A community-endorsed open-source lexicon for contrast agent-based perfusion MRI: A consensus guidelines report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI)

This manuscript describes the ISMRM OSIPI (Open Science Initiative for Perfusion Imaging) lexicon for dynamic contrast-enhanced and dynamic susceptibility-contrast MRI. The lexicon was developed by Taskforce 4.2 of OSIPI to provide standardized definitions of commonly used quantities, models, and analysis processes with the aim of reducing reporting variability. The taskforce was established in February 2020 and consists of medical physicists, engineers, clinicians, data and computer scientists, and DICOM (Digital Imaging and Communications in Medicine) standard experts. Members of the taskforce collaborated via a slack channel and quarterly virtual meetings. Members participated by defining lexicon items and reporting formats that were reviewed by at least two other members of the taskforce. Version 1.0.0 of the lexicon was subject to open review from the wider perfusion imaging community between January and March 2022, and endorsed by the Perfusion Study Group of the ISMRM in the summer of 2022. The initial scope of the lexicon was set by the taskforce and defined such that it contained a basic set of quantities, processes, and models to enable users to report an end-to-end analysis pipeline including kinetic model fitting. We also provide guidance on how to easily incorporate lexicon items and definitions into free-text descriptions (e.g., in manuscripts and other documentation) and introduce an XML-based pipeline encoding format to encode analyses using lexicon definitions in standardized and extensible machine-readable code. The lexicon is designed to be open-source and extendable, enabling ongoing expansion of its content. We hope that widespread adoption of lexicon terminology and reporting formats described herein will increase reproducibility within the field.

Addressing image misalignments in multi-parametric prostate MRI for enhanced computer-aided diagnosis of prostate cancer

Prostate cancer (PCa) diagnosis on multi-parametric magnetic resonance images (MRI) requires radiologists with a high level of expertise. Misalignments between the MRI sequences can be caused by patient movement, elastic soft-tissue deformations, and imaging artifacts. They further increase the complexity of the task prompting radiologists to interpret the images. Recently, computer-aided diagnosis (CAD) tools have demonstrated potential for PCa diagnosis typically relying on complex co-registration of the input modalities. However, there is no consensus among research groups on whether CAD systems profit from using registration. Furthermore, alternative strategies to handle multi-modal misalignments have not been explored so far. Our study introduces and compares different strategies to cope with image misalignments and evaluates them regarding to their direct effect on diagnostic accuracy of PCa. In addition to established registration algorithms, we propose 'misalignment augmentation' as a concept to increase CAD robustness. As the results demonstrate, misalignment augmentations can not only compensate for a complete lack of registration, but if used in conjunction with registration, also improve the overall performance on an independent test set.

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.

Statistical breathing curve sampling to quantify interplay effects of moving lung tumors in a 4D Monte Carlo dose calculation framework

PURPOSE

The interplay between respiratory tumor motion and dose application by intensity modulated radiotherapy (IMRT) techniques can potentially lead to undesirable and non-intuitive deviations from the planned dose distribution. We developed a 4D Monte Carlo (MC) dose recalculation framework featuring statistical breathing curve sampling, to precisely simulate the dose distribution for moving target volumes aiming at a comprehensive assessment of interplay effects.

METHODS

We implemented a dose accumulation tool that enables dose recalculations of arbitrary breathing curves including the actual breathing curve of the patient. This MC dose recalculation framework is based on linac log-files, facilitating a high temporal resolution up to 0.1 s. By statistical analysis of 128 different breathing curves, interplay susceptibility of different treatment parameters was evaluated for an exemplary patient case. To facilitate prospective clinical application in the treatment planning stage, in which patient breathing curves or linac log-files are not available, we derived a log-file free version with breathing curves generated by a random walk approach. Interplay was quantified by standard deviations σ in D_5%, D_50% and D_95%.

RESULTS

Interplay induced dose deviations for single fractions were observed and evaluated for IMRT and volumetric arc therapy (σ_D95% up to 1.3 %) showing a decrease with higher fraction doses and an increase with higher MU rates. Interplay effects for conformal treatment techniques were negligible (σ<0.1%). The log-file free version and the random walk generated breathing curves yielded similar results (deviations in σ< 0.1 %) and can be used as substitutes for interplay assessment.

CONCLUSION

It is feasible to combine statistically sampled breathing curves with MC dose calculations. The universality of the presented framework allows comprehensive assessment of interplay effects in retrospective and prospective clinically relevant scenarios.

Joint Imaging Platform for Federated Clinical Data Analytics

PURPOSE

Image analysis is one of the most promising applications of artificial intelligence (AI) in health care, potentially improving prediction, diagnosis, and treatment of diseases. Although scientific advances in this area critically depend on the accessibility of large-volume and high-quality data, sharing data between institutions faces various ethical and legal constraints as well as organizational and technical obstacles.

METHODS

The Joint Imaging Platform (JIP) of the German Cancer Consortium (DKTK) addresses these issues by providing federated data analysis technology in a secure and compliant way. Using the JIP, medical image data remain in the originator institutions, but analysis and AI algorithms are shared and jointly used. Common standards and interfaces to local systems ensure permanent data sovereignty of participating institutions.

RESULTS

The JIP is established in the radiology and nuclear medicine departments of 10 university hospitals in Germany (DKTK partner sites). In multiple complementary use cases, we show that the platform fulfills all relevant requirements to serve as a foundation for multicenter medical imaging trials and research on large cohorts, including the harmonization and integration of data, interactive analysis, automatic analysis, federated machine learning, and extensibility and maintenance processes, which are elementary for the sustainability of such a platform.

CONCLUSION

The results demonstrate the feasibility of using the JIP as a federated data analytics platform in heterogeneous clinical information technology and software landscapes, solving an important bottleneck for the application of AI to large-scale clinical imaging data.

Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions

Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.

IDH-wildtype glioblastomas and grade III/IV IDH-mutant gliomas show elevated tracer uptake in fibroblast activation protein-specific PET/CT

PURPOSE

Targeting fibroblast activation protein (FAP) is a new diagnostic approach allowing the visualization of tumor stroma. Here, we applied FAP-specific PET imaging to gliomas. We analyzed the target affinity and specificity of two FAP ligands (FAPI-02 and FAPI-04) in vitro, and the pharmacokinetics and biodistribution in mice in vivo. Clinically, we used ⁶⁸Ga-labeled FAPI-02/04 for PET imaging in 18 glioma patients (five IDH-mutant gliomas, 13 IDH-wildtype glioblastomas).

METHODS

For binding studies with ¹⁷⁷Lu-radiolabeled FAPI-02/04, we used the glioblastoma cell line U87MG, FAP-transfected fibrosarcoma cells, and CD26-transfected human embryonic kidney cells. For pharmacokinetic and biodistribution studies, U87MG-xenografted mice were injected with ⁶⁸Ga-labeled compounds followed by small-animal PET imaging and ¹⁷⁷Lu-labeled FAPI-02/04, respectively. Clinical PET/CT scans were performed 30 min post intravenous administration of ⁶⁸Ga-FAPI-02/04. PET and MRI scans were co-registrated. Immunohistochemistry was done on 14 gliomas using a FAP-specific antibody.

RESULTS

FAPI-02 and FAPI-04 showed high binding specificity to FAP. FAPI-04 demonstrated higher tumor accumulation and delayed elimination compared with FAPI-02 in preclinical studies. IDH-wildtype glioblastomas and grade III/IV, but not grade II, IDH-mutant gliomas showed elevated tracer uptake. In glioblastomas, we observed spots with increased uptake in projection on contrast-enhancing areas. Immunohistochemistry showed FAP-positive cells with mainly elongated cell bodies and perivascular FAP-positive cells in glioblastomas and an anaplastic IDH-mutant astrocytoma.

CONCLUSIONS

Using FAP-specific PET imaging, increased tracer uptake in IDH-wildtype glioblastomas and high-grade IDH-mutant astrocytomas, but not in diffuse astrocytomas, may allow non-invasive distinction between low-grade IDH-mutant and high-grade gliomas. Therefore, FAP-specific imaging in gliomas may be useful for follow-up studies although further clinical evaluation is required.

Comparison of planned dose on different CT image sets to four-dimensional Monte Carlo dose recalculation using the patient's actual breathing trace for lung stereotactic body radiation therapy

PURPOSE

The need for four-dimensional (4D) treatment planning becomes indispensable when it comes to radiation therapy for moving tumors in the thoracic and abdominal regions. The primary purpose of this study is to combine the actual breathing trace during each individual treatment fraction with the Linac's log file information and Monte Carlo 4D dose calculations. We investigated this workflow on multiple computed tomography (CT) datasets in a clinical environment for stereotactic body radiation therapy (SBRT) treatment planning.

METHODS

We have developed a workflow, which allows us to recalculate absorbed dose to a 4DCT dataset using Monte Carlo calculation methods and accumulate all 4D doses in order to compare them to the planned dose using the Linac's log file, a 4DCT dataset, and the patient's actual breathing curve for each individual fraction. For five lung patients, three-dimensional-conformal radiation therapy (3D-CRT) and volumetric modulated arc treatment (VMAT) treatment plans were generated on four different CT image datasets: a native free-breathing 3DCT, an average intensity projection (AIP) and a maximum intensity projection (MIP) CT both obtained from a 4DCT, and a 3DCT with density overrides based on the 3DCT (DO). The Monte Carlo 4D dose has been calculated on each 4DCT phase using the Linac's log file and the patient's breathing trace as a surrogate for tumor motion and dose was accumulated to the gross tumor volume (GTV) at the 50% breathing phase (end of exhale) using deformable image registration.

RESULTS

Δ D 98 % and Δ D 2 % between 4D dose and planned dose differed largely for 3DCT-based planning and also for DO in three patients. Least dose differences between planned and recalculated dose have been found for AIP and MIP treatment planning which both tend to be superior to DO, but the results indicate a dependency on the breathing variability, tumor motion, and size. An interplay effect has not been observed in the small patient cohort.

CONCLUSIONS

We have developed a workflow which, to our best knowledge, is the first incorporation of the patient breathing trace over the course of all individual treatment fractions with the Linac's log file information and 4D Monte Carlo recalculations of the actual treated dose. Due to the small patient cohort, no clear recommendation on which CT can be used for SBRT treatment planning can be given, but the developed workflow, after adaption for clinical use, could be used to enhance a priori 4D Monte Carlo treatment planning in the future and help with the decision on which CT dataset treatment planning should be carried out.

MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging - design, implementation and application on the example of DCE-MRI

Background

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow.

Results

We present a framework for medical image fitting tasks that is included in the Medical Imaging Interaction Toolkit MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth.

Conclusions

Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.

Feasibility and robustness of dynamic 18F-FET PET based tracer kinetic models applied to patients with recurrent high-grade glioma prior to carbon ion irradiation

The aim of this study was to analyze the robustness and diagnostic value of different compartment models for dynamic 18F-FET PET in recurrent high-grade glioma (HGG). Dynamic 18F-FET PET data of patients with recurrent WHO grade III (n:7) and WHO grade IV (n: 9) tumors undergoing re-irradiation with carbon ions were analyzed by voxelwise fitting of the time-activity curves with a simplified and an extended one-tissue compartment model (1TCM) and a two-tissue compartment model (2TCM), respectively. A simulation study was conducted to assess robustness and precision of the 2TCM. Parameter maps showed enhanced detail on tumor substructure. Neglecting the blood volume VB in the 1TCM yields insufficient results. Parameter K1 from both 1TCM and 2TCM showed correlation with overall patient survival after carbon ion irradiation (p = 0.043 and 0.036, respectively). The 2TCM yields realistic estimates for tumor blood volume, which was found to be significantly higher in WHO IV compared to WHO III (p = 0.031). Simulations on the 2TCM showed that K1 yields good accuracy and robustness while k2 showed lowest stability of all parameters. The 1TCM provides the best compromise between parameter stability and model accuracy; however application of the 2TCM is still feasible and provides a more accurate representation of tracer-kinetics at the cost of reduced robustness. Detailed tracer kinetic analysis of 18F-FET PET with compartment models holds valuable information on tumor substructures and provides additional diagnostic and prognostic value.

Simultaneous whole-body 18F-PSMA-1007-PET/MRI with integrated high-resolution multiparametric imaging of the prostatic fossa for comprehensive oncological staging of patients with prostate cancer: a pilot study

INTRODUCTION

The aim of the present study was to explore the clinical feasibility and reproducibility of a comprehensive whole-body ¹⁸F-PSMA-1007-PET/MRI protocol for imaging prostate cancer (PC) patients.

METHODS

Eight patients with high-risk biopsy-proven PC underwent a whole-body PET/MRI (3 h p.i.) including a multi-parametric prostate MRI after ¹⁸F-PSMA-1007-PET/CT (1 h p.i.) which served as reference. Seven patients presented with non-treated PC, whereas one patient presented with biochemical recurrence. SUV_mean-quantification was performed using a 3D-isocontour volume-of-interest. Imaging data was consulted for TNM-staging and compared with histopathology. PC was confirmed in 4/7 patients additionally by histopathology after surgery. PET-artifacts, co-registration of pelvic PET/MRI and MRI-data were assessed (PI-RADS 2.0).

RESULTS

The examinations were well accepted by patients and comprised 1 h. SUV_mean-values between PET/CT (1 h p.i.) and PET/MRI (3 h p.i.) were significantly correlated (p < 0.0001, respectively) and similar to literature of ¹⁸F-PSMA-1007-PET/CT 1 h vs 3 h p.i. The dominant intraprostatic lesion could be detected in all seven patients in both PET and MRI. T2c, T3a, T3b and T4 features were detected complimentarily by PET and MRI in five patients. PET/MRI demonstrated moderate photopenic PET-artifacts surrounding liver and kidneys representing high-contrast areas, no PET-artifacts were observed for PET/CT. Simultaneous PET-readout during prostate MRI achieved optimal co-registration results.

CONCLUSIONS

The presented ¹⁸F-PSMA-1007-PET/MRI protocol combines efficient whole-body assessment with high-resolution co-registered PET/MRI of the prostatic fossa for comprehensive oncological staging of patients with PC.

Improved clinical workflow for simultaneous whole-body PET/MRI using high-resolution CAIPIRINHA-accelerated MR-based attenuation correction

PURPOSE

To explore the value and reproducibility of a novel magnetic resonance based attenuation correction (MRAC) using a CAIPIRINHA-accelerated T1-weighted Dixon 3D-VIBE sequence for whole-body PET/MRI compared to the clinical standard.

METHODS

The PET raw data of 19 patients from clinical routine were reconstructed with standard MRAC (MRAC_std) and the novel MRAC (MRAC_caipi), a prototype CAIPIRINHA accelerated Dixon 3D-VIBE sequence, both acquired in 19 s/bed position. Volume of interests (VOIs) for liver, lung and all voxels of the total image stack were created to calculate standardized uptake values (SUV_mean) followed by inter-method agreement (Passing-Bablok regression, Bland-Altman analysis). A voxel-wise SUV comparison per patient was performed for intra-individual correlation between MRAC_std and MRAC_caipi. Difference images (MRAC_std-MRAC_caipi) of attenuation maps and SUV images were calculated. The image quality of in/opposed-phase water and fat images obtained from MRAC_caipi was assessed by two readers on a 5-point Likert-scale including intra-class coefficients for inter-reader agreement.

RESULTS

SUV_mean correlations of VOIs demonstrated high linearity (0.95<Spearman's rho<1, p<0.0001, respectively), substantiated by voxel-wise SUV scatter-plots (1.79×10⁸ pixels). Outliers could be explained by different physiological conditions between the scans such as different segmentation of air-containing tissue, lungs, kidneys, metal implants, diaphragm edge or small air bubbles in the gastrointestinal tracts that moved between MRAC acquisitions. Nasal sinuses and the trachea were better segmented in MRAC_caipi. High-resolution T1w Dixon 3D VIBE images were acquired in all cases and could be used for PET/MRI fusion. MRAC_caipi images were of high diagnostic quality (4.2±0.8) with 0.92-0.96 intra-class correlation.

CONCLUSIONS

The novel prototype MRAC_caipi extends the value for attenuation correction by providing a high spatial resolution DIXON-based dataset suited for diagnostic assessment towards time-efficient whole-body PET/MRI.

Time-dependent parameter of perfusion imaging as independent predictor of clinical outcome in symptomatic carotid artery stenosis

BACKGROUND

Carotid artery stenosis is a frequent cause of ischemic stroke. While any degree of stenosis can cause embolic stroke, a higher degree of stenosis can also cause hemodynamic infarction. The hemodynamic effect of a stenosis can be assessed via perfusion weighted MRI (PWI). Our aim was to investigate the ability of PWI-derived parameters such as TTP (time-to-peak) and T(max) (time to the peak of the residue curve) to predict outcome in patients with unilateral acute symptomatic internal carotid artery (sICA) stenosis.

METHODS

Patients with unilateral acute sICA stenosis (≥50% according to NASCET), without intracranial stenosis or occlusion, who underwent PWI, were included. Clinical characteristics, volume of restricted diffusion, volume of prolonged TTP and T(max) were retrospectively analyzed and correlated with outcome represented by the modified Rankin Scale (mRS) score at discharge. TTP and T(max) volumes were dichotomized using a ROC curve analysis. Multivariate analysis was performed to determine which PWI-parameter was an independent predictor of outcome.

RESULTS

Thirty-two patients were included. Degree of stenosis, volume of visually assessed TTP and volume of TTP ≥2 s did not distinguish patients with favorable (mRS 0-2) and unfavorable (mRS 3-6) outcome. In contrast, patients with unfavorable outcome had higher volumes of TTP ≥4 s (9.12 vs. 0.87 ml; p = 0.043), TTP ≥6 s (6.70 vs. 0.20 ml; p = 0.017), T(max) ≥4 s (25.27 vs. 0.00 ml; p = 0.043), T(max) ≥6 s (9.21 vs. 0.00 ml; p = 0.017), T(max) ≥8 s (6.86 vs. 0.00 ml; p = 0.011) and T(max) ≥10s (5.94 vs. 0.00 ml; p = 0.025) in univariate analysis. Multivariate logistic regression showed that NIHSS score on admission (Odds Ratio (OR) 0.466, confidence interval (CI) [0.224;0.971], p = 0.041), T(max) ≥8 s (OR 0.025, CI [0.001;0.898] p = 0.043) and TTP ≥6 s (OR 0.025, CI [0.001;0.898] p = 0.043) were independent predictors of clinical outcome.

CONCLUSION

As they stood out in multivariate regression and are objective and reproducible parameters, PWI-derived volumes of T(max) ≥8 s and TTP ≥6 s might be superior to degree of stenosis and visually assessed TTP maps in predicting short term patient outcome. Future studies should assess if perfusion weighted imaging might guide the selection of patients for recanalization procedures.

Differentiation of brain metastases by percentagewise quantification of intratumoral-susceptibility-signals at 3Tesla

INTRODUCTION

Evaluation of intratumoral-susceptibility-signals (ITSS) in susceptibility-weighted-imaging (SWI) has been reported to improve diagnostic performance for solitary enhancing brain lesions. Due to the distinct morphologic variability of ITSS, standardized evaluation proved to be difficult. We analyzed, if a new postprocessing method using percentagewise quantification (PQ) of ITSS enables differentiation between different entities of cerebral metastases and may thus improve differential diagnosis in cases of unknown primary.

MATERIALS AND METHODS

SWI and contrast enhanced T1-weighted MR images were acquired from 84 patients with intracerebral metastases (20 patients with mamma carcinoma (MC), 15 patients with malignant melanoma (MM), 49 patients with bronchial carcinoma (BC)) at 3Tesla MR. Images were co-registered and enhancing lesions were delineated on T1-weighted images and the outline transferred to the corresponding SWI map. All voxels within the lesion presenting values below a reference value placed in the ventricular system were determined and percentagewise calculated.

RESULTS

Diagnostic performance of percentagewise quantification (PQ) of ITSS, as determined with area under the receiver operating characteristic curve (AUC) was excellent (AUC=0.96; 95% confidence interval (CI) 0.90, 1.00) to discriminate MM from MC, good for the discrimination of MM and BC (AUC=0.81, 95% CI 0.70, 0.92) and poor for the discrimination of MC and BC (AUC=0.60; 95% CI 0.47, 0.73).

CONCLUSION

PQ is a new approach for the assessment of SWI that can be used for differential diagnosis of intracerebral metastases. Metastases of MM and MC or BC can be distinguished with high sensitivity and specificity.

Software-guided standardization of manual landmark data in medical images

It is crucial to evaluate registration algorithms in order to make them available in clinical practice. Several evaluation strategies have been proposed in the past, and one approach is to evaluate these algorithms with intrinsic anatomical landmarks identified by a health professional. The acquisition and handling of large amounts of these landmark data is a time-consuming task for the health professional, and it is vulnerable to errors and inconsistencies. Additionally, limited access to appropriate tools makes dealing with landmark data considerably more difficult. We introduce a strategy for the acquisition of landmarks for the landmark-based evaluation of registration algorithms and we present an ontology-driven software tool that assists the different partners involved to act according to that strategy. This tool provides the user with intrinsic knowledge of the registration problems, the possibility to conveniently make the acquired data available to further processing, and an easy-to-use graphical interface.

A flexible registration and evaluation engine (f.r.e.e.)

We present an image registration framework which offers effective assistance for solving current registration problems. This work was motivated by the huge amount of registration problems in clinical applications and the problem of finding adequate solutions and properly comparing them. We have therefore designed a framework that supports the establishment, evaluation and comparison of registration approaches. Flexible registration and evaluation engine (f.r.e.e.) achieves a broad basis of algorithms by utilizing the insight segmentation and registration toolkit (ITK). This basis can be extended by virtually any new approach or algorithm, which then becomes seamlessly integrated into the method set of the f.r.e.e. framework. The framework offers suitable tools for an easy integration, optimization and proper evaluation of registration approaches, as well as an efficient utilization of the results in clinical routine. The framework is currently being evaluated at the Heidelberg University Hospital, Germany. The first results were gathered with an application implemented for the Neurosurgical Department of the hospital. In these tests the framework concept, along with its specific tools, was very promising for establishing clinical applications (e.g. preoperative neurosurgical planning; registration of cardiac images) and therefore motivated further development. The ability to automatically optimize the parameterization of registration methods regarding a given test set also proved useful, allowing more concentration on scientific problems themselves and not on the laborious task of parameter tweaking. Due to implemented abstraction layers, f.r.e.e. also allows a high degree of transparency and thus good comparability of registration approaches and results.

A flexible registration framework for multimodal image data

This paper describes a registration framework based on the insight segmentation and registration toolkit (ITK) which can be used for matching multimodal image data. Different target groups with individual needs and precognition are addressed. The framework offers tools for supporting different matching tasks in a clinical environment. A setup editor defines specific rigid or non rigid matching approaches and the appropriate parameters. Different metrics including a correlation metric, a difference metric and mutual information based metrics are available. Furthermore, a test series editor can be used to evaluate the selected setup. The evaluation results, which are expressed in statistical figures, trends and performance measures, can be visualized and used for an optimal adapted setup configuration. Tests for matching precision, quality and parameter adjustments are offered. For export and import of image data, the most frequently used file formats of clinical environments like DICOM and ANALYZE are supported. We demonstrate some registration examples which frequently occur in the neurosurgical routine of a University Hospital.