Automatic rigid image Fusion of preoperative MR and intraoperative US acquired after craniotomy.

BACKGROUND: Neuronavigation of preoperative MRI is limited by several errors. Intraoperative ultrasound (iUS) with navigated probes that provide automatic superposition of pre-operative MRI and iUS and three-dimensional iUS reconstruction may overcome some of these limitations. Aim of the present study is to verify the accuracy of an automatic MRI - iUS fusion algorithm to improve MR-based neuronavigation accuracy. METHODS: An algorithm using Linear Correlation of Linear Combination (LC2)-based similarity metric has been retrospectively evaluated for twelve datasets acquired in patients with brain tumor. A series of landmarks were defined both in MRI and iUS scans. The Target Registration Error (TRE) was determined for each pair of landmarks before and after the automatic Rigid Image Fusion (RIF). The algorithm has been tested on two conditions of the initial image alignment: registration-based fusion (RBF), as given by the navigated ultrasound probe, and different simulated course alignments during convergence test. RESULTS: Except for one case RIF was successfully applied in all patients considering the RBF as initial alignment. Here, mean TRE after RBF was significantly reduced from 4.03 (± 1.40) mm to (2.08 ± 0.96 mm) (p = 0.002), after RIF. For convergence test, the mean TRE value after initial perturbations was 8.82 (± 0.23) mm which has been reduced to a mean TRE of 2.64 (± 1.20) mm after RIF (p < 0.001). CONCLUSIONS: The integration of an automatic image fusion method for co-registration of pre-operative MRI and iUS data may improve the accuracy in MR-based neuronavigation.

Intraoperative Integration of Multimodal Imaging to Improve Neuronavigation: A Technical Note.

BACKGROUND: Brain shift may cause significant error in neuronavigation, leading the surgeon to possible mistakes. Intraoperative magnetic resonance imaging (MRI) is the most reliable technique in brain tumor surgery. Unfortunately, it is highly expensive and time consuming and, at the moment, it is available only in few neurosurgical centers. METHODS: In this case series the surgical workflow for brain tumor surgery is described where neuronavigation of preoperative MRI, intraoperative computed tomography (CT) scan, and ultrasound (US) as well as rigid and elastic image fusion between preoperative MRI and intraoperative US and CT, respectively, was applied to 4 brain tumor patients in order to compensate for surgically induced brain shift by using a commercially available software (Elements Image Fusion 4.0 with Virtual iMRI Cranial; Brainlab AG, München, Germany). RESULTS: Four illustrative cases demonstrated successful integration of different components of the described intraoperative surgical workflow. The data indicate that intraoperative navigation update is feasible by applying intraoperative 3-dimensional US and CT scanning as well as rigid and elastic image fusion applied depending on the degree of observed brain shift. CONCLUSIONS: Integration of multiple intraoperative imaging techniques combined with rigid and elastic image fusion of preoperative MRI may reduce the risk of incorrect neuronavigation during brain tumor resection. Further studies are needed to confirm the present findings in a larger population.

Elastic Image Fusion Software to Coregister Preoperatively Planned Pedicle Screws With Intraoperative Computed Tomography Data for Image-Guided Spinal Surgery.

BACKGROUND: For complex spinal cases, especially when robotic guidance is used, preoperative planning of pedicle screws can be helpful. Transfer of these preoperatively planned pedicle screws to intraoperative 3-dimensional imaging is challenging because of changes in anatomic alignment between preoperative supine and intraoperative prone imaging, especially when multiple levels are involved. In the spine, where each individual vertebra is subject to independent movement from adjacent level, rigid image fusion is confined to a single vertebra and can display fusion inaccuracies on adjacent levels. A novel elastic fusion algorithm is introduced to overcome these disadvantages. This study aimed to investigate image registration accuracy of preoperatively planned pedicle screws with an elastic fusion algorithm vs. rigid fusion for intraoperative placement with image-guided surgery. METHODS: A total of 12 patients, were selected depending on the availability of a preoperative spinal computed tomography (CT) and an intraoperative AIRO CT scan (BrainLAB AG, Munich, Germany) of the same spinal region. To verify accuracy differences between rigid fusion and elastic fusion 76 bilateral screw trajectories were virtually defined in the preoperative CT image, and they were transferred via either rigid fusion or elastic fusion to the intraoperative CT scan. Accuracy of the transferred screws in the rigid and elastic fusion group was determined by measuring pedicle breaches on the intraoperative CT. RESULTS: In the rigid fusion group 1.3% of screws showed a breach of less than 2 mm, 9.2% showed breaches between 2 and 4 mm, and 18.4% of the screws showed an error above 4 mm. The elastic fusion group showed no breaches and provided high accuracy between preoperative and intraoperative screw placement. CONCLUSION: Elastic fusion provides high registration accuracy and represents a considerable step towards efficiency and safety in CT-based image-guided surgery. LEVEL OF EVIDENCE: 3.

Elastic Fusion Enables Fusion of Intraoperative Magnetic Resonance Imaging Data with Preoperative Neuronavigation Data.

OBJECTIVE: Intraoperative magnetic resonance imaging (iMRI) has been shown to optimize the extent of resection of parenchymal brain tumors. To facilitate the use of preoperative treatment plans after an intraoperative navigation update via iMRI, an elastic image fusion (EIF) algorithm was developed. METHODS: Ten MRI-iMRI data pairs of patients with brain tumor were evaluated and typical anatomic landmarks were assessed. The pre- and iMRI scans were elastically fused by using a prototype EIF software (Elements Virtual iMRI [Brainlab AG]). For each landmark pair, the Euclidean distance was calculated for rigidly and elastically fused image data. RESULTS: The Euclidean distance was 2.67 ± 2.62 mm using standard rigid image fusion and 1.8 ± 1.57 mm using our EIF algorithm (P = 0.005). For landmarks near the resected lesion, which were subject to higher anatomic distortion, the Euclidian distances were 4.38 ± 2.51 and 2.52 ± 1.9 mm (P = 0.003). CONCLUSIONS: This feasibility study shows that EIF can compensate for surgery-related brain shift in a highly significant manner even in this small number of cases. The establishment of an easy applicable and reliable EIF tool integrated in the clinical workflow could open a large variety of new options for image-guided tumor surgery.

Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus.

INTRODUCTION: Various automatic segmentation algorithms for the subthalamic nucleus (STN) have been published recently. However, most of the available software tools are not approved for clinical use. OBJECTIVE: The aim of this study is to evaluate a clinically available automatic segmentation tool of the navigation planning software Brainlab Elements (BL-E) by comparing the output to manual segmentation and a nonclinically approved research method using the DISTAL atlas (DA) and the Horn electrophysiological atlas (HEA). METHODS: Preoperative MRI data of 30 patients with idiopathic Parkinson's disease were used, resulting in 60 STN segmentations. The segmentations were created manually by two clinical experts. Automatic segmentations of the STN were obtained from BL-E and Advanced Normalization Tools using DA and HEA. Differences between manual and automatic segmentations were quantified by Dice and Jaccard coefficient, target overlap, and false negative/positive value (FNV/FPV) measurements. Statistical differences between similarity measures were assessed using the Wilcoxon signed-rank test with continuity correction, and comparison with interrater results was performed using the Mann-Whitney U test. RESULTS: For manual segmentation, the mean size of the segmented STN was 133 ± 24 mm3. The mean size of the STN was 121 ± 18 mm3 for BL-E, 162 ± 21 mm3 for DA, and 130 ± 17 mm3 for HEA. The Dice coefficient for the interrater comparison was 0.63 and 0.54 ± 0.12, 0.59 ± 0.13, and 0.52 ± 0.14 for BL-E, DA, and HEA, respectively. Significant differences between similarity measures were found for Dice and Jaccard coefficient, target overlap and FNV between BL-E and DA; and FPV between BL-E and HEA. However, none of the differences were significant compared to interrater variability. The analysis of the center of gravity of the segmentations revealed that the BL-E STN ROI was located more medially, superior and posterior compared to other segmentations. Regarding the target overlap for beta power within the STN ROI included with the HEA, the BL-E segmentation showed a significantly higher value compared to manual segmentation. CONCLUSION: Automatic image segmentation by means of the clinically approved software BL-E provides STN segmentations with similar accuracy like research tools, and differences are in the range of observed interrater variability. Further studies are required to investigate the clinical validity, for example, by comparing segmentation results of BL-E with electrophysiological data.

Intraoperative Computed Tomography and Finite Element Modelling for Multimodal Image Fusion in Brain Surgery.

BACKGROUND: intraoperative computer tomography (iCT) and advanced image fusion algorithms could improve the management of brainshift and the navigation accuracy. OBJECTIVE: To evaluate the performance of an iCT-based fusion algorithm using clinical data. METHODS: Ten patients with brain tumors were enrolled; preoperative MRI was acquired. The iCT was applied at the end of microsurgical resection. Elastic image fusion of the preoperative MRI to iCT data was performed by deformable fusion employing a biomechanical simulation based on a finite element model. Fusion accuracy was evaluated: the target registration error (TRE, mm) was measured for rigid and elastic fusion (Rf and Ef) and anatomical landmark pairs were divided into test and control structures according to distinct involvement by the brainshift. Intraoperative points describing the stereotactic position of the brain were also acquired and a qualitative evaluation of the adaptive morphing of the preoperative MRI was performed by 5 observers. RESULTS: The mean TRE for control and test structures with Rf was 1.81 ± 1.52 and 5.53 ± 2.46 mm, respectively. No significant change was observed applying Ef to control structures; the test structures showed reduced TRE values of 3.34 ± 2.10 mm after Ef (P < .001). A 32% average gain (range 9%-54%) in accuracy of image registration was recorded. The morphed MRI showed robust matching with iCT scans and intraoperative stereotactic points. CONCLUSIONS: The evaluated method increased the registration accuracy of preoperative MRI and iCT data. The iCT-based non-linear morphing of the preoperative MRI can potentially enhance the consistency of neuronavigation intraoperatively.

Retrospective distortion correction of diffusion tensor imaging data by semi-elastic image fusion - Evaluation by means of anatomical landmarks.

OBJECTIVE: Diffusion tensor imaging (DTI) based on echo-planar imaging (EPI) can suffer from geometric image distortions in comparison to conventional anatomical magnetic resonance imaging (MRI). Therefore, DTI-derived information, such as fiber tractography (FT) used for treatment planning of brain tumors, might be associated with spatial inaccuracies when linearly projected on anatomical MRI. Hence, a non-linear, semi-elastic image fusion shall be evaluated in this study that aims at correcting for image distortions in DTI. PATIENTS AND METHODS: In a sample of 27 patient datasets, 614 anatomical landmark pairs were retrospectively defined in DTI and T1- or T2-weighted three-dimensional (3D) MRI data. The datasets were processed by a commercial software package (Elements Image Fusion .0; Brainlab AG, Munich, Germany) providing rigid and semi-elastic fusion functionalities, such as DTI distortion correction. To quantify the displacement prior to and after semi-elastic fusion, the Euclidian distances of rigidly and elastically fused landmarks were evaluated by means of descriptive statistics and Bland-Altman plot. RESULTS: For rigid and semi-elastic fusion mean target registration errors of 3.03 ±â€¯2.29 mm and 2.04 ±â€¯1.95 mm were found, respectively, with 91% of the evaluated landmarks moving closer to their position determined in T1- or T2-weighted 3D MRI data after distortion correction. Most efficient correction was achieved for non-superficial landmarks showing distortions up to 1 cm. CONCLUSION: This study indicates that semi-elastic image fusion can be used for retrospective distortion correction of DTI data acquired for image guidance, such as DTI FT as used for a broad range of clinical indications.

Automatic image segmentation based on synthetic tissue model for delineating organs at risk in spinal metastasis treatment planning.

PURPOSE: One of the main goals in software solutions for treatment planning is to automatize delineation of organs at risk (OARs). In this pilot feasibility study a clinical validation was made of computed tomography (CT)-based extracranial auto-segmentation (AS) using the Brainlab Anatomical Mapping tool (AM). METHODS: The delineation of nine extracranial OARs (lungs, kidneys, trachea, heart, liver, spinal cord, esophagus) from clinical datasets of 24 treated patients was retrospectively evaluated. Manual delineation of OARs was conducted in clinical routine and compared with AS datasets using AM. The Dice similarity coefficient (DSC) and maximum Hausdorff distance (HD) were used as statistical and geometrical measurements, respectively. Additionally, all AS structures were validated using a subjective qualitative scoring system. RESULTS: All patient datasets investigated were successfully processed with the evaluated AS software. For the left lung (0.97 ± 0.03), right lung (0.97 ± 0.05), left kidney (0.91 ± 0.07), and trachea (0.93 ± 0.04), the DSC was high with low variability. The DSC scores of other organs (right kidney, heart, liver, spinal cord), except the esophagus, ranged between 0.7 and 0.9. The calculated HD values yielded comparable results. Qualitative assessment showed a general acceptance in more than 85% of AS OARs-except for the esophagus. CONCLUSIONS: The Brainlab AM software is ready for clinical use in most of the OARs evaluated in the thoracic and abdominal region. The software generates highly conformal structure sets compared to manual contouring. The current study design needs revision for further research.

Evaluation of a novel elastic registration algorithm for spinal imaging data: A pilot clinical study.

BACKGROUND: Rigid image coregistration is an established technique that allows spatial aligning. However, rigid fusion is prone to deformation of the imaged anatomies. In this work, a novel fully automated elastic image registration method is evaluated. METHODS: Cervical CT and MRI data of 10 patients were evaluated. The MRI was acquired with the patient in neutral, flexed, and rotated head position. Vertebrawise rigid fusions were performed to transfer bony landmarks for each vertebra from the CT to the MRI space serving as a reference. RESULTS: Elastic fusion of 3D MRI data showed the highest image registration accuracy (target registration error of 3.26 mm with 95% confidence). Further, an elastic fusion of 2D axial MRI data (<4.75 mm with 95% c.) was more reliable than for 2D sagittal sequences (<6.02 mm with 95% c.). CONCLUSIONS: The novel method enables elastic MRI-to-CT image coregistration for cervical indications with changes of the head position.

The reproducibility of different metabolic markers for muscle fiber type distributions investigated by functional 31P-MRS during dynamic exercise.

PURPOSE: The objective of the study was to investigate the reproducibility of exercise induced pH-heterogeneity by splitting of the inorganic phosphate (Pi) signal in the corresponding 31P-MRS spectra and to compare results of this approach with other fiber-type related markers, like phosphocreatine/adenosine triphosphate (PCr/ATP) ratio, and PCr-recovery parameters. MATERIAL AND METHODS: Subjects (N=3) with different sportive background were tested in 10 test sessions separated by at least 3 days. A MR-compatible pedal ergometer was used to perform the exercise and to induce a pH-based splitting of the Pi-signal in 31P-MR spectra of the medial gastrocnemius muscle. The PCr recovery was analyzed using a non-negative least square algorithm (NNLS) and multi-exponential regression analysis to estimate the number of non-exponential components as well as their amplitude and time constant. The reproducibility of the estimated metabolic marker and the resulting fiber-type distributions between the 10 test sessions were compared. RESULTS: The reproducibility (standard deviation between measurements) based on (1) Pi components varied from 2% to 4%, (2) PCr recovery time components varied from 10% to 12% and (3) phosphate concentrations at rest varied from 8% to 11% between test sessions. Due to the sportive activity differences between the 3 subjects were expected in view of fiber type distribution. All estimated markers indicate the highest type I percentage for volunteer 3 medium for volunteer 2 and the lowest for volunteer 1. CONCLUSIONS: The relative high reproducibility of pH dependent Pi components during exercise indicates a high potential of this method to estimate muscle fiber-type distributions in vivo. To make this method usable not only to detect differences in muscle fiber distributions but also to determine individual fiber-type volume contents it is therefore recommended to validate this marker by histological methods and to reveal the effects of muscle fiber recruitments and fiber-type specific Pi concentrations on the intensity ratios between the splitted Pi-components.

Age-related structural and functional changes of low back muscles.

During aging declining maximum force capacity with more or less unchanged fatigability is observed with the underlying mechanisms still not fully understood. Therefore, we compared morphology and function of skeletal muscles between different age groups. Changes in high-energy phosphate turnover (PCr, Pi and pH) and muscle functional MRI (mfMRI) parameters, including proton transverse relaxation time (T2), diffusion (D) and vascular volume fraction (f), were investigated in moderately exercised low back muscles of young and late-middle-aged healthy subjects with (31)P-MR spectroscopy, T2- and diffusion-weighted MRI at 3T. In addition, T1-weighted MRI data were acquired to determine muscle cross-sectional areas (CSA) and to assess fat infiltration into muscle tissue. Except for pH, both age groups showed similar load-induced MR changes and rates of perceived exertion (RPE), which indicates comparable behavior of muscle activation at moderate loads. Changes of mfMRI parameters were significantly associated with RPE in both cohorts. Age-related differences were observed, with lower pH and higher Pi/ATP ratios as well as lower D and f values in the late-middle-aged subjects. These findings are ascribed to age-related changes of fiber type composition, fiber size and vascularity. Interestingly, post exercise f was negatively associated with fat infiltration with the latter being significantly higher in late-middle-aged subjects. CSA of low back muscles remained unchanged, while CSA of inner back muscle as well as mean T2 at rest were associated with maximum force capacity. Overall, applying the proposed MR approach provides evidence of age-related changes in several muscle tissue characteristics and gives new insights into the physiological processes that take place during aging.

Interrelations of muscle functional MRI, diffusion-weighted MRI and (31) P-MRS in exercised lower back muscles.

Exercise-induced changes of transverse proton relaxation time (T2 ), tissue perfusion and metabolic turnover were investigated in the lower back muscles of volunteers by applying muscle functional MRI (mfMRI) and diffusion-weighted imaging (DWI) before and after as well as dynamic (31) P-MRS during the exercise. Inner (M. multifidus, MF) and outer lower back muscles (M. erector spinae, ES) were examined in 14 healthy young men performing a sustained isometric trunk-extension. Significant phosphocreatine (PCr) depletions ranging from 30% (ES) to 34% (MF) and Pi accumulations between 95% (left ES) and 120%-140% (MF muscles and right ES) were observed during the exercise, which were accompanied by significantly decreased pH values in all muscles (∆pH ≈ -0.05). Baseline T2 values were similar across all investigated muscles (approximately 27 ms at 3 T), but revealed right-left asymmetric increases (T2 ,inc ) after the exercise (right ES/MF: T2 ,inc = 11.8/9.7%; left ES/MF: T2 ,inc = 4.6/8.9%). Analyzed muscles also showed load-induced increases in molecular diffusion D (p = .007) and perfusion fraction f (p = .002). The latter parameter was significantly higher in the MF than in the ES muscles both at rest and post exercise. Changes in PCr (p = .03), diffusion (p < .01) and perfusion (p = .03) were strongly associated with T2,inc , and linear mixed model analysis revealed that changes in PCr and perfusion both affect T2,inc (p < .001). These findings support previous assumptions that T2 changes are not only an intra-cellular phenomenon resulting from metabolic stress but are also affected by increased perfusion in loaded muscles.

MR-compatible pedal ergometer for reproducible exercising of the human calf muscle.

A pneumatic MR-compatible pedal ergometer was designed to perform dynamic contraction exercises of the human calf muscle in a whole-body 3T MR scanner. The set-up includes sensors for monitoring mechanical parameters, such as pedal angle, cadence as well as applied force and power. Actual parameter values during the exercise were presented to the volunteer as a visual feedback to enable real-time self-adjustment of pedal deflection and cadence to the target reference value. Time-resolved dynamic (31)P-MR spectroscopic measurements of phosphocreatine (PCr), inorganic phosphate (Pi) and pH were performed in a pilot experiment before, during, and after the exercise by a single volunteer. Two different load strengths were applied in these experiments (15% and 25% of the maximum voluntary contraction, MVC). As expected, mechanical and metabolic parameters differed for the two load levels. Small variations of the cadence, power and metabolic changes (time constants of PCr depletion and Pi accumulation) during the experiments demonstrate a highly reproducible mechanical output by the volunteer mediated by the ergometer.

Fast low-angle shot diffusion tensor imaging with stimulated echo encoding in the muscle of rabbit shank.

In the past, spin-echo (SE) echo planar imaging(EPI)-based diffusion tensor imaging (DTI) has been widely used to study the fiber structure of skeletal muscles in vivo. However, this sequence has several shortcomings when measuring restricted diffusion in small animals, such as its sensitivity to susceptibility-related distortions and a relatively short applicable diffusion time. To address these limitations, in the current work, a stimulated echo acquisition mode (STEAM) MRI technique, in combination with fast low-angle shot (FLASH) readout (turbo-STEAM MRI), was implemented and adjusted for DTI in skeletal muscles. Signal preparation using stimulated echoes enables longer effective diffusion times, and thus the detection of restricted diffusion within muscular tissue with intracellular distances up to 100 µm. Furthermore, it has a reduced penalty for fast T2 muscle signal decay, but at the expense of 50% signal loss compared with a SE preparation. Turbo-STEAM MRI facilitates high-resolution DTI of skeletal muscle without introducing susceptibility-related distortions. To demonstrate its applicability, we carried out rabbit in vivo measurements on a human whole-body 3 T scanner. DTI parameters of the shank muscles were extracted, including the apparent diffusion coefficient, fractional anisotropy, eigenvalues and eigenvectors. Eigenvectors were used to calculate maps of structural parameters, such as the planar index and the polar coordinates θ and ϕ of the largest eigenvector. These parameters were compared between three muscles. θ and ϕ showed clear differences between the three muscles, reflecting different pennation angles of the underlying fiber structures. Fiber tractography was performed to visualize and analyze the architecture of skeletal pennate muscles. Optimization of tracking parameters and utilization of T2 -weighted images for improved muscle boundary detection enabled the determination of additional parameters, such as the mean fiber length. The presented results support the applicability of turbo-STEAM MRI as a promising method for quantitative DTI analysis and fiber tractography in skeletal muscles.

Absolute quantitation of brain metabolites with respect to heterogeneous tissue compositions in (1)H-MR spectroscopic volumes.

OBJECT: Referencing metabolite intensities to the tissue water intensity is commonly applied to determine metabolite concentrations from in vivo (1)H-MRS brain data. However, since the water concentration and relaxation properties differ between grey matter, white matter and cerebrospinal fluid (CSF), the volume fractions of these compartments have to be considered in MRS voxels. MATERIALS AND METHODS: The impact of partial volume correction was validated by phantom measurements in voxels containing mixtures of solutions with different NAA and water concentrations as well as by analyzing in vivo (1)H-MRS brain data acquired with various voxel compositions. RESULTS: Phantom measurements indicated substantial underestimation of NAA concentrations when assuming homogeneously composed voxels, especially for voxels containing solution, which simulated CSF (error: ≤ 92%). This bias was substantially reduced by taking into account voxel composition (error: ≤ 10%). In the in vivo study, tissue correction reduced the overall variation of quantified metabolites by up to 35% and revealed the expected metabolic differences between various brain tissues. CONCLUSIONS: Tissue composition affects extraction of metabolite concentrations and may cause misinterpretations when comparing measurements performed with different voxel sizes. This variation can be reduced by considering the different tissue types by means of combined analysis of spectroscopic and imaging data.

Diffusion weighted inner volume imaging of lumbar disks based on turbo-STEAM acquisition.

A magnetic resonance imaging (MRI) technique for diffusion weighted imaging (DWI) is described which, in contrast to echo planar imaging (EPI), is insensitive to off-resonance effects caused by tissue susceptibility differences, magnetic field inhomogeneities, or chemical shifts. The sequence combines a diffusion weighted (DW) spin-echo preparation and a stimulated echo acquisition mode (STEAM) module. Inner volume imaging (IVI) allows reduced rectangular field-of-view (FoV) in the phase encode direction, while suppressing aliasing artifacts that are usually the consequence of reduced FoVs. Sagittal turbo-STEAM images of the lumbar spine were acquired at 3.0T with 2.0 × 2.0 mm² in-plane resolution and 7 mm slice thickness with acquisition times of 407 ms per image. To calculate the apparent diffusion coefficient (ADC) in lumbar intervertebral disks (IVDs), the DW gradients were applied in three orthogonal gradient directions with b-values of 0 and 300 s/mm². For initial assessment of the ADC of normal and abnormal IVDs a pilot study with 8 subjects was performed. Mean ADC values of all normal IVDs were (2.27±0.40)×10⁻³ mm²/s and (1.89±0.34)×10⁻³ mm²/s for turbo-STEAM IVI and SE-EPI acquisition, respectively. Corresponding mean ADC values, averaged over all abnormal disks, were (1.93±0.39)×10⁻³ mm²/s and (1.51±0.46)×10⁻³ mm²/s, respectively, indicating a substantial ADC decrease (p<0.001).