Intraoperative computed tomography and automated registration for image-guided cranial surgery

Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Today, image-guided systems play a significant role in improving the outcome of diagnostic and therapeutic interventions. They provide crucial anatomical information during the procedure to decrease the size and the extent of the approach, to reduce intraoperative complications, and to increase accuracy, repeatability, and safety. Image-to-patient registration is the first step in image-guided procedures. It establishes a correspondence between the patient's preoperative imaging and the intraoperative data. When it comes to the head-and-neck region, the presence of many sensitive structures such as the central nervous system or the neurosensory organs requires a millimetric precision. This review allows evaluating the characteristics and the performances of different registration methods in the head-and-neck region used in the operation room from the perspectives of accuracy, invasiveness, and processing times. Our work led to the conclusion that invasive marker-based methods are still considered as the gold standard of image-to-patient registration. The surface-based methods are recommended for faster procedures and applied on the surface tissues especially around the eyes. In the near future, computer vision technology is expected to enhance these systems by reducing human errors and cognitive load in the operating room.

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.

Tongue Contour Tracking and Segmentation in Lingual Ultrasound for Speech Recognition: A Review

Lingual ultrasound imaging is essential in linguistic research and speech recognition. It has been used widely in different applications as visual feedback to enhance language learning for non-native speakers, study speech-related disorders and remediation, articulation research and analysis, swallowing study, tongue 3D modelling, and silent speech interface. This article provides a comparative analysis and review based on quantitative and qualitative criteria of the two main streams of tongue contour segmentation from ultrasound images. The first stream utilizes traditional computer vision and image processing algorithms for tongue segmentation. The second stream uses machine and deep learning algorithms for tongue segmentation. The results show that tongue tracking using machine learning-based techniques is superior to traditional techniques, considering the performance and algorithm generalization ability. Meanwhile, traditional techniques are helpful for implementing interactive image segmentation to extract valuable features during training and postprocessing. We recommend using a hybrid approach to combine machine learning and traditional techniques to implement a real-time tongue segmentation tool.

Virtual extensions improve perception-based instrument alignment using optical see-through devices

Instrument alignment is a common task in various surgical interventions using navigation. The goal of the task is to position and orient an instrument as it has been planned preoperatively. To this end, surgeons rely on patient-specific data visualized on screens alongside preplanned trajectories. The purpose of this manuscript is to investigate the effect of instrument visualization/non visualization on alignment tasks, and to compare it with virtual extensions approach which augments the realistic representation of the instrument with simple 3D objects. 18 volunteers performed six alignment tasks under each of the following conditions: no visualization on the instrument; realistic visualization of the instrument; realistic visualization extended with virtual elements (Virtual extensions). The first condition represents an egocentric-based alignment while the two other conditions additionally make use of exocentric depth estimation to perform the alignment. The device used was a see-through device (Microsoft HoloLens 2). The positions of the head and the instrument were acquired during the experiment. Additionally, the users were asked to fill NASA-TLX and SUS forms for each condition. The results show that instrument visualization is essential for a good alignment using see-through devices. Moreover, virtual extensions helped achieve the best performance compared to the other conditions with medians of 2 mm and 2° positional and angular error respectively. Furthermore, the virtual extensions decreased the average head velocity while similarly reducing the frustration levels. Therefore, making use of virtual extensions could facilitate alignment tasks in augmented and virtual reality (AR/VR) environments, specifically in AR navigated surgical procedures when using optical see-through devices.

Reliable navigation registration in cranial and spine surgery based on intraoperative computed tomography

OBJECTIVE

Low registration errors are an important prerequisite for reliable navigation, independent of its use in cranial or spinal surgery. Regardless of whether navigation is used for trajectory alignment in biopsy or implant procedures, or for sophisticated augmented reality applications, all depend on a correct registration of patient space and image space. In contrast to fiducial, landmark, or surface matching–based registration, the application of intraoperative imaging allows user-independent automatic patient registration, which is less error prone. The authors’ aim in this paper was to give an overview of their experience using intraoperative CT (iCT) scanning for automatic registration with a focus on registration accuracy and radiation exposure.

METHODS

A total of 645 patients underwent iCT scanning with a 32-slice movable CT scanner in combination with navigation for trajectory alignment in biopsy and implantation procedures (n = 222) and for augmented reality (n = 437) in cranial and spine procedures (347 craniotomies and 42 transsphenoidal, 56 frameless stereotactic, 59 frame-based stereotactic, and 141 spinal procedures). The target registration error was measured using skin fiducials that were not part of the registration procedure. The effective dose was calculated by multiplying the dose length product with conversion factors.

RESULTS

Among all 1281 iCT scans obtained, 1172 were used for automatic patient registration (645 initial registration scans and 527 repeat iCT scans). The overall mean target registration error was 0.86 ± 0.38 mm (± SD) (craniotomy, 0.88 ± 0.39 mm; transsphenoidal, 0.92 ± 0.39 mm; frameless, 0.74 ± 0.39 mm; frame-based, 0.84 ± 0.34 mm; and spinal, 0.80 ± 0.28 mm). Compared with standard diagnostic scans, a distinct reduction of the effective dose could be achieved using low-dose protocols for the initial registration scan with mean effective doses of 0.06 ± 0.04 mSv for cranial, 0.50 ± 0.09 mSv for cervical, 4.12 ± 2.13 mSv for thoracic, and 3.37 ± 0.93 mSv for lumbar scans without impeding registration accuracy.

CONCLUSIONS

Reliable automatic patient registration can be achieved using iCT scanning. Low-dose protocols ensured a low radiation exposure for the patient. Low-dose scanning had no negative effect on navigation accuracy.

Development of mobile intraoperative computed tomography imaging system and assessment of its performance in a brain and body phantom study

BACKGROUND

Intraoperative computed tomography (iCT) system has been developed focusing on combining the advanced imaging techniques for the best imaging modality. However, the use of iCT system in the operating rooms is limited due to the lack of flexible mobility.

OBJECTIVE

This study aims to develop a mobile iCT imaging system and assess its imaging performance in a phantom study.

METHODS

The mobile iCT system with mecanum omni-directional wheels has three major components namely, a rotating gantry, a slip-ring and a stationary gantry. Performance of mecanum iCT system was evaluated using the indices of signal-to-noise (SNR), contrast-to noise (CNR), and spatial resolution (MTF). Anatomical landmarks on phantom images were assessed using a 5-point scale (5 = definitely seen; 4 = probably seen; 3 = equivocal; 2 = probably not seen; and 1 = definitely not seen).

RESULTS

The mecanum iCT system can be conveniently used for a whole-body scan under intraoperative conditions even in narrow operating rooms due to a smaller turning radius. The image quality of the mecanum iCT system was found to be acceptable for clinical applications (with SNR = 162.72, CNR = 134.29 and MTF = 694 μm). The diagnostic scores on the phantom images were 'definitely seen' value.

CONCLUSIONS

The proposed mecanum iCT system achieved the improved flexible mobility and has potential to better serve as a useful imaging tool in the clinical intraoperative setting.

Intraoperative computed tomography as reliable navigation registration device in 200 cranial procedures

BACKGROUND

Registration accuracy is a main factor influencing overall navigation accuracy. Standard fiducial- or landmark-based patient registration is user dependent and error-prone. Intraoperative imaging offers the possibility for user-independent patient registration. The aim of this paper is to evaluate our initial experience applying intraoperative computed tomography (CT) for navigation registration in cranial neurosurgery, with a special focus on registration accuracy and effective radiation dose.

METHODS

A total of 200 patients (141 craniotomy, 19 transsphenoidal, and 40 stereotactic burr hole procedures) were investigated by intraoperative CT applying a 32-slice movable CT scanner, which was used for automatic navigation registration. Registration accuracy was measured by at least three skin fiducials that were not part of the registration process.

RESULTS

Automatic registration resulted in high registration accuracy (mean registration error: 0.93 ± 0.41 mm). Implementation of low-dose scanning protocols did not impede registration accuracy (registration error applying the full dose head protocol: 0.87 ± 0.36 mm vs. the low dose sinus protocol 0.72 ± 0.43 mm) while a reduction of the effective radiation dose by a factor of 8 could be achieved (mean effective radiation dose head protocol: 2.73 mSv vs. sinus protocol: 0.34 mSv).

CONCLUSION

Intraoperative CT allows highly reliable navigation registration with low radiation exposure.

In Vivo Near Infrared Virtual Intraoperative Surgical Photoacoustic Optical Coherence Tomography

Since its first implementation in otolaryngological surgery nearly a century ago, the surgical microscope has improved the accuracy and the safety of microsurgeries. However, the microscope shows only a magnified surface view of the surgical region. To overcome this limitation, either optical coherence tomography (OCT) or photoacoustic microscopy (PAM) has been independently combined with conventional surgical microscope. Herein, we present a near-infrared virtual intraoperative photoacoustic optical coherence tomography (NIR-VISPAOCT) system that combines both PAM and OCT with a conventional surgical microscope. Using optical scattering and absorption, the NIR-VISPAOCT system simultaneously provides surgeons with real-time comprehensive biological information such as tumor margins, tissue structure, and a magnified view of the region of interest. Moreover, by utilizing a miniaturized beam projector, it can back-project 2D cross-sectional PAM and OCT images onto the microscopic view plane. In this way, both microscopic and cross-sectional PAM and OCT images are concurrently displayed on the ocular lens of the microscope. To verify the usability of the NIR-VISPAOCT system, we demonstrate simulated surgeries, including in vivo image-guided melanoma resection surgery and in vivo needle injection of carbon particles into a mouse thigh. The proposed NIR-VISPAOCT system has potential applications in neurosurgery, ophthalmological surgery, and other microsurgeries.

Efficient methods for implementation of multi-level nonrigid mass-preserving image registration on GPUs and multi-threaded CPUs

BACKGROUND AND OBJECTIVE

Faster and more accurate methods for registration of images are important for research involved in conducting population-based studies that utilize medical imaging, as well as improvements for use in clinical applications. We present a novel computation- and memory-efficient multi-level method on graphics processing units (GPU) for performing registration of two computed tomography (CT) volumetric lung images.

METHODS

We developed a computation- and memory-efficient Diffeomorphic Multi-level B-Spline Transform Composite (DMTC) method to implement nonrigid mass-preserving registration of two CT lung images on GPU. The framework consists of a hierarchy of B-Spline control grids of increasing resolution. A similarity criterion known as the sum of squared tissue volume difference (SSTVD) was adopted to preserve lung tissue mass. The use of SSTVD consists of the calculation of the tissue volume, the Jacobian, and their derivatives, which makes its implementation on GPU challenging due to memory constraints. The use of the DMTC method enabled reduced computation and memory storage of variables with minimal communication between GPU and Central Processing Unit (CPU) due to ability to pre-compute values. The method was assessed on six healthy human subjects.

RESULTS

Resultant GPU-generated displacement fields were compared against the previously validated CPU counterpart fields, showing good agreement with an average normalized root mean square error (nRMS) of 0.044±0.015. Runtime and performance speedup are compared between single-threaded CPU, multi-threaded CPU, and GPU algorithms. Best performance speedup occurs at the highest resolution in the GPU implementation for the SSTVD cost and cost gradient computations, with a speedup of 112 times that of the single-threaded CPU version and 11 times over the twelve-threaded version when considering average time per iteration using a Nvidia Tesla K20X GPU.

CONCLUSIONS

The proposed GPU-based DMTC method outperforms its multi-threaded CPU version in terms of runtime. Total registration time reduced runtime to 2.9min on the GPU version, compared to 12.8min on twelve-threaded CPU version and 112.5min on a single-threaded CPU. Furthermore, the GPU implementation discussed in this work can be adapted for use of other cost functions that require calculation of the first derivatives.

Increased 68Ga-DOTATATE uptake in PET imaging discriminates meningioma and tumor-free tissue

Meningiomas are known to express somatostatin receptor 2 (SSTR2). PET using the SSTR2 analog (68)Ga-DOTATATE has recently been introduced for imaging of meningiomas. However, a systematic correlation between (68)Ga-DOTATATE uptake, SSTR2 expression, and histology (including tumor-free scar tissue) is still lacking. For elucidation, we conducted this prospective study.

METHODS

Twenty-one adult patients with primary (n = 12) or recurrent (n = 9) meningiomas were prospectively enrolled. Preoperative MR imaging and (68)Ga-DOTATATE PET scans were fused and used for a spatially precise neuronavigated tissue-sampling procedure during tumor resection. Histopathologic diagnosis included immunohistochemical determination of SSTR2 expression. At each individual sampling site, the maximum standardized uptake value (SUVmax) of (68)Ga-DOTATATE was correlated with MR imaging findings, histology, and semiquantitative SSTR2 expression.

RESULTS

One hundred fifteen samples (81 tumor, 34 tumor-free) were obtained. There was a significant positive correlation between SUVmax and SSTR2 expression. Receiver-operating characteristic analysis revealed a threshold of 2.3 for SUVmax to discriminate between tumor and nontumoral tissue. Regarding the detection of tumor tissue, PET imaging showed a higher sensitivity (90% vs. 79%; P = 0.049), with specificity and positive predictive values similar to MR imaging, for both de novo and recurrent tumors.

CONCLUSION

(68)Ga-DOTATATE uptake correlates with SSTR2 expression and offers high diagnostic accuracy to delineate meningioma from tumor-free tissue even in recurrent tumors after previous therapy. Our findings substantiate an important role for (68)Ga-DOTATATE PET in meningioma management.

Computer-assisted virtual autopsy using surgical navigation techniques

OBJECTIVE; Virtual autopsy methods, such as postmortem CT and MRI, are increasingly being used in forensic medicine. Forensic investigators with little to no training in diagnostic radiology and medical laypeople such as state's attorneys often find it difficult to understand the anatomic orientation of axial postmortem CT images. We present a computer-assisted system that permits postmortem CT datasets to be quickly and intuitively resliced in real time at the body to narrow the gap between radiologic imaging and autopsy.

CONCLUSION

Our system is a potentially valuable tool for planning autopsies, showing findings to medical laypeople, and teaching CT anatomy, thus further closing the gap between radiology and forensic pathology.

[New developments in computer-assisted surgery (CAS). From intraoperative imaging to ultrasound-based navigation]

Ever faster processor capacity is having an impact on computer-assisted or computer-aided surgery (CAS). The fusion of different imaging modalities enables functional data such as PET-CT, for example, to be available in image-guided surgery. Referencing of image data is the key to precise navigation. Intraoperative data acquisition is a new approach to improving accuracy. Thus, intraoperative CT conducted under navigational support enables automatic referencing of up-to-date image data. Alternatively, intraoperative magnetic resonance imaging or intraoperative sonography can be performed. Ultrasound systems have already been successfully integrated in existing navigational systems to compensate for intraoperative tissue shifting. Ultrasound systems may play a role in the future as a single modality in image-guided surgery in soft tissue of the neck and skull bone.

The future of surgical orthopaedics of the knee

Over the past two decades, orthopaedics has gone through major changes, principally in the surgical treatment options for articular defects of the knee. This paper explores the advantages and shortcomings of the current surgical treatment modalities for cartilaginous defects in the knee. Emphasis is placed on current techniques in knee arthroplasty, including a view on the future of orthopaedic knee surgery.