Geometry planning and image registration in magnetic particle imaging using bimodal fiducial markers.

PURPOSE: Magnetic particle imaging (MPI) is a quantitative imaging modality that allows the distribution of superparamagnetic nanoparticles to be visualized. Compared to other imaging techniques like x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI), MPI only provides a signal from the administered tracer, but no additional morphological information, which complicates geometry planning and the interpretation of MP images. The purpose of the authors' study was to develop bimodal fiducial markers that can be visualized by MPI and MRI in order to create MP-MR fusion images. METHODS: A certain arrangement of three bimodal fiducial markers was developed and used in a combined MRI/MPI phantom and also during in vivo experiments in order to investigate its suitability for geometry planning and image fusion. An algorithm for automated marker extraction in both MR and MP images and rigid registration was established. RESULTS: The developed bimodal fiducial markers can be visualized by MRI and MPI and allow for geometry planning as well as automated registration and fusion of MR-MP images. CONCLUSIONS: To date, exact positioning of the object to be imaged within the field of view (FOV) and the assignment of reconstructed MPI signals to corresponding morphological regions has been difficult. The developed bimodal fiducial markers and the automated image registration algorithm help to overcome these difficulties.

Cerebral aneurysm pulsation: do iterative reconstruction methods improve measurement accuracy in vivo?

BACKGROUND AND PURPOSE: Electrocardiogram-gated 4D-CTA is a promising technique allowing new insight into aneurysm pathophysiology and possibly improving risk prediction of cerebral aneurysms. Due to the extremely small pulsational excursions (<0.1 mm in diameter), exact segmentation of the aneurysms is of critical importance. In vitro examinations have shown improvement of the accuracy of vessel delineation by iterative reconstruction methods. We hypothesized that this improvement shows a measurable effect on aneurysm pulsations in vivo. MATERIALS AND METHODS: Ten patients with cerebral aneurysms underwent 4D-CTA. Images were reconstructed with filtered back-projection and iterative reconstruction. The following parameters were compared between both groups: image noise, absolute aneurysm volumes, pulsatility, and sharpness of aneurysm edges. RESULTS: In iterative reconstruction images, noise was significantly reduced (mean, 9.8 ± 4.0 Hounsfield units versus 8.0 ± 2.5 Hounsfield units; P = .04), but the sharpness of aneurysm edges just missed statistical significance (mean, 3.50 ± 0.49 mm versus 3.42 ± 0.49 mm; P = .06). Absolute volumes (mean, 456.1 ± 775.2 mm(3) versus 461.7 ± 789.9 mm(3); P = .31) and pulsatility (mean, 1.099 ± 0.088 mm(3) versus 1.095 ± 0.082 mm(3); P = .62) did not show a significant difference between iterative reconstruction and filtered back-projection images. CONCLUSIONS: CT images reconstructed with iterative reconstruction methods show a tendency toward shorter vessel edges but do not affect absolute aneurysm volumes or pulsatility measurements in vivo.

Improved agreement between experienced and inexperienced observers using a standardized evaluation protocol for cardiac volumetry and infarct size measurement.

PURPOSE: To study the agreement between experienced and inexperienced observers before and after training using a standardized evaluation protocol for cardiac magnetic resonance imaging (CMR) measurements of left ventricular (LV) volumes, mass and infarct size. MATERIALS AND METHODS: First, 10 CMR studies from patients with myocardial infarction were analyzed by 2 experienced and 4 inexperienced observers in respect to end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), LV mass and infarct size. Subsequently, the inexperienced observers were trained using a standardized evaluation protocol. Thereafter, all observers analyzed another 10 CMR studies. RESULTS: Before training the relative difference between experienced and inexperienced observers was -4.3±8.2% for EDV, -13.3±14.2% for ESV, 5.9±8.2% for EF, -12.2±10.9% for LV mass and -27.0±29.0% for infarct size in gram. After training, agreement significantly improved to 0.2±8.8% for EDV (p<0.05), -2.1±10.9 for ESV (p<0.01), 1.5±6.9% for EF (p<0.05), and -3.6±17.1% for infarct size (p<0.0001), but no improvement was seen for LV mass (-11.2±7.9, p=0.64). A slice based analysis showed, that the variable inclusion of the most basal and apical slices were mainly responsible for the low agreement of the measurements before training. CONCLUSION: Training using a standardized evaluation protocol significantly improved the agreement between experienced and inexperienced observers for important CMR parameters. The proposed evaluation protocol can be used for training to improve the reproducibility of CMR measurements.

Automatic correction of gaps in cerebrovascular segmentations extracted from 3D time-of-flight MRA datasets.

OBJECTIVES: Exact cerebrovascular segmentations are required for several applications in today's clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. METHODS: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. RESULTS: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. CONCLUSION: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

Analysis of the influence of 4D MR angiography temporal resolution on time-to-peak estimation error for different cerebral vessel structures.

BACKGROUND AND PURPOSE: Time-resolved MRA imaging is a promising technique for blood flow evaluation in case of cerebrovascular malformations. Unfortunately, 4D MRA imaging is a trade-off between spatial and temporal resolution. The aim of this study was to investigate the influence of temporal resolution on the error associated with TTP estimation from indicator dilution curves derived from different vascular structures. MATERIALS AND METHODS: Monte Carlo simulation was performed to compute indicator dilution curves with known criterion standard TTP at temporal resolutions between 0.1 and 5 seconds. TTPs were estimated directly and by using 4 hemodynamic models for each curve and were compared with criterion standard TTP. Furthermore, clinical evaluation was performed by using 226 indicator dilution curves from different vessel structures obtained from clinical datasets. The temporal resolution was artificially decreased, and TTPs were estimated and compared with those obtained at the original temporal resolutions. The results of the clinical evaluations were further stratified for different vessel structures. RESULTS: The results of both evaluations show that the TTP estimation error increases exponentially when one lowers the temporal resolution. TTP estimation by using hemodynamic model curves leads to lower estimation errors compared with direct estimation. A temporal resolution of 1.5 seconds for arteries and 2.5 seconds for venous and arteriovenous malformation vessel structures appears to be reasonable to achieve TTP estimations adequate for clinical application. CONCLUSIONS: Different vessel structures require different temporal resolutions to enable comparable TTP estimation errors, which should be considered for achieving a case-optimal temporal and spatial resolution.

Fuzzy-based vascular structure enhancement in Time-of-Flight MRA images for improved segmentation.

OBJECTIVES: Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been proposed, which contrariwise have problems detecting malformed vessels. In this paper, a fuzzy logic-based method for fusion of intensity and vesselness information is presented, allowing an improved segmentation of malformed and small vessels at preservation of advantages of both approaches. METHODS: After preprocessing of a TOF dataset, the corresponding vesselness image is computed. The role of the fuzzy logic is to voxel-wisely fuse the intensity information from the TOF dataset with the corresponding vesselness information based on an analytically designed rule base. The resulting fuzzy parameter image can then be used for improved cerebrovascular segmentation. RESULTS: Six datasets, manually segmented by medical experts, were used for evaluation. Based on TOF, vesselness and fused fuzzy parameter images, the vessels of each patient were segmented using optimal thresholds computed by maximizing the agreement to manual segmentations using the Tanimoto coefficient. The results showed an overall improvement of 0.054 (fuzzy vs. TOF) and 0.079 (fuzzy vs. vesselness). Furthermore, the evaluation has shown that the method proposed yields better results than statistical Bayes classification. CONCLUSION: The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.

Automatic brain segmentation in Time-of-Flight MRA images.

OBJECTIVES: Cerebral vascular malformations might, caused by ruptures, lead to strokes. The rupture risk depends to a great extent on the individual anatomy of the vasculature. The 3D Time-of-Flight (TOF) MRA technique is one of the most commonly used non-invasive imaging techniques to obtain knowledge about the individual vascular anatomy. Unfortunately TOF images exhibit drawbacks for segmentation and direct volume visualization of the vasculature. To overcome these drawbacks an initial segmentation of the brain tissue is required. METHODS: After preprocessing of the data is applied the low-intensity tissues surrounding the brain are segmented using region growing. In a following step this segmentation is used to extract supporting points at the border of the brain for a graph-based contour extraction. Finally a consistency check is performed to identify local outliers which are corrected using non-linear registration. RESULTS: A quantitative validation of the method proposed was performed on 18 clinical datasets based on manual segmentations. A mean Dice coefficient of 0.989 was achieved while in average 99.56% of all vessel voxels were included by the brain segmentation. A comparison to the results yielded by three commonly used tools for brain segmentation revealed that the method described achieves better results, using TOF images as input, which are within the inter-observer variability. CONCLUSION: The method suggested allows a robust and automatic segmentation of brain tissue in TOF images. It is especially helpful to improve the automatic segmentation or direct volume rendering of the cerebral vascular system.

3D segmentation of the left ventricle combining long- and short-axis MR images.

OBJECTIVES: Segmentation of the left ventricle (LV) is required to quantify LV remodeling after myocardial infarction. Therefore spatiotemporal cine MR sequences including long-axis and short-axis images are acquired. In this paper a new segmentation method for fast and robust segmentation of the left ventricle is presented. METHODS: The new approach considers the position of the mitral valve and the apex as well as the long-axis contours to generate a 3D LV surface model. The segmentation result can be checked and adjusted in the short-axis images. Finally quantitative parameters were extracted. RESULTS: For evaluation the LV was segmented in eight datasets of the same subject by two medical experts using a contour drawing tool and the new segmentation tool. The results of both methods were compared concerning interaction time and intra- and inter-observer variance. The presented segmentation method proved to be fast. The mean difference and standard deviation of all parameters are decreased. In case of intra-observer comparison e.g. the mean ESV difference is reduced from 8.8% to 0.5%. CONCLUSION: A semi-automatic LV segmentation method has been developed that combines long- and short-axis views. Using the presented approach the intra- and interobserver difference as well as the time for the segmentation process are decreased. So the semi-automatic segmentation using long- and short-axis information proved to be fast and robust for the quantification of LV mass and volume properties.

Territorial and microvascular perfusion impairment in brain arteriovenous malformations.

BACKGROUND AND PURPOSE: Both the existence and clinical relevance of a steal phenomenon in brain arteriovenous malformations (AVMs) remains a matter of debate. This study aimed to assess perfusion in the brain adjacent to brain AVMs and to relate these to macrovascular blood flow in a single measurement. MATERIALS AND METHODS: Twenty consecutive patients with AVMs with a median age of 37 years were evaluated by 3T MR imaging by using 3D time-resolved MR angiography to determine blood flow and perfusion patterns. Cerebral perfusion was estimated by using an arterial spin-labeling technique in vascular territories around the nidus and in symmetric regions of interest in the ipsilateral and contralateral hemispheres. Mapping of concentric shells around the nidus was used to define the immediate and adjacent brain and relative perfusion reductions >20% of baseline, termed perinidal dip (PND). RESULTS: A significant reduction in perfusion ratios between ipsilateral and contralateral hemispheres remote to the AVMs was demonstrated in the middle and posterior cerebral artery territories. PND was detected in 5 patients, and 17 patients overall showed reduced perfusion in the perinidal region on visual inspection. There was a negative correlation of the hemispheric territorial perfusion with the affected/nonaffected inflow time ratio (R = -0.402, P = .015). CONCLUSIONS: The perfusion impairment in vascular territories adjacent to brain AVMs that we identified as PND may reflect the existence of 2 levels of perfusion impairment: a territorial and a microvascular perfusion disturbance. Although the hemispheric asymmetry in territorial perfusion seems the result of arterioarterial redistribution, the PND was inhomogeneously distributed within a single vascular territory and thus might result from low perfusion pressure in small arteries and arterioles.

Motion artifact reducing reconstruction of 4D CT image data for the analysis of respiratory dynamics.

OBJECTIVES: Respiratory motion represents a major problem in radiotherapy of thoracic and abdominal tumors. Methods for compensation require comprehensive knowledge of underlying dynamics. Therefore, 4D (= 3D + t) CT data can be helpful. But modern CT scanners cannot scan a large region of interest simultaneously. So patients have to be scanned in segments. Commonly used approaches for reconstructing the data segments into 4D CT images cause motion artifacts. In order to reduce the artifacts, a new method for 4D CT reconstruction is presented. The resulting data sets are used to analyze respiratory motion. METHODS: Spatiotemporal CT image sequences of lung cancer patients were acquired using a multi-slice CT in cine mode during free breathing. 4D CT reconstruction was done by optical flow based temporal interpolation. The resulting 4D image data were compared with data generated by the commonly used nearest neighbor reconstruction. Subsequent motion analysis is mainly concerned with tumor mobility. RESULTS: The presented optical flow-based method enables the reconstruction of 3D CT images at arbitrarily chosen points of the patient's breathing cycle. A considerable reduction of motion artifacts has been proven in eight patient data sets. Motion analysis showed that tumor mobility differs strongly between the patients. CONCLUSIONS: Due to the proved reduction of motion artifacts, the optical flow-based 4D CT reconstruction offers the possibility of high-quality motion analysis. Because the method is based on an interpolation scheme, it additionally has the potential to enable the reconstruction of 4D CT data from a lesser number of scans.

Structure-preserving interpolation of temporal and spatial image sequences using an optical flow-based method.

OBJECTIVES: Modern tomographic imaging devices enable the acquisition of spatial and temporal image sequences. But, the spatial and temporal resolution of such devices is limited and therefore image interpolation techniques are needed to represent images at a desired level of discretization. This paper presents a method for structure-preserving interpolation between neighboring slices in temporal or spatial image sequences. METHODS: In a first step, the spatiotemporal velocity field between image slices is determined using an optical flow-based registration method in order to establish spatial correspondence between adjacent slices. An iterative algorithm is applied using the spatial and temporal image derivatives and a spatiotemporal smoothing step. Afterwards, the calculated velocity field is used to generate an interpolated image at the desired time by averaging intensities between corresponding points. Three quantitative measures are defined to evaluate the performance of the interpolation method. RESULTS: The behavior and capability of the algorithm is demonstrated by synthetic images. A population of 17 temporal and spatial image sequences are utilized to compare the optical flow-based interpolation method to linear and shape-based interpolation. The quantitative results show that the optical flow-based method outperforms the linear and shape-based interpolation statistically significantly. CONCLUSIONS: The interpolation method presented is able to generate image sequences with appropriate spatial or temporal resolution needed for image comparison, analysis or visualization tasks. Quantitative and qualitative measures extracted from synthetic phantoms and medical image data show that the new method definitely has advantages over linear and shape-based interpolation.

Computer-assisted analysis of 4D cardiac MR image sequences after myocardial infarction.

OBJECTIVES: Spatial-temporal MR image sequences of the heart contain information about shape and motion changes and pathological structures after myocardial infarction. In this paper a Heart Analysis Tool (HeAT) for the quantitative analysis of 4D MR image sequences of infarct patients is presented. METHODS: HeAT supports interactive segmentation of anatomical and pathological structures. Registration of Cine- and DE-MR image data is applied to enable their combined evaluation during the analysis process. Partitioning of the myocardium in segments enables the analysis with high local resolution. Corresponding segments are generated and used for inter/intrapatient comparison. Quantitative parameters were extracted and visualized. RESULTS: Parameters like endocard movement in the infarcted area of six infarct patients were computed in HeAT. Parameters in the infarct area show the expected dysfunctional characteristics. Based on theses parameters passive endocardial movement and myocardial areas with decreased contraction could be identified. CONCLUSION: In contrast to other software tools HeAT supports the combination of contour information of Cine-MR and DE-MR, local analysis with high resolution and inter/intra patient comparison. HeAT enables an observer-independent evaluation of the complex cardiac image data. Using HeAT in further studies can increase the understanding of left ventricle (LV) remodeling.