantitative analysis of the spinal cord from MR images is of significant clinical interest when studying certain neurologic diseases. Especially for multiple sclerosis, a number of studies have analyzed the relation between spinal cord atrophy and clinically monitored progression of the disease. A commonly analyzed parameter in this field is the mean cross-sectional area of the cord, which can also be expressed as the average volume per cm. In this paper, we present a novel approach for precise measurement of the volume, length, and cross-sectional area of the spinal cord from T1-weighted MR images. It is computationally fast, with a low effort of required user interaction. It is based on a semi-automated pre-segmentation of a sub-section of the spinal cord, followed by an automated Gaussian mixture-model fit for volume calculation. Additionally, the centerline of the cord is extracted, which allows for calculation of the mean cross-sectional area of the measured section. We evaluate the accuracy of our method with respect to scan/re-scan reproducibility as well as intra- and inter-rater agreement. We achieved a mean coefficient of variation of 0.62% over repeated MR acquisitions, mean CoV of 0.39% for intra-rater comparison, and a mean CoV of 0.28% for inter-rater comparison by five different observers. These results prove a high sensitivity to detect even small changes in atrophy, as it could typically be observed over the temporal progression of MS