A spiculated mass target model for clinical image quality control in digital mammography.

OBJECTIVES: Quality assurance of breast imaging has a long history of using test objects to optimize and follow up imaging devices. In particular, the evaluation of new techniques benefits from suitable test objects. The applicability of a phantom consisting of spiculated masses to assess image quality and its dependence on dose in flat field digital mammography (FFDM) and digital breast tomosynthesis systems (DBT) is investigated. METHODS: Two spiculated masses in five different sizes each were created from a database of clinical tumour models. The masses were produced using 3D printing and embedded into a cuboid phantom. Image quality is determined by the number of spicules identified by human observers. RESULTS: The results suggest that the effect of dose on spicule detection is limited especially in cases with smaller objects and probably hidden by the inter-reader variability. Here, an average relative inter-reader variation of the counted number of 31% was found (maximum 83%). The mean relative intra-reader variability was found to be 17%. In DBT, sufficiently good results were obtained only for the largest masses. CONCLUSIONS: It is possible to integrate spiculated masses into a cuboid phantom. It is easy to print and should allow a direct and prompt evaluation of the quality status of the device by counting visible spicules. Human readout presented the major uncertainty in this study, indicating that automated readout may improve the reproducibility and consistency of the results considerably. ADVANCES IN KNOWLEDGE: A cuboid phantom including clinical objects as spiculated lesion models for visual assessing the image quality in FFDM and DBT was developed and is introduced in this work. The evaluation of image quality works best with the two larger masses with 21 spicules.

Technical note: Realization and uncertainty analysis for an adjustable 3D structured breast phantom in digital breast tomosynthesis.

BACKGROUND: Projection imaging phantoms are often optimized for 2-dimensional image characteristics in homogeneous backgrounds. Therefore, evaluation of image quality in tomosynthesis (DBT) lacks accepted and established phantoms. PURPOSE: We describe a 3D breast phantom with a structured, variable background. The phantom is an adaptable and advanced version of the L1 phantom by Cockmartin et al. Phantom design and its use for quality assurance measurements for DBT devices are described. Four phantoms were compared to assess the objectivity. METHODS: The container size was increased to a diameter of 24 cm and a total height of 53.5 mm. Spiculated masses were replaced by five additional non-spiculated masses for higher granularity in threshold diameter resolution. These patterns are adjustable to the imaging device. The masses were printed in one session with a base layer using two-component 3D printing. New materials compared to the L1 phantom improved the attenuation difference between the lesion models and the background. Four phantoms were built and intra-human observer, inter-human observer and inter-phantom variations were determined. The latter assess the reproducibility of the phantom production. Coefficients of variance (V) were calculated for all three variations. RESULTS: The difference of the attenuation coefficients between the lesion models and the background was 0.20 cm-1 (with W/Al at 32 kV, equivalent to 19-20 keV effective energy) compared to 0.21 cm-1 for 50/50 glandular/adipose breast tissue and cancerous lesions. PMMA equivalent thickness of the phantom was 47.0 mm for the Siemens Mammomat Revelation. For the masses, the V i n t r a $V_{intra}$ for the intra-observer variation was 0.248, the averaged inter-observer variation, V ¯ i n t e r $\overline{V}_{inter}$ was 0.383. V p h a n t o m $V_{phantom}$ for phantom variance was 0.321. For the micro-calcifications, V i n t r a $V_{intra}$ was 0.0429, V ¯ i n t e r = $\overline{V}_{inter}=$ 0.0731 and V p h a n t o m = $V_{phantom}=$ 0.0759. CONCLUSIONS: Position, orientation and shape of the masses are reproducible and attenuation differences appropriate. The phantom presented proved to be a candidate test object for quality control.

3D ultrasound guided navigation system with hybrid image fusion.

A prototype of a navigation system to fuse two image modalities is presented. The standard inter-modality registration is replaced with a tracker-based image registration of calibrated imaging devices. Intra-procedure transrectal US (TRUS) images were merged with pre-procedure magnetic resonance (MR) images for prostate biopsy. The registration between MR and TRUS images was performed by an additional abdominal 3D-US (ab-3D-US), which enables replacing the inter-modal MR/TRUS registration by an intra-modal ab-3D-US/3D-TRUS registration. Calibration procedures were carried out using an optical tracking system (OTS) for the pre-procedure image fusion of the ab-3D-US with the MR. Inter-modal ab-3D-US/MR image fusion was evaluated using a multi-cone phantom for the target registration error (TRE) and a prostate phantom for the Dice score and the Hausdorff distance of lesions . Finally, the pre-procedure ab- 3D-US was registered with the TRUS images and the errors for the transformation from the MR to the TRUS were determined. The TRE of the ab-3D-US/MR image registration was 1.81 mm. The Dice-score and the Hausdorff distance for ab-3D-US and MR were found to be 0.67 and 3.19 mm. The Dice score and the Hausdorff distance for TRUS and MR were 0.67 and 3.18 mm. The hybrid navigation system showed sufficient accuracy for fusion guided biopsy procedures with prostate phantoms. The system might provide intra-procedure fusion for most US-guided biopsy and ablation interventions.

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic 18F-FDG PET Brain Studies.

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic 18F-FDG brain studies. Methods: Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test-retest 18F-FDG PET/MRI examination of the brain (n = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence. Arterial blood samples were collected as a reference standard representing the arterial input function (AIF). Training of the cGAN was performed using 70% of the total datasets (n = 16, randomly chosen), which was corrected for motion using MR navigators. The resulting cGAN mappings (between individual frames and the reference frame [55-60 min after injection]) were then applied to the test dataset (remaining 30%, n = 6), producing artificially generated low-noise images from early high-noise PET frames. These low-noise images were then coregistered to the reference frame, yielding 3-dimensional motion vectors. Performance of cGAN-aided motion correction was assessed by comparing the image-derived input function (IDIF) extracted from a cGAN-aided motion-corrected dynamic sequence with the AIF based on the areas under the curves (AUCs). Moreover, clinical relevance was assessed through direct comparison of the average cerebral metabolic rates of glucose (CMRGlc) values in gray matter calculated using the AIF and the IDIF. Results: The absolute percentage difference between AUCs derived using the motion-corrected IDIF and the AIF was (1.2% + 0.9%). The gray matter CMRGlc values determined using these 2 input functions differed by less than 5% (2.4% + 1.7%). Conclusion: A fully automated data-driven motion-compensation approach was established and tested for 18F-FDG PET brain imaging. cGAN-aided motion correction enables the translation of noninvasive clinical absolute quantification from PET/MR to PET/CT by allowing the accurate determination of motion vectors from the PET data itself.

Evaluation of 3D ultrasound for image guidance.

PURPOSE: In this paper we compared two different 3D ultrasound (US) modes (3D free-hand mode and 3D wobbler mode) to see which is more suitable to perform the 3D-US/3D-US registration for clinical guidance applications. The typical errors with respect to their impact on the final localization error were evaluated step by step. METHODS: Multi-point target and Hand-eye calibration methods were used for 3D US calibration together with a newly designed multi-cone phantom. Pointer based and image based methods were used for 2D US calibration. The calibration target error was computed by using a different multi-cone phantom. An egg-shaped phantom was used as ground truth to compare distortions for both 3D modes along with the measurements of the volume. Finally, we compared 3D ultrasound images acquired by 3D wobbler mode and 3D free-hand mode with respect to their 3D-US/3D-US registration accuracy using both, phantom and patient data. A theoretical step by step error analysis was performed and compared to empirical data. RESULTS: Target registration errors based on the calibration with the 3D Multi-point and 2D pointer/image method have been found to be comparable (∼1mm). They both outperformed the 3D Hand-eye method (error >2mm). Volume measurements with the 3D free-hand mode were closest to the ground truth (around 6% error compared to 9% with the 3D wobbler mode). Additional scans on phantoms showed a 3D-US/3D-US registration error below 1 mm for both, the 3D free-hand mode and the 3D wobbler mode, respectively. Results with patient data showed greater error with the 3D free-hand mode (6.50mm - 13.37mm) than with the 3D wobbler mode (2.99 ± 1.54 mm). All the measured errors were found to be in accordance to their theoretical upper bounds. CONCLUSION: While both 3D volume methods showed comparable results with respect to 3D-US/3D-US registration for phantom images, for patient data registrations the 3D wobbler mode is superior to the 3D free-hand mode. The effect of all error sources could be estimated by theoretical derivations.

Comparison of a personalized breast dosimetry method with standard dosimetry protocols.

Average glandular dose (AGD) in digital mammography crucially depends on the estimation of breast glandularity. In this study we compared three different methods of estimating glandularities according to Wu, Dance and Volpara with respect to resulting AGDs. Exposure data from 3050 patient images, acquired with a GE Senographe Essential constituted the study population of this work. We compared AGD (1) according to Dance et al. applying custom g, c, and s factors using HVL, breast thickness, patient age and incident air kerma (IAK) from the DICOM headers; (2) according to Wu et al. as determined by the GE system; and (3) AGD derived with the Dance model with personalized c factors using glandularity determined with the Volpara (Volpara Solutions, Wellington, New Zealand) software (Volpare AGD). The ratios of the resulting AGDs were analysed versus parameters influencing dose. The highest deviation between the resulting AGDs was found in the ratio of GE AGD to Volpara AGD for breast thicknesses between 20 and 40 mm (ratio: 0.80). For thicker breasts this ratio is close to one (1 ± 0.02 for breast thicknesses >60 mm). The Dance to Volpara ratio was between 0.86 (breast thickness 20-40 mm) and 0.99 (>80 mm), and Dance/GE AGD was between 1.07 (breast thickness 20-40 mm) and 0.98 (41-60, and >80 mm). Glandularities by Volpara were generally smaller than the one calculated with the Dance method. This effect is most pronounced for small breast thickness and older ages. Taking the considerable divergences between the AGDs from different methods into account, the selection of the method should by done carefully. As the Volpara method provides an analysis of the individual breast tissue, while the Wu and the Dance methods use look up tables and custom parameter sets, the Volpara method might be more appropriate if individual ADG values are sought. For regulatory purposes and comparison with diagnostic reference values, the method to be used needs to be defined exactly and clearly be stated. However, it should be accepted that dose values calculated with standardized models, like AGD and also effective dose, are afflicted with a considerable uncertainty budgets that need to be accounted for in the interpretation of these values.

Deformable registration of 3D ultrasound volumes using automatic landmark generation.

US image registration is an important task e.g. in Computer Aided Surgery. Due to tissue deformation occurring between pre-operative and interventional images often deformable registration is necessary. We present a registration method focused on surface structures (i.e. saliencies) of soft tissues like organ capsules or vessels. The main concept follows the idea of representative landmarks (so called leading points). These landmarks represent saliencies in each image in a certain region of interest. The determination of deformation was based on a geometric model assuming that saliencies could locally be described by planes. These planes were calculated from the landmarks using two dimensional linear regression. Once corresponding regions in both images were found, a displacement vector field representing the local deformation was computed. Finally, the deformed image was warped to match the pre-operative image. For error calculation we used a phantom representing the urinary bladder and the prostate. The phantom could be deformed to mimic tissue deformation. Error calculation was done using corresponding landmarks in both images. The resulting target registration error of this procedure amounted to 1.63 mm. With respect to patient data a full deformable registration was performed on two 3D-US images of the abdomen. The resulting mean distance error was 2.10 ± 0.66 mm compared to an error of 2.75 ± 0.57 mm from a simple rigid registration. A two-sided paired t-test showed a p-value < 0.001. We conclude that the method improves the results of the rigid registration considerably. Provided an appropriate choice of the filter there are many possible fields of applications.

Design of a head phantom produced on a 3D rapid prototyping printer and comparison with a RANDO and 3M lucite head phantom in eye dosimetry applications.

An anthropomorphic head phantom including eye inserts allowing placement of TLDs 3 mm below the cornea has been produced on a 3D printer using a photo-cured acrylic resin to best allow tissue equivalence. Thus Hp(3) can be determined in radiological and interventional photon radiation fields. Eye doses and doses to the forehead have been compared to an Alderson RANDO head and a 3M Lucite skull phantom in terms of surface dose per incident air kerma for frontal irradiation since the commercial phantoms do not allow placement of TLDs 3 mm below the corneal surface. A comparison of dose reduction factors (DRFs) of a common lead glasses model has also been performed. Eye dose per incident air kerma were comparable between all three phantoms (printed phantom: 1.40, standard error (SE) 0.04; RANDO: 1.36, SE 0.03; 3M: 1.37, SE 0.03). Doses to the forehead were identical to eye surface doses for the printed phantom and the RANDO head (ratio 1.00 SE 0.04, and 0.99 SE 0.03, respectively). In the 3M Lucite skull phantom dose on the forehead was 15% lower than dose to the eyes attributable to phantom properties. DRF of a sport frame style leaded glasses model with 0.75 mm lead equivalence measured were 6.8 SE 0.5, 9.3 SE 0.4 and 10.5 SE 0.5 for the RANDO head, the printed phantom, and the 3M Lucite head phantom, respectively, for frontal irradiation. A comparison of doses measured in 3 mm depth and on the surface of the eyes in the printed phantom revealed no difference larger than standard errors from TLD dosimetry. 3D printing offers an interesting opportunity for phantom design with increasing potential as printers allowing combinations of tissue substitutes will become available. Variations between phantoms may provide a useful indication of uncertainty budgets when using phantom measurements to estimate individual personnel doses.

Technical Note: Fully-automated analysis of Jaszczak phantom measurements as part of routine SPECT quality control.

PURPOSE: Inspection and quantitative validation of tomographic imaging properties of SPECT systems, i.e., spatial resolution, contrast, and inhomogeneity must be performed in regular intervals. Typically, the modular Jaszczak phantom is used for that purpose, as it offers the possibility to investigate all three system properties with a single measurement. The interpretation of the measurement is performed visually, thus, being insensitive to subtle changes in system performance. To overcome this limitation, a fully-automated software for the objective analysis of Jaszczak phantom measurements is proposed here. METHODS: The software was developed as an ImageJ plugin and offers a number of sequential evaluation steps: automatic determination of the type of Jaszczak phantom, calculation of sector and sphere contrast, detection of ring artifacts using either the Hough transform, followed by a threshold-based decision criterion, or Student's t-test. Monte Carlo simulations were used to estimate the detectability limits for ring artifacts. RESULTS: The software successfully calculated sector and sphere contrasts and reliably determined ring artifacts present in the homogeneity part of the Jaszczak phantom, based on automatic identification of the phantom type. CONCLUSION: Given the quantitative nature of the produced output, results from one imaging system can easily be compared to another in an objective way. The advantage of the software is clearly that the information provided is objective and does not rely on the experience level of the user.

Conversion factors between human and automatic readouts of CDMAM phantom images of CR mammography systems.

In mammography screening, profound assessment of technical image quality is imperative. The European protocol for the quality control of the physical and technical aspects of mammography screening (EPQCM) suggests using an alternate fixed choice contrast-detail phantom-like CDMAM. For the evaluation of technical image quality, human or automated readouts can be used. For automatic evaluation, a software (cdcom) is provided by EUREF. If the automated readout indicates unacceptable image quality, additional human readout may be performed overriding the automated readout. As the latter systematically results in higher image quality ratings, conversion factors between both methods are regularly applied. Since most image quality issues with mammography systems arise within CR systems, an assessment restricted to CR systems with data from the Austrian Reference Center in the mammography screening program has been conducted. Forty-five CR systems were evaluated. Human readouts were performed with a randomisation software to avoid bias due to learning effects. Additional automatic evaluation allowed for the computation of conversion factors between human and automatic readouts. These factors were substantially lower compared to those suggested by EUREF, namely 1.21 compared to 1.62 (EUREF UK method) and 1.42 (EUREF EU method) for 0.1 mm, and 1.40 compared to 1.83 (EUREF UK) and 1.73 (EUREF EU) for 0.25 mm structure size, respectively. Using either of these factors to adjust patient dose in order to comply with image quality requirements results in differences in the dose increase of up to 90%. This necessitates a consensus on their proper application and limits the validity of the assessment methods. Clear criteria for CR systems based on appropriate studies should be promoted.

Spectrum optimization for computed radiography mammography systems.

PURPOSE: Technical quality assurance is a key issue in breast screening protocols. While full-field digital mammography systems produce excellent image quality at low dose, it appears difficult with computed radiography (CR) systems to fulfill the requirements for image quality, and to keep the dose below the limits. However, powder plate CR systems are still widely used, e.g., they represent ∼30% of the devices in the Austrian breast cancer screening program. For these systems the selection of an optimal spectrum is a key issue. METHODS: We investigated different anode/filter (A/F) combinations over the clinical range of tube voltages. The figure-of-merit (FOM) to be optimized was squared signal-difference-to-noise ratio divided by glandular dose. Measurements were performed on a Siemens Mammomat 3000 with a Fuji Profect reader (SiFu) and on a GE Senograph DMR with a Carestream reader (GECa). RESULTS: For 50mm PMMA the maximum FOM was found with a Mo/Rh spectrum between 27kVp and 29kVp, while with 60mm Mo/Rh at 28kVp (GECa) and W/Rh 25kVp (SiFu) were superior. For 70mm PMMA the Rh/Rh spectrum had a peak at about 31kVp (GECa). FOM increases from 10% to >100% are demonstrated. CONCLUSION: Optimization as proposed in this paper can either lead to dose reduction with comparable image quality or image quality improvement if necessary. For systems with limited A/F combinations the choice of tube voltage is of considerable importance. In this work, optimization of AEC parameters such as anode-filter combination and tube potential was demonstrated for mammographic CR systems.

Automatic patient alignment system using 3D ultrasound.

PURPOSE: Recent developments in radiation therapy such as intensity modulated radiotherapy (IMRT) or dose painting promise to provide better dose distribution on the tumor. For effective application of these methods the exact positioning of the patient and the localization of the irradiated organ and surrounding structures is crucial. Especially with respect to the treatment of the prostate, ultrasound (US) allows for differentiation between soft tissue and was therefore applied by various repositioning systems, such as BAT or Clarity. The authors built a new system which uses 3D US at both sites, the CT room and the intervention room and applied a 3D/3D US/US registration for automatic repositioning. METHODS: In a first step the authors applied image preprocessing methods to prepare the US images for an optimal registration process. For the 3D/3D registration procedure five different metrics were evaluated. To find the image metric which fits best for a particular patient three 3D US images were taken at the CT site and registered to each other. From these results an US registration error was calculated. The most successful image metric was then applied for the US/US registration process. The success of the whole repositioning method was assessed by taking the results of an ExacTrac system as golden standard. RESULTS: The US/US registration error was found to be 2.99 ± 1.54 mm with respect to the mutual information metric by Mattes (eleven patients) which revealed to be the most suitable of the assessed metrics. For complete repositioning chain the error amounted to 4.15 ± 1.20 mm (ten patients). CONCLUSIONS: The authors developed a system for patient repositioning which works automatically without the necessity of user interaction with an accuracy which seems to be suitable for clinical application.

An error analysis perspective for patient alignment systems.

PURPOSE: This paper analyses the effects of error sources which can be found in patient alignment systems. As an example, an ultrasound (US) repositioning system and its transformation chain are assessed. The findings of this concept can also be applied to any navigation system. METHODS AND MATERIALS: In a first step, all error sources were identified and where applicable, corresponding target registration errors were computed. By applying error propagation calculations on these commonly used registration/calibration and tracking errors, we were able to analyse the components of the overall error. Furthermore, we defined a special situation where the whole registration chain reduces to the error caused by the tracking system. Additionally, we used a phantom to evaluate the errors arising from the image-to-image registration procedure, depending on the image metric used. We have also discussed how this analysis can be applied to other positioning systems such as Cone Beam CT-based systems or Brainlab's ExacTrac. RESULTS: The estimates found by our error propagation analysis are in good agreement with the numbers found in the phantom study but significantly smaller than results from patient evaluations. We probably underestimated human influences such as the US scan head positioning by the operator and tissue deformation. Rotational errors of the tracking system can multiply these errors, depending on the relative position of tracker and probe. CONCLUSIONS: We were able to analyse the components of the overall error of a typical patient positioning system. We consider this to be a contribution to the optimization of the positioning accuracy for computer guidance systems.

Factors for conversion between human and automatic read-outs of CDMAM images.

PURPOSE: According to the European protocol for the quality control of the physical and technical aspects of mammography screening (EPQCM) image quality of digital mammography devices has to be assessed using human evaluation of the CDMAM contrast-detail phantom. This is accomplished by the determination of threshold thicknesses of gold disks with different diameters (0.08-2 mm) and revealed to be very time consuming. Therefore a software solution based on a nonprewhitening matched filter (NPW) model was developed at the University of Nijmegen. Factors for the conversion from automatic to human readouts have been determined by Young et al. [Proc. SPIE 614206, 1-13 (2006) and Proc. SPIE 6913, 69131C1 (2008)] using a huge amount of data of both human and automatic readouts. These factors depend on the observer groups and are purely phenomenological. The authors present an alternative approach to determine the factors by using the Rose observer model. METHODS: Their method uses the Rose theory which gives a relationship between threshold contrast, diameter of the object and number of incident photons. To estimate the conversion factors for the five diameters from 0.2 to 0.5 mm they exposed with five different current-time products which resulted in 25 equations with five unknowns. RESULTS: The theoretical conversion factors (in dependence of the diameters) amounted to be 1.61 ± 0.02 (0.2 mm diameter), 1.67 ± 0.02 (0.25 mm), 1.85 ± 0.02 (0.31 mm), 2.09 ± 0.02 (0.4 mm), and 2.28 ± 0.02 (0.5 mm). The corresponding phenomenological factors found in literature are 1.74 (0.2 mm), 1.78 (0.25 mm), 1.83 (0.31 mm), 1.88 (0.4 mm), and 1.93 (0.5 mm). CONCLUSIONS: They transferred the problem of determining the factors to a well known observer model which has been examined for many years and is also well established. This method reveals to be reproduceable and produces factors comparable to the phenomenological ones.

Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies.

In this article, the authors evaluate a merit function for 2D/3D registration called stochastic rank correlation (SRC). SRC is characterized by the fact that differences in image intensity do not influence the registration result; it therefore combines the numerical advantages of cross correlation (CC)-type merit functions with the flexibility of mutual-information-type merit functions. The basic idea is that registration is achieved on a random subset of the image, which allows for an efficient computation of Spearman's rank correlation coefficient. This measure is, by nature, invariant to monotonic intensity transforms in the images under comparison, which renders it an ideal solution for intramodal images acquired at different energy levels as encountered in intrafractional kV imaging in image-guided radiotherapy. Initial evaluation was undertaken using a 2D/3D registration reference image dataset of a cadaver spine. Even with no radiometric calibration, SRC shows a significant improvement in robustness and stability compared to CC. Pattern intensity, another merit function that was evaluated for comparison, gave rather poor results due to its limited convergence range. The time required for SRC with 5% image content compares well to the other merit functions; increasing the image content does not significantly influence the algorithm accuracy. The authors conclude that SRC is a promising measure for 2D/3D registration in IGRT and image-guided therapy in general.

A comparative study on manual and automatic slice-to-volume registration of CT images.

In order to assess the clinical relevance of a slice-to-volume registration algorithm, this technique was compared to manual registration. Reformatted images obtained from a diagnostic CT examination of the lower abdomen were reviewed and manually registered by 41 individuals. The results were refined by the algorithm. Furthermore, a fully automatic registration of the single slices to the whole CT examination, without manual initialization, was also performed. The manual registration error for rotation and translation was found to be 2.7+/-2.8 degrees and 4.0+/-2.5 mm. The automated registration algorithm significantly reduced the registration error to 1.6+/-2.6 degrees and 1.3+/-1.6 mm (p = 0.01). In 3 of 41 (7.3%) registration cases, the automated registration algorithm failed completely. On average, the time required for manual registration was 213+/-197 s; automatic registration took 82+/-15 s. Registration was also performed without any human interaction. The resulting registration error of the algorithm without manual pre-registration was found to be 2.9+/-2.9 degrees and 1.1+/-0.2 mm. Here, a registration took 91+/-6 s, on average. Overall, the automated registration algorithm improved the accuracy of manual registration by 59% in rotation and 325% in translation. The absolute values are well within a clinically relevant range.

2D/3D registration of endoscopic ultrasound to CT volume data.

This paper describes a computer-aided navigation system using image fusion to support endoscopic interventions such as the accurate collection of biopsy specimens. An endoscope provides the physician with real-time ultrasound (US) and a video image. An image slice that corresponds to the corresponding image from the US scan head is derived from a preoperative computed tomography (CT) or magnetic resonance image volume data set using oblique reformatting and displayed side by side with the US image. The position of the image acquired by the US scan head is determined by a miniaturized electromagnetic tracking system (EMTS) after calibrating the endoscope's scan head. The transformation between the patient coordinate system and the preoperative data set is calculated using a 2D/3D registration. This is achieved by calibrating an intraoperative interventional CT slice with an optical tracking system (OTS) using the same algorithm as for the US calibration. The slice is then used for 2D/3D registration with the coordinate system of the preoperative volume. The fiducial registration error (FRE) for the US calibration was 2.0 mm +/- 0.4 mm; the interventional CT FRE was 0.36 +/- 0.12 mm; and the 2D/3D registration target registration error (TRE) was 1.8 +/- 0.3 mm. The point-to-point registration between the OTS and the EMTS had an FRE of 0.9 +/- 0.4 mm. Finally, we found an overall TRE for the complete system to be 3.9 +/- 0.6 mm.

Rigid 2D/3D slice-to-volume registration and its application on fluoroscopic CT images.

Registration of single slices from FluoroCT, CineMR, or interventional magnetic resonance imaging to three dimensional (3D) volumes is a special aspect of the two-dimensional (2D)/3D registration problem. Rather than digitally rendered radiographs (DRR), single 2D slice images obtained during interventional procedures are compared to oblique reformatted slices from a high resolution 3D scan. Due to the lack of perspective information and the different imaging geometry, convergence behavior differs significantly from 2D/3D registration applications comparing DRR images with conventional x-ray images. We have implemented a number of merit functions and local and global optimization algorithms for slice-to-volume registration of computed tomography (CT) and FluoroCT images. These methods were tested on phantom images derived from clinical scans for liver biopsies. Our results indicate that good registration accuracy in the range of 0.50 and 1.0 mm is achievable using simple cross correlation and repeated application of local optimization algorithms. Typically, a registration took approximately 1 min on a standard personal computer. Other merit functions such as pattern intensity or normalized mutual information did not perform as well as cross correlation in this initial evaluation. Furthermore, it appears as if the use of global optimization algorithms such as simulated annealing does not improve reliability or accuracy of the registration process. These findings were also confirmed in a preliminary registration study on five clinical scans. These experiments have, however, shown that a strict breath-hold protocol is inevitable when using rigid registration techniques for lesion localization in image-guided biopsy retrieval. Finally, further possible applications of slice-to-volume registration are discussed.

A fully automated calibration method for an optical see-through head-mounted operating microscope with variable zoom and focus.

Ever since the development of the first applications in image-guided therapy (IGT), the use of head-mounted displays (HMDs) was considered an important extension of existing IGT technologies. Several approaches to utilizing HMDs and modified medical devices for augmented reality (AR) visualization were implemented. These approaches include video-see through systems, semitransparent mirrors, modified endoscopes, and modified operating microscopes. Common to all these devices is the fact that a precise calibration between the display and three-dimensional coordinates in the patient's frame of reference is compulsory. In optical see-through devices based on complex optical systems such as operating microscopes or operating binoculars-as in the case of the system presented in this paper-this procedure can become increasingly difficult since precise camera calibration for every focus and zoom position is required. We present a method for fully automatic calibration of the operating binocular Varioscope M5 AR for the full range of zoom and focus settings available. Our method uses a special calibration pattern, a linear guide driven by a stepping motor, and special calibration software. The overlay error in the calibration plane was found to be 0.14-0.91 mm, which is less than 1% of the field of view. Using the motorized calibration rig as presented in the paper, we were also able to assess the dynamic latency when viewing augmentation graphics on a mobile target; spatial displacement due to latency was found to be in the range of 1.1-2.8 mm maximum, the disparity between the true object and its computed overlay represented latency of 0.1 s. We conclude that the automatic calibration method presented in this paper is sufficient in terms of accuracy and time requirements for standard uses of optical see-through systems in a clinical environment.