Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Real-time motion monitoring using orthogonal cine MRI during MR-guided adaptive radiation therapy for abdominal tumors on 1.5T MR-Linac.

BACKGROUND: Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT). PURPOSE: Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac. METHODS: A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline). The MRI data acquired under free-breathing during the routine MRgART on a 1.5T MR-Linac for 18 patients with abdominal malignancies of 8 liver, 4 adrenal glands (renal fossa), and 6 pancreas cases were used to evaluate the MMRP package. For each patient, a 3D mid-position image derived from an in-house daily 4D-MRI was used to define a target mask or a surrogate sub-region encompassing the target. Additionally, an exploratory case reviewed for an MRI dataset of a healthy volunteer acquired under both free-breathing and deep inspiration breath-hold (DIBH) was used to test how effectively the RTMM using the MMRP can address through-plane motion (TPM). For all cases, the 2D T2/T1-weighted cine MRIs were captured with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. Manually delineated contours on the cine frames were used as the ground-truth motion. Common visible vessels and segments of target boundaries in proximity to the target were used as anatomical landmarks for reproducible delineations on both the 3D and the cine MRI images. Standard deviation of the error (SDE) between the ground-truth and the measured target motion from the MMRP package were analyzed to evaluate the RTMM accuracy. The maximum target motion (MTM) was measured on the 4D-MRI for all cases during free-breathing. RESULTS: The mean (range) centroid motions for the 13 abdominal tumor cases were 7.69 (4.71-11.15), 1.73 (0.81-3.05), and 2.71 (1.45-3.93) mm with an overall accuracy of <2 mm in the superior-inferior (SI), the left-right (LR), and the anterior-posterior (AP) directions, respectively. The mean (range) of the MTM from the 4D-MRI was 7.38 (2-11) mm in the SI direction, smaller than the monitored motion of centroid, demonstrating the importance of the real-time motion capture. For the remaining patient cases, the ground-truth delineation was challenging under free-breathing due to the target deformation and the large TPM in the AP direction, the implant-induced image artifacts, and/or the suboptimal image plane selection. These cases were evaluated based on visual assessment. For the healthy volunteer, the TPM of the target was significant under free-breathing which degraded the RTMM accuracy. RTMM accuracy of <2 mm was achieved under DIBH, indicating DIBH is an effective method to address large TPM. CONCLUSIONS: We have successfully developed and tested the use of a template-based registration method for an accurate RTMM of abdominal targets during MRgART on a 1.5T MR-Linac without using injected contrast agents or radio-opaque implants. DIBH may be used to effectively reduce or eliminate TPM of abdominal targets during RTMM.

Feasibility of real-time motion tracking using cine MRI during MR-guided radiation therapy for abdominal targets.

PURPOSE: Real-time high soft-tissue contrast magnetic resonance imaging (MRI) from the MR-Linac offers the best opportunity for accurate motion tracking during radiation therapy delivery via high-frequency two-dimensional (2D) cine imaging. This work investigates the efficacy of real-time organ motion tracking based on the registration of MRI acquired on MR-Linac. METHODS: Algorithms based on image intensity were developed to determine the three-dimensional (3D) translation of abdominal targets. 2D and 3D abdominal MRIs were acquired for 10 healthy volunteers using a high-field MR-Linac. For each volunteer, 3D respiration-gated T2 and 2D T2/T1-weighted cine in sagittal, coronal, and axial planes with a planar temporal resolution of 0.6 for 60 s was captured. Datasets were also collected on MR-compatible physical and virtual four-dimensional (4D) motion phantoms. Target contours for the liver and pancreas from the 3D T2 were populated to the cine and assumed as the ground-truth motion. We performed image registration using a research software to track the target 3D motion. Standard deviations of the error (SDE) between the ground-truth and tracking were analyzed. RESULTS: Algorithms using a research software were demonstrated to be capable of tracking arbitrary targets in the abdomen at 5 Hz with an overall accuracy of 0.6 mm in phantom studies and 2.1 mm in volunteers. However, this value is subject to patient-specific considerations, namely motion amplitude. Calculation times of < 50 ms provide a pathway of real-time motion tracking integration. A major challenge in using 2D cine MRI to track the target is handling the full 3D motion of the target. CONCLUSIONS: Feasibility to track organ motion using intensity-based registration of MRIs was demonstrated for abdominal targets. Tracking accuracy of about 2 mm was achieved for the motion of the liver and pancreatic head for typical patient motion. Further development is ongoing to improve the tracking algorithm for large and complex motions.

A dataset of multi-contrast population-averaged brain MRI atlases of a Parkinson׳s disease cohort.

Parkinson׳s disease (PD) is a neurodegenerative disease that primarily affects the motor functions of the patients. Research and surgical treatment of PD (e.g., deep brain stimulation) often require human brain atlases for structural identification or as references for anatomical normalization. However, two pitfalls exist for many current atlases used for PD. First, most atlases do not represent the disease-specific anatomy as they are based on healthy young subjects. Second, subcortical structures, such as the subthalamic nucleus (STN) used in deep brain stimulation procedures, are often not well visualized. The dataset described in this Data in Brief is a population-averaged atlas that was made with 3 T MRI scans of 25 PD patients, and contains 5 image contrasts: T1w (FLASH & MPRAGE), T2*w, T1-T2* fusion, phase, and an R2* map. While the T1w, T2*w, and T1-T2* fusion templates provide excellent anatomical details for both cortical and sub-cortical structures, the phase and R2* map contain bio-chemical features. Probabilistic tissue maps of whiter matter, grey matter, and cerebrospinal fluid are provided for the atlas. We also manually segmented eight subcortical structures: caudate nucleus, putamen, globus pallidus internus and externus (GPi & GPe), thalamus, STN, substantia nigra (SN), and the red nucleus (RN). Lastly, a co-registered histology-derived digitized atlas containing 123 anatomical structures is included. The dataset is made freely available at the MNI data repository accessible through the link http://nist.mni.mcgill.ca/?p=1209.

IBIS: an OR ready open-source platform for image-guided neurosurgery.

PURPOSE: Navigation systems commonly used in neurosurgery suffer from two main drawbacks: (1) their accuracy degrades over the course of the operation and (2) they require the surgeon to mentally map images from the monitor to the patient. In this paper, we introduce the Intraoperative Brain Imaging System (IBIS), an open-source image-guided neurosurgery research platform that implements a novel workflow where navigation accuracy is improved using tracked intraoperative ultrasound (iUS) and the visualization of navigation information is facilitated through the use of augmented reality (AR). METHODS: The IBIS platform allows a surgeon to capture tracked iUS images and use them to automatically update preoperative patient models and plans through fast GPU-based reconstruction and registration methods. Navigation, resection and iUS-based brain shift correction can all be performed using an AR view. IBIS has an intuitive graphical user interface for the calibration of a US probe, a surgical pointer as well as video devices used for AR (e.g., a surgical microscope). RESULTS: The components of IBIS have been validated in the laboratory and evaluated in the operating room. Image-to-patient registration accuracy is on the order of [Formula: see text] and can be improved with iUS to a median target registration error of 2.54 mm. The accuracy of the US probe calibration is between 0.49 and 0.82 mm. The average reprojection error of the AR system is [Formula: see text]. The system has been used in the operating room for various types of surgery, including brain tumor resection, vascular neurosurgery, spine surgery and DBS electrode implantation. CONCLUSIONS: The IBIS platform is a validated system that allows researchers to quickly bring the results of their work into the operating room for evaluation. It is the first open-source navigation system to provide a complete solution for AR visualization.

Automatic SWI Venography Segmentation Using Conditional Random Fields.

Susceptibility-weighted imaging (SWI) venography can produce detailed venous contrast and complement arterial dominated MR angiography (MRA) techniques. However, these dense reversed-contrast SWI venograms pose new segmentation challenges. We present an automatic method for whole-brain venous blood segmentation in SWI using Conditional Random Fields (CRF). The CRF model combines different first and second order potentials. First-order association potentials are modeled as the composite of an appearance potential, a Hessian-based shape potential and a non-linear location potential. Second-order interaction potentials are modeled using an auto-logistic (smoothing) potential and a data-dependent (edge) potential. Minimal post-processing is used for excluding voxels outside the brain parenchyma and visualizing the surface vessels. The CRF model is trained and validated using 30 SWI venograms acquired within a population of deep brain stimulation (DBS) patients (age range [Formula: see text] years). Results demonstrate robust and consistent segmentation in deep and sub-cortical regions (median kappa = 0.84 and 0.82), as well as in challenging mid-sagittal and surface regions (median kappa = 0.81 and 0.83) regions. Overall, this CRF model produces high-quality segmentation of SWI venous vasculature that finds applications in DBS for minimizing hemorrhagic risks and other surgical and non-surgical applications.

Multi-contrast unbiased MRI atlas of a Parkinson's disease population.

PURPOSE: Parkinson's disease (PD) is the second leading neurodegenerative disease after Alzheimer's disease. In PD research and its surgical treatment, such as deep brain stimulation (DBS), anatomical structural identification and references for spatial normalization are essential, and human brain atlases/templates are proven highly instrumental. However, two shortcomings affect current templates used for PD. First, many templates are derived from a single healthy subject that is not sufficiently representative of the PD-population anatomy. This may result in suboptimal surgical plans for DBS surgery and biased analysis for morphological studies. Second, commonly used mono-contrast templates lack sufficient image contrast for some subcortical structures (i.e., subthalamic nucleus) and biochemical information (i.e., iron content), a valuable feature in current PD research. METHODS: We employed a novel T1-T2* fusion MRI that visualizes both cortical and subcortical structures to drive groupwise registration to create co-registered multi-contrast (T1w, T2*w, T1-T2* fusion, phase, and an R2* map) unbiased templates from 15 PD patients, and a high-resolution histology-derived 3D atlas is co-registered. For validation, these templates are compared against the Colin27 template for landmark registration and midbrain nuclei segmentation. RESULTS: While the T1w, T2*w, and T1-T2* fusion templates provide excellent anatomical details for both cortical and subcortical structures, the phase and R2* map contain the biochemical features. By one-way ANOVA tests, our templates significantly ([Formula: see text]) outperform the Colin27 template in the registration-based tasks. CONCLUSION: The proposed unbiased templates are more representative of the population of interest and can benefit both the surgical planning and research of PD.

Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson's disease.

PURPOSE: Parkinson's disease (PD) is a neurodegenerative disorder that impairs the motor functions. Both surgical treatment and study of PD require delineation of basal ganglia nuclei morphology. While many automatic volumetric segmentation methods have been proposed for the lentiform nucleus, few have attempted to identify the key brainstem substructures including the subthalamic nucleus (STN), substantia nigra (SN), and red nucleus (RN) due to their small size and poor contrast in conventional T1W MRI. METHODS: A dual-contrast patch-based label fusion method was developed to segment the SN, STN, and RN using multivariate cross-correlation. Two different MRI contrasts (T2*w and phase) are produced from a multi-contrast multi-echo FLASH MRI sequence, enabling visualization of these nuclei. T1-T2* fusion MRI was used to resolve the issue of poor nuclei (i.e., the STN, SN, and RN) contrast on T1w MRI, and to mitigate susceptibility artifacts that may hinder accurate nonlinear registration on T2*w MRI. Unbiased group-wise registration was used for anatomical normalization between the atlas library and the target subject. The performance of the proposed method was compared with a state-of-the-art single-contrast label fusion technique. RESULTS: The proposed method outperformed a state-of-the-art single-contrast patch-based method in segmenting the STN, RN and SN, and the results were better than those reported in previous literature. CONCLUSION: Our dual-contrast patch-based label fusion method was superior to a single-contrast method for segmenting brainstem nuclei using a multi-contrast multi-echo FLASH MRI sequence. The method is promising for the treatment and research of Parkinson's disease. This method can be extended for multiple alternative image contrasts and other fields of applications.

Neuronavigation using susceptibility-weighted venography: application to deep brain stimulation and comparison with gadolinium contrast.

Careful trajectory planning on preoperative vascular imaging is an essential step in deep brain stimulation (DBS) to minimize risks of hemorrhagic complications and postoperative neurological deficits. This paper compares 2 MRI methods for visualizing cerebral vasculature and planning DBS probe trajectories: a single data set T1-weighted scan with double-dose gadolinium contrast (T1w-Gd) and a multi-data set protocol consisting of a T1-weighted structural, susceptibility-weighted venography, and time-of-flight angiography (T1w-SWI-TOF). Two neurosurgeons who specialize in neuromodulation surgery planned bilateral STN DBS in 18 patients with Parkinson's disease (36 hemispheres) using each protocol separately. Planned trajectories were then evaluated across all vascular data sets (T1w-Gd, SWI, and TOF) to detect possible intersection with blood vessels along the entire path via an objective vesselness measure. The authors' results show that trajectories planned on T1w-SWI-TOF successfully avoided the cerebral vasculature imaged by conventional T1w-Gd and did not suffer from missing vascular information or imprecise data set registration. Furthermore, with appropriate planning and visualization software, trajectory corridors planned on T1w-SWI-TOF intersected significantly less fine vasculature that was not detected on the T1w-Gd (p < 0.01 within 2 mm and p < 0.001 within 4 mm of the track centerline). The proposed T1w-SWI-TOF protocol comes with minimal effects on the imaging and surgical workflow, improves vessel avoidance, and provides a safe cost-effective alternative to injection of gadolinium contrast.

Multicontrast multiecho FLASH MRI for targeting the subthalamic nucleus.

The subthalamic nucleus (STN) is one of the most common stimulation targets for treating Parkinson's disease using deep brain stimulation (DBS). This procedure requires precise placement of the stimulating electrode. Common practice of DBS implantation utilizes microelectrode recording to locate the sites with the correct electrical response after an initial location estimate based on a universal human brain atlas that is linearly scaled to the patient's anatomy as seen on the preoperative images. However, this often results in prolonged surgical time and possible surgical complications since the small-sized STN is difficult to visualize on conventional magnetic resonance (MR) images and its intersubject variability is not sufficiently considered in the atlas customization. This paper proposes a multicontrast, multiecho MR imaging (MRI) method that directly delineates the STN and other basal ganglia structures through five co-registered image contrasts (T1-weighted navigation image, R2 map, susceptibility-weighted imaging (phase, magnitude and fusion image)) obtained within a clinically acceptable time. The image protocol was optimized through both simulation and in vivo experiments to obtain the best image quality. Taking advantage of the multiple echoes and high readout bandwidths, no interimage registration is required since all images are produced in one acquisition, and image distortion and chemical shift are reduced. This MRI protocol is expected to mitigate some of the shortcomings of the state-of-the-art DBS implantation methods.

A multi-modal approach to computer-assisted deep brain stimulation trajectory planning.

PURPOSE: Both frame-based and frameless approaches to deep brain stimulation (DBS) require planning of insertion trajectories that mitigate hemorrhagic risk and loss of neurological function. Currently, this is done by manual inspection of multiple potential electrode trajectories on MR-imaging data. We propose and validate a method for computer-assisted DBS trajectory planning. METHOD: Our framework integrates multi-modal MRI analysis (T1w, SWI, TOF-MRA) to compute suitable DBS trajectories that optimize the avoidance of specific critical brain structures. A cylinder model is used to process each trajectory and to evaluate complex surgical constraints described via a combination of binary and fuzzy segmented datasets. The framework automatically aggregates the multiple constraints into a unique ranking of recommended low-risk trajectories. Candidate trajectories are represented as a few well-defined cortical entry patches of best-ranked trajectories and presented to the neurosurgeon for final trajectory selection. RESULTS: The proposed algorithm permits a search space containing over 8,000 possible trajectories to be processed in less than 20 s. A retrospective analysis on 14 DBS cases of patients with severe Parkinson's disease reveals that our framework can improve the simultaneous optimization of many pre-formulated surgical constraints. Furthermore, all automatically computed trajectories were evaluated by two neurosurgeons, were judged suitable for surgery and, in many cases, were judged preferable or equivalent to the manually planned trajectories used during the operation. CONCLUSIONS: This work provides neurosurgeons with an intuitive and flexible decision-support system that allows objective and patient-specific optimization of DBS lead trajectories, which should improve insertion safety and reduce surgical time.

Towards computer-assisted deep brain stimulation targeting with multiple active contacts.

We present a novel method for preoperative computer-assisted deep brain stimulation (DBS) electrode targeting that takes into account the multiplicity of available contacts and their polarity. Our framework automatically evaluates the efficacy of many possible electrode orientations to optimize the interplay between the extracellular electric field, created from distinct arrangements of active contacts, and anatomical structures responsible for therapeutic and potential side effects. Experimental results on subthalamic DBS cases suggest bipolar configurations provide more flexibility and control on the spread of electric field and, consequently, are most robust to targeting imprecision. Visualization of predicted efficacy maps provides surgeons with complementary feedback that can bridge the gap between insertion safety and optimal therapeutic efficacy. Overall, this work adds a new dimension to preoperative DBS planning and suggests new insights regarding multi-target stimulation.

Automatic trajectory planning of DBS neurosurgery from multi-modal MRI datasets.

We propose an automated method for preoperative trajectory planning of deep brain stimulation image-guided neurosurgery. Our framework integrates multi-modal MRI analysis (T1w, SWI, TOF-MRA) to determine an optimal trajectory to DBS targets (subthalamic nuclei and globus pallidus interna) while avoiding critical brain structures for prevention of hemorrhages, loss of function and other complications. Results show that our method is well suited to aggregate many surgical constraints and allows the analysis of thousands of trajectories in less than 1/10th of the time for manual planning. Finally, a qualitative evaluation of computed trajectories resulted in the identification of potential new constraints, which are not addressed in the current literature, to better mimic the decision-making of the neurosurgeon during DBS planning.