CT and MRI fusion for postimplant prostate brachytherapy evaluation

Postoperative evaluation of prostate brachytherapy is typically performed using CT, which does not have sufficient soft tissue contrast for accurate anatomy delineation. MR-CT fusion enables more accurate localization of both anatomy and implanted radioactive seeds, and hence, improves the accuracy of postoperative dosimetry. We propose a method for automatic registration of MR and CT images without a need for manual initialization. Our registration method employs a point-to-volume registration scheme during which localized seeds in the CT images, produced by commercial treatment planning systems as part of the standard of care, are rigidly registered to preprocessed MRI images. We tested our algorithm on ten patient data sets and achieved an overall registration error of 1.6 ± 0.8 mm with a running time of less than 20s. With high registration accuracy and computational speed, and no need for manual intervention, our method has the potential to be employed in clinical applications

3D motion tracking of pulmonary lesions using CT fluoroscopy images for robotically assisted lung biopsy

ABSTRACT We are developing a prototype system for robotically assisted lung biopsy. For directing the robot in biopsy needle placement, we propose a non-invasive algorithm to track the 3D position of the target lesion using 2D CT fluoroscopy image sequences. A small region of the CT fluoroscopy image is registered to a corresponding region in a pre-operative CT volume to infer the position of the target lesion with respect to the imaging plane. The registration is implemented in a coarse to fine fashion. The local deformation between the two regions is modeled by an affine transformation. The sum-of-squared-differences (SSD) between the two regions is minimized using the Levenberg-Marquardt method. Multiresolution and multi-start strategies are used to avoid local minima. As a result, multiple candidate transformations between the two regions are obtained, from which the true transformation is selected by similarity voting. The true transformation of each frame of the CT fluoroscopy image is then incorporated into a Kalman filter to predict the lesion's position for the next frame. Tests were completed to evaluate the performance of the algorithm using a respiratory motion simulator and a swine animal study

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning

We established a direct communication channel between Microsoft HoloLens 2 and 3D Slicer to exchange transform and image messages between the platforms in real time. This allows us to seamlessly display a CT reslice of a patient in the AR world.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Research supported by projects PI122/00601 and AC20/00102 (Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, Asociación Española Contra el Cáncer and European Regional Development Fund “Una manera de hacer Europa”), project PerPlanRT (ERA Permed), TED2021-129392B-I00 and TED2021-132200B-I00 (MCIN/AEI/10.13039/501100011033 and European Union “NextGenerationEU”/PRTR) and EU Horizon 2020 research and innovation programme Conex plus UC3M (grant agreement 801538). APC funded by Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)

Effects of Ultrasound Section-Thickness on Brachytherapy Needle Tip Localization Error

Abstract. Purpose: Ultrasound section-thickness is the out-of-plane beamwidth causing major roles in creating image artifacts normally appearing around the anechoic areas. These artifacts can introduce errors in localizing the needle tips during any ultrasound-guided procedure. To study how section-thickness and imaging parameters can affect observing and localizing needle tips, we have conducted a typical calibration setup experiment. Method: Multiple needles were inserted orthogonal to the axial image plane, at various distances from the transducer. The experiment was conducted on a brachytherapy stepper for a curvilinear transrectal-ultrasound probe. Result: Experiments demonstrated that the imaging parameters have direct impacts on observing needle tips at different axial locations. They suggest specific settings to minimize the imaging artifacts. Conclusion: The ultrasound section-thickness and side lobes could result in misjudgment of needle insertion depth in an ultrasound-guided procedure. A beam profile could assist in considering the likelihood of position errors, when the effects of side lobes are minimized

Multi-Modality Breast MRI Segmentation Using nn-UNet for Preoperative Planning of Robotic Surgery Navigation

Segmentation of the chest region and breast tissues is essential for surgery planning and navigation. This paper proposes the foundation for preoperative segmentation based on two cascaded architectures of deep neural networks (DNN) based on the state-of-the-art nnU-Net. Additionally, this study introduces a polyvinyl alcohol cryogel (PVA-C) breast phantom based on the segmentation of the DNN automated approach, enabling the experiments of navigation system for robotic breast surgery. Multi-modality breast MRI datasets of T2W and STIR images were acquired from 10 patients. Segmentation evaluation utilized the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. First, a single class labeling was used to segment the breast region. Then it was employed as an input for three-class labeling to segment fat, fibroglandular (FGT) tissues, and tumorous lesions. The first architecture has a 0.95 DCS, while the second has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively

Automated intraoperative calibration for prostate cancer brachytherapy

Purpose: Prostate cancer brachytherapy relies on an accurate spatial registration between the implant needles and the TRUS image, called "calibration". The authors propose a new device and a fast, automatic method to calibrate the brachytherapy system in the operating room, with instant error feedback. Methods: A device was CAD-designed and precision-engineered, which mechanically couples a calibration phantom with an exact replica of the standard brachytherapy template. From real-time TRUS images acquired from the calibration device and processed by the calibration system, the coordinate transformation between the brachytherapy template and the TRUS images was computed automatically. The system instantly generated a report of the target reconstruction accuracy based on the current calibration outcome. Results: Four types of validation tests were conducted. First, 50 independent, real-time calibration trials yielded an average of 0.57 6 0.13 mm line reconstruction error (LRE) relative to ground truth. Second, the averaged LRE was 0.37 6 0.25 mm relative to ground truth in tests with six different commercial TRUS scanners operating at similar imaging settings. Furthermore, testing with five different commercial stepper systems yielded an average of 0.29 6 0.16 mm LRE relative to ground truth. Finally, the system achieved an average of 0.56 6 0.27 mm target registration error (TRE) relative to ground truth in needle insertion tests through the template in a water tank. Conclusions: The proposed automatic, intraoperative calibration system for prostate cancer brachytherapy has achieved high accuracy, precision, and robustness

1.5 T augmented reality navigated interventional MRI: paravertebral sympathetic plexus injections

PURPOSE:The high contrast resolution and absent ionizing radiation of interventional magnetic resonance imaging (MRI) can be advantageous for paravertebral sympathetic nerve plexus injections. We assessed the feasibility and technical performance of MRI-guided paravertebral sympathetic injections utilizing augmented reality navigation and 1.5 T MRI scanner.METHODS:A total of 23 bilateral injections of the thoracic (8/23, 35%), lumbar (8/23, 35%), and hypogastric (7/23, 30%) paravertebral sympathetic plexus were prospectively planned in twelve human cadavers using a 1.5 Tesla (T) MRI scanner and augmented reality navigation system. MRI-conditional needles were used. Gadolinium-DTPA-enhanced saline was injected. Outcome variables included the number of control magnetic resonance images, target error of the needle tip, punctures of critical nontarget structures, distribution of the injected fluid, and procedure length.RESULTS: Augmented-reality navigated MRI guidance at 1.5 T provided detailed anatomical visualization for successful targeting of the paravertebral space, needle placement, and perineural paravertebral injections in 46 of 46 targets (100%). A mean of 2 images (range, 1–5 images) were required to control needle placement. Changes of the needle trajectory occurred in 9 of 46 targets (20%) and changes of needle advancement occurred in 6 of 46 targets (13%), which were statistically not related to spinal regions (P = 0.728 and P = 0.86, respectively) and cadaver sizes (P = 0.893 and P = 0.859, respectively). The mean error of the needle tip was 3.9±1.7 mm. There were no punctures of critical nontarget structures. The mean procedure length was 33±12 min.CONCLUSION:1.5 T augmented reality-navigated interventional MRI can provide accurate imaging guidance for perineural injections of the thoracic, lumbar, and hypogastric sympathetic plexus

Targeted prostate biopsy using statistical image analysis

Abstract-In this paper, a method for maximizing the probability of prostate cancer detection via biopsy is presented, by combining image analysis and optimization techniques. This method consists of three major steps. First, a statistical atlas of the spatial distribution of prostate cancer is constructed from histological images obtained from radical prostatectomy specimen. Second, a probabilistic optimization framework is employed to optimize the biopsy strategy, so that the probability of cancer detection is maximized under needle placement uncertainties. Finally, the optimized biopsy strategy generated in the atlas space is mapped to a specific patient space using an automated segmentation and elastic registration method. Cross-validation experiments showed that the predictive power of the optimized biopsy strategy for cancer detection reached the 94%-96% levels for 6-7 biopsy cores, which is significantly better than standard random-systematic biopsy protocols, thereby encouraging further investigation of optimized biopsy strategies in prospective clinical studies. Index Terms-Biopsy optimization, prostate cancer, spatial normalization, statistical image analysis

DCMQI: An open source library for standardized communication of quantitative image analysis results using DICOM

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi