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Electromagnetic (EM) tracking has been recognized as a valuable toolfor tracking the interventional devices in procedures such as lungand liver biopsy and ablation. The advantage of this technology overconventional X-ray fluoroscopy or CT-guided procedures is its real-time connection to the 3D volumetric roadmap of a patient’s anatomywhile the intervention is performed. EM-based guidance requires tracking of the tip of the interventional device, transforming the location of the device with pre-operative CT images, and superimposing the device in the 3D images to assist physician to complete the procedure more effectively. A key requirement of this integration of datais to find automatically the mapping between EM and CT coordinate systems. Thus, skin fiducial sensors are attached to patients before acquiring the pre-operative CTs. Then, those sensors can be recognized in both CT and EM coordinate systems to calculate the transformation matrix. In order to automate the EM-based navigation workflow andreduce procedural preparation time, an automatic fiducial detectionmethod is proposed to obtain the centroids of the sensors from thepre-operative CT. The approach has been applied to 13 rabbit datasets derived from an animal study, and numerical results show that it is a reliable and efficient method for use in EM-guided application.

Computed Tomography (CT) has been widely used for assisting lung cancer detection/diagnosis and treatment. In lung cancer diagnosis, suspect lesions or regions of interest (ROIs) are usually analyzed in screening CT scans, and CT-based image-guided minimally invasive procedures are performed for further diagnosis through bronchoscopic orpercutaneous approaches. Thus, ROI segmentation is a preliminary butvital step for abnormality detection, procedural planning, and intra-procedural guidance. In lung cancer diagnosis, such ROIs can be tumors, lymph nodes, nodules, etc. They may vary in size, shape, and other complication phenomena. Manual segmentation approaches are timeconsuming, user-biased, and cannot guarantee reproducible results.Automatic methods do not require user input, but they are usually highly application-dependent. To counterbalance among efficiency, accuracy, and robustness, considerable efforts have been contributed tosemi-automatic strategies, which enable full user control, while minimizing human interactions. Among available semi-automatic approaches, the live-wire algorithm has been recognized as a valuable tool for segmentation of a wide range of ROIs from chest CT images. In thispaper, the traditional 2D live-wire method is revisited and improved for 3D ROI segmentation. In the experiments, the proposed approachis applied to a set of anatomical ROIs from 3D chest CT images, andthe results are compared with the segmentation derived from previous evaluated live-wire-based approaches.

Purpose Prostate cancer (PCA) is the second most frequent cause of cancer-related death in men in the United States and Europe. Transrectal ultrasound (TRUS) guided systematic prostate biopsy is the standard of care for detection and diagnosis of PCA. However, due to inadequate visualization of PCA in ultrasound, the false negative rate of systematic TRUS-guided biopsy is 15-30%. In this work, a novel prostate guidance system using image fusion of live TRUS with pre-acquired prostate magnetic resonance imaging (MRI) is presented and validated in pre-clinical and clinical studies. Methods (A) Fusion guidance system: Endorectal ultrasound probe (Philips C9-5, Andover, MA,USA) biopsy guides were equipped with electromagnetic (EM) trackingsensors. The pose of the sensors was calibrated relative to the image area of the probe, enabling realtime spatial tracking of the TRUSimages. A software application was created supporting registrationof the live ultrasound with pre-acquired MRI using the following steps. (1) The prostate in the 3-dimensional (3D) MRI image was segmented using a semi-automatic segmentation tool. (2) A spatial sweep (base to apex in transverse view) across the prostate with the trackedTRUS probe was reconstructed into a 3D TRUS volume and was segmentedautomatically using adaptive local shape statistics. (3) Registration between the TRUS volume and the MRI volume was initialized basedon the known sweep geometry and the TRUS and MRI segmentations. (4)The TRUS-MRI registration was optimized interactively by combining manual manipulation of individual rotational and translational degrees of freedom (DOFs) with subsequent automatic optimization of the remaining DOFs using the iterative closest point (ICP) algorithm. (5)After registration, the live 2D TRUS image was shown side-by-side with the corresponding multi-planar reconstruction (MPR) of the MRI image. The MRI-based segmentation and any MRI-identified points of interest (POIs) were also superimposed on the live images. This workflow is illustrated in Figure 1. (B) Validation: The system and workflow were validated in phantom studies (as reported earlier), dog models, and clinical studies. In 5 dog models, MRI-visible but ultrasound-occult targets were created by injecting 1/32” synthetic ruby balls, diluted Gadolinium, or diluted Feridex® into the dog prostate. The fusion system was used to inject a secondary ruby or Feridex® marker in the MRI-identified target location. The spatial distance between the primary and secondary injections in subsequent MRI were usedto define the overall spatial accuracy. Figure 2 shows T1-weighted MRI slices of one of the injected targets in a dog prostate (white arrow) before and after the secondary injection, and the targeted injection of the secondary fiducial (black arrow). (C) Clinical study: In 203 patients with elevated prostate-specific antigen (PSA) or abnormal digital rectal exam (DRE), multi-parametric MRI (T2-weighted,diffusion-weighted, dynamic contrast-enhanced, and magnetic resonance spectroscopy) of the prostate was obtained on a 3 Tesla Philips Achieva (Andover, MA, USA) using an endorectal coil (BPX-30; Medrad, Pittsburgh, Pa, USA). The MRI was read by 2 radiologists and lesionssuspicious for PCA were identified. The MRI lesions were categorizedinto low, moderate and high suspicion based on the number of MRI sequences positive for that lesion (1-2 sequences positive: low; 3: moderate; 4: high). Subsequently, the patients underwent systematic 12-core TRUS-guided biopsy and TRUS-MRI fusion-targeted biopsy of MRI-identified suspicious lesions. All patients provided written informed consent. 10 patients were inevaluable because non-standard equipment was used or because of other reasons. The positive biopsy rates for systematic biopsy, targeted biopsy and for the combined approach(systematic + targeted) were compared, and were correlated with theMRI suspicion labels. Results In 5 dog prostates, a total of 10 target markers (2 Feridex, 4 Gadolinium and 4 synthetic ruby balls) and10 secondary fiducials (2 ruby balls, 8 Feridex injections) were injected. All 10 of the targets and 9 of the fiducials could be identified in follow-up MRI. The mean +- standard deviation of the distance between targets and secondary injections was 5.0 +- 2.5 mm. The clinical patient population had a mean age of 61.5 years (median 61, range 40 – 82) and a mean PSA of 8.5 ng/ml (mean 5.8, range 0.0 – 103.0). 133 patients had a prior prostate biopsy, of which 75 were biopsy. Figure 3 shows the fusion display provided for biopsy of an MRI-identified “high suspicion” target in a 67 year old male. In the study population there was a significant increase in the per-patient and per-core positive biopsy rates with increasing MRI suspicion level, for systematic as well as for targeted and combined biopsies. Also, targeted positive core rates were significantly (p<0.01) higher than systematic core rates in patients with moderate or high MRI-suspicion but were equivalent in patients with low suspicion (see Figure4). For high suspicion patients, the targeted positive core rate (46.5%) was more than double the systematic positive core rate (22.4%).Furthermore, in the patient group with moderate or high suspicion,the combined approach had significantly (p<0.05) higher per-patientpositive biopsy rates than systematic biopsy alone. Conclusions A system enabling MRI-targeted prostate biopsy by fusing pre-acquired MRI with live TRUS outside the MRI gantry was developed, validated, and clinically tested. The spatial accuracy of the fusion system wassufficient to target clinically significant prostate cancer. MRI-based cancer suspicion categories correlated well with biopsy-proven cancer detection rates, suggesting an important role of MRI in prostate cancer management. MRI fusion targeting significantly increased cancer detection rates in select patient groups, and may allow improved out-of-gantry management of patients with moderate or high MRI suspicion.