Real-time opto-electronic verification of patient position in breast cancer radiotherapy.

OBJECTIVE: The clinical application of an opto-electronic system for real-time three-dimensional (3D) control of patient position in breast cancer radiotherapy is described. The specific features of the motion analysis technology (shape recognition of passive markers) are detailed, and the outcomes of its clinical use for quantitative position control and immobility verification of the thoracic irradiation field during breast cancer treatment are reported. MATERIALS AND METHODS :The position control system is based on the ELITEtrade mark opto-electronic motion analyzer, which provides in real time the 3D coordinates of a set of passive markers (plastic hemispheres 3 mm in diameter) previously placed on selected landmarks on the patient's skin. The system-dedicated hardware performs marker recognition by means of 2D correlation of shape with a predefined marker modeling mask. This feature ensures a high accuracy, even with small marker dimensions, and successful analysis in a noisy environment (due to room light, reflexes, etc.). The patient repositioning control was based on a comparison between the current positions of the markers and a corresponding reference configuration. The resulting marker displacements were graphically displayed in real time for immediate control. This information was not provided to the operator as a repositioning tool. Instead, the kinematic data was stored for subsequent off-line analysis aimed at quantifying the different factors contributing to patient mis-positioning (initial repositioning errors, patient's breathing, and random movements) when conventional means for patient alignment (laser centering) and immobilization (casting techniques) are used. RESULTS: Clinical application of the system revealed median 3D localization errors for the directly controlled anatomical landmarks of around 4.5 mm. This value is proposed to represent the intrinsic accuracy of conventional laser-centering techniques in breast cancer radiotherapy, including the effects of patient body deformations. When the positional inaccuracies introduced by patients' respiration were also considered, the extent of the resulting 3D mis-positioning of the control points increased to median values of up to 8 mm. CONCLUSIONS: The reported clinical trial confirms the significant role that real-time opto-electronic motion analysis based on passive markers can have in augmenting the accuracy of patient repositioning and immobility verification in the radiotherapy of a non-rigid body area while also accounting for physiological movements. Evaluation of the data collected during each irradiation session for five patients provided valuable information concerning the optimization of the efficacy of traditional methods for patient centering and immobilization.     

Soft tissue scanning for patient registration in image-guided surgery.

Prior to an image-guided surgical intervention, a correlation between the patient's data set and the surgical site is required. This study introduces a markerless registration method for cranio-maxillofacial surgery that is based on a high-resolution laser scan of the patient's skin surface. The Surgical Segment Navigator SSN++ rejects contaminated surface measurements in a way similar to the bluescreen technique. Acquisition of the spatial position and the corresponding surface color of each laser-scanned point facilitates this bluescreen method, removing points with a defined surface color, e.g., blue or green points. The accuracy of the laser-scan-based registration was measured via additional intraoral titanium-markers. These markers served only to check the accuracy of the markerless registration process. In twelve patients, the stability and accuracy of the data set alignment was evaluated for high-(300,000 surface points), medium-, and low-resolution (down to 3,750 surface points) laser scanning. The accuracy of the registration technique was best for high-resolution laser scanning (mean deviation 1.1 mm; maximum deviation 1.8 mm). Low-resolution laser scans revealed inaccuracies up to 6 mm.     

Automated fiducial marker detection for patient registration in image-guided neurosurgery.

OBJECTIVE: The registration of applied fiducial markers within the preoperative data is often left to the surgeon, who has to identify and tag the center of each marker. This is both time-consuming and a potential source of error. For this reason, the development of an automated procedure was desirable. In this study, we have investigated the accuracy of a software algorithm for detecting fiducial markers within the navigation data set. The influence of adjustable values for accuracy and threshold on the sensitivity and specificity of the detection process, as well as the time gain, was investigated. PATIENTS AND METHODS: One hundred MP-RAGE MRI data sets of patients with different pathologies who were scheduled for image-guided surgery were used in this study. A total of 591 applied fiducial markers were to be detected using the algorithm of the software VVPlanning 1.3 (BrainLAB, Heimstetten, Germany) on a Pentium II standard PC. The size value of a marker in the y-direction is called "accuracy" and depends on the slice thickness. "Threshold" describes the gray level above which the algorithm starts searching for pixel clusters. The threshold value was changed stepwise on the basis of a constant "accuracy" value. The "accuracy" value was changed on the basis of that threshold value at which all markers were detected correctly. RESULTS: The time needed for automatic detection varied between 12 s and 25 s. An optimum value for adjustable marker size was found to be 1.1 mm, with 8 undetected markers (1.35%) and 7 additionally detected structures (1.18%) out of 591. The mean gray level (Threshold) for all data sets above which marker detection was correct was 248.9. The automatic detection of markers was good for higher gray levels, with 11 missed markers (1.86%). Starting the algorithm at lower gray levels led to a decreased incidence of missed markers (0.17%), but increased the incidence of additionally detected structures to 27.92%. CONCLUSION: The automatic marker-detection algorithm is a robust, fast and objective instrument for reliable fiducial marker registration when used with optimum settings for both threshold and accuracy.     

A new patient registration method for intensive care department management

A new method to describe intensive care department performance is presented. The method is a complication of available administrative and medical data, completed with a severity of illness measure (Acute Physiology And Chronic Health Evaluation, APACHE) and the registration of nursing care intensity. The development of this latter patient stratification system (Intensive Care Activity Score, INCAS) is described. The performance of the method is demonstrated by a study of 200 consecutive admissions.     

High-resolution laser surface scanning for patient registration in cranial computer-assisted surgery.

OBJECTIVE: Markerless patient registration is a new procedure that may reduce logistical efforts and possibly also the radiation load on the patients prior to a computer-assisted intervention. Congruent surfaces, such as bone surfaces or skin surfaces, represented in a data set and in the surgical site, can be overlapped using surface-matching. Previous studies describing this kind of markerless registration, however, show inaccuracies of up to 10 mm during computer-assisted navigation. Furthermore, these systems use less than 1000 surface points of the soft tissue surface in order to establish a correlation between the patient and the data set. Previous papers did not answer the question whether it is this scanning resolution that induces these inaccuracies in registration or rather intraoperative skin deformations. STUDY DESIGN: In the present study therefore a new navigation system (SSN++) was used which is able to register up to 180,000 surface points of the surgical site. SSN++ is an infrared navigation system enlarged by a Minolta VI 900 3D volume digitizer. Three different kinds of laser scan-resolution were used for data correlation. An additional congruence analysis was performed in order to assess the geometry of the matched skin surfaces. 22 patients suffering from different cranial diseases (tumors, bony malformations, foreign bodies) were prepared for a computer-assisted intervention. Intraoral titanium-markers, rigidly fixed on the patients by a maxillary splint, were placed as targets while the CT data sets were made. These targets were - after markerless laser scan registration of the patients - supposed to serve for validating the new high-resolution navigation system SSN++. RESULTS: The accuracy of markerless laser scan registration depends on the intraoperative laser scan's resolution. A high accuracy of the data correlation can be achieved if the number of the laser scan cloud points is about the same as the number of voxels of the corresponding surface on the CT data set. A reduction of the laser scan cloud points to less than 10 % compared to the number of voxels of the CT surface, however, leads to a significant loss of accuracy after markerless patient registration. CONCLUSION: The markerless laser scan registration of the surgical site may achieve the same accuracy as a patient registration made by rigidly fixed titanium screws (mean accuracy: 1.2 mm) as long as a high-resolution laser scan is being used.     

A surface-matching technique for robot-assisted registration.

Successful implementation of robot-assisted surgery (RAS) requires coherent integration of spatial image data with sensing and actuating devices, each having its own coordinate system. Hence, accurate estimation of the geometric relationships between relevant reference frames, known as registration, is a crucial procedure in all RAS applications. The purpose of this paper is to present a new registration scheme, along with the results of an experimental evaluation of a robot-assisted registration method for RAS applications in orthopedics. The accuracy of the proposed registration is appropriate for specified orthopedic surgical applications such as Total Knee Replacement. The registration method is based on a surface-matching algorithm that does not require marker implants, thereby reducing surgical invasiveness. Points on the bone surface are sampled by the robot, which in turn directs the surgical tool. This technique eliminates additional coordinate transformations to an external device (such as a digitizer), resulting in increased surgical accuracy. The registration technique was tested on an RSPR six-degrees-of-freedom parallel robot specifically designed for medical applications. A six-axis force sensor attached to the robot's moving platform enables fast and accurate acquisition of positions and surface normal directions at sampled points. Sampling with a robot probe was shown to be accurate, fast, and easy to perform. The whole procedure takes about 2 min, with the robot performing most of the registration procedures, leaving the surgeon's hands free. Robotic registration was shown to provide a flawless link between preoperative planning and robotic assistance during surgery.     

Laser surface scanning for patient registration in intracranial image-guided surgery

OBJECTIVE: To report our clinical experience with a new laser scanning-based technique of surface registration. We performed a prospective study to measure the calculated registration error and the application accuracy of laser surface registration for intracranial image-guided surgery in the clinical setting. METHODS: Thirty-four consecutive patients with different intracranial diseases were scheduled for intracranial image-guided surgery by use of a passive infrared surgical navigation system. Surface registration was performed by use of a Class I laser device that emits a visible laser beam. The Polaris camera system (Northern Digital, Waterloo, ON, Canada) detects the skin reflections of the laser, which the software uses to generate a virtual three-dimensional matrix of the anatomy of each patient. An advanced surface-matching algorithm then matches this virtual three-dimensional matrix to the three-dimensional magnetic resonance therapy data set. Registration error as calculated by the computer was noted. Application accuracy was assessed by use of the localization error for three distant anatomic landmarks. RESULTS: Laser surface registration was successful in all patients. For the surgical field, application accuracy was 2.4 +/- 1.7 mm (range, 1-9 mm). Application accuracy was higher for the surgical field of frontally located lesions (mean, 1.8 +/- 0.8 mm; n = 13) as compared with temporal, parietal, occipital, and infratentorial lesions (mean, 2.8 +/- 2.1 mm; n = 21). CONCLUSION: Laser scanning for surface registration is an accurate, robust, and easy-to-use method of patient registration for image-guided surgery.     

Online head motion tracking applied to the patient registration problem.

Image-guided systems for surgical procedures in the region of the head require a method to correlate the diagnostic image data with the corresponding site of pathology in the patient. Considering that patient movement can occur, detection and correction of such movement errors during the acquisition of images is a basic prerequisite for accurate treatment. For this reason, we developed a new registration method based upon on-line tracking of the patient's head to solve the problem of registration in the presence of head motion. The method provides non-invasive active patient registration for correction of movements during imaging and continuous update of the patient's head position during surgery. The patient motion correction applies the rigid body model to register the images using feature correspondence. The new registration method is described, and results of experiments that were performed to evaluate its accuracy and reliability in a plastic skull model and in patients are presented. The error analysis resulted in a final target registration error of 0.90 mm +/- 0.16 mm using experimental model data and 1.58 mm +/- 0.26 mm using clinical patient data. In addition, the residual registration error is modeled as a function of the measured and predicted head motion in order to determine the error that is introduced by motion tracking during image data acquisition. Furthermore, the clinical application of the method is demonstrated for oto-, rhino-, and neurosurgical procedures in the region of the head.     

Spectral entropy measurement of patient responsiveness during propofol and remifentanil. A comparison with the bispectral index

Background. We compared two spectral entropies, state entropy(SE) and response entropy (RE), based on the irregularity ofthe EEG, to measure loss of response to verbal command (LOR_verbal)and noxious stimulus (LOR_noxious) with the bispectral index(BIS) during propofol infusion with and without remifentanil. Methods. Three groups of 20 patients received an effect-sitecontrolled propofol infusion (Ce_PROP) starting at 1 µgml^–1 and increased in steps of 0.5 µg ml^–1at 4 min intervals. In addition, a remifentanil infusion wasmaintained at a group-dependent, fixed effect-site target concentration(Ce_REMI) (0, 2 or 4 ng ml^–1). The ability of BIS, SE orRE to predict LOR_verbal and LOR_noxious were compared with thechanges in BIS, SE and RE using logistic regression, predictionprobability (P_K), and sensitivity/specificity. Results. In all groups, BIS, SE and RE decreased with increasingCe_PROP. However, BIS decreased more smoothly than SE and REat deeper levels of sedation. At LOR_verbal, BIS₅₀, SE₅₀ andRE₅₀ increased with increasing Ce_REMI. BIS, SE and RE all detectedLOR_verbal accurately but BIS performed better at 100% sensitivity.Sensitivity/specificity for detection of LOR_verbal decreasedfor all methods with increasing Ce_REMI. LOR_noxious was poorlydescribed by all measures. Conclusion. LOR_verbal was detected accurately by BIS, SE andRE except for 100% sensitivity, where BIS performed better.Though BIS, SE and RE were influenced by remifentanil duringpropofol administration, their ability to detect LOR_verbal remainedaccurate. None of the measures predicted LOR_noxious.      

Study on esophageal cancer radiotherapy dosimetry and position verification for volumetric modulated arc therapy

BackgroundThis study analyzed the respective advantages and disadvantages by comparing volumetric modulated arc therapy (VMAT) and intensity modulated radiotherapy (IMRT) on the dose distribution and position verification distribution characteristics in esophageal cancer radiotherapy, in order to provide the reference for the clinical radiotherapy technology optimization of esophageal cancer.MethodsA total of 56 cases of patients with esophageal cancer were selected and applied to the Pinnacle three-dimensional radiation treatment planning system (TPS), in order to design a VMAT plan and IMRT plan under the guidance of image-guided radiotherapy (IGRT). The dosimetry and position verification difference were compared between the two groups.ResultsRevealed that the target dose distribution of the VMAT plan and IMRT plan meets the requirements in clinical dosimetry for all 56 patients in this study. Under the premise of similar target coverage, the conformal index (CI) of the VMAT plan, homogeneity index (HI), target volume, BODY-PTV radiated volume and spinal cord D_max, bilateral lung V5, V20 and mean lung dose (MLD), monitor unit (MU) and treatment time (TT), as well as position verification and others, were obviously superior to those in the IMRT plan; and the difference was statistically significant.ConclusionCBCT guided VMAT is a potential effective treatment for esophageal cancer and may be more effective and safer than IMRT.     

A comparison of registration techniques for computer- and image-assisted elbow surgery.

Optimal function following elbow replacement surgery is dependent on the accurate replication of the elbow's flexion-extension axis. Currently, position and orientation of the axis are estimated from visual landmarks. In order to develop computer-assisted techniques to more accurately define this axis, a surface-based registration technique employing a hand-held laser scanner was evaluated against a conventional paired-point registration method to determine whether it produced improved alignment of the flexion-extension axis of the elbow. Registration error was 0.8 +/- 0.3 mm for surface-based registration, compared with 1.9 +/- 1.0 mm for the conventional registration method. These results suggest that the implementation of a surface-based registration technique may lead to a more accurate axis determination and improved clinical outcomes following elbow replacement surgery.     

Three new primary pelvic carcinomas in a patient following radiotherapy for carcinoma of the cervix. Case report

A 67-year-old woman developed three separate pelvic malignancies forty years after undergoing radiotherapy for a squamous cell carcinoma of the cervix. Although previous exposure to ionising radiation is associated with certain malignancies, review of the literature indicates that there is no strong evidence to support such an association with urothelial or colorectal malignancy.