Denoising diffusion-based MR to CT image translation enables whole spine vertebral segmentation in 2D and 3D without manual annotations

Background: Automated segmentation of spinal MR images plays a vital role both scientifically and clinically. However, accurately delineating posterior spine structures presents challenges. Methods: This retrospective study, approved by the ethical committee, involved translating T1w and T2w MR image series into CT images in a total of n=263 pairs of CT/MR series. Landmark-based registration was performed to align image pairs. We compared 2D paired (Pix2Pix, denoising diffusion implicit models (DDIM) image mode, DDIM noise mode) and unpaired (contrastive unpaired translation, SynDiff) image-to-image translation using "peak signal to noise ratio" (PSNR) as quality measure. A publicly available segmentation network segmented the synthesized CT datasets, and Dice scores were evaluated on in-house test sets and the "MRSpineSeg Challenge" volumes. The 2D findings were extended to 3D Pix2Pix and DDIM. Results: 2D paired methods and SynDiff exhibited similar translation performance and Dice scores on paired data. DDIM image mode achieved the highest image quality. SynDiff, Pix2Pix, and DDIM image mode demonstrated similar Dice scores (0.77). For craniocaudal axis rotations, at least two landmarks per vertebra were required for registration. The 3D translation outperformed the 2D approach, resulting in improved Dice scores (0.80) and anatomically accurate segmentations in a higher resolution than the original MR image. Conclusion: Two landmarks per vertebra registration enabled paired image-to-image translation from MR to CT and outperformed all unpaired approaches. The 3D techniques provided anatomically correct segmentations, avoiding underprediction of small structures like the spinous process.Comment: 35 pages, 7 figures, Code and a model weights available https://doi.org/10.5281/zenodo.8221159 and https://doi.org/10.5281/zenodo.819869

Image Fusion and Axial Labeling of the Spine

In order to improve radiological diagnosis of back pain and spine disease, two new algorithms have been developed to aid the 75% of Canadians who will suffer from back pain in a given year. With the associated medical imaging required for many of these patients, there is a potential for improvement in both patient care and healthcare economics by increasing the accuracy and efficiency of spine diagnosis. A real-time spine image fusion system and an automatic vertebra/disc labeling system have been developed to address this. Both magnetic resonance (MR) images and computed tomography (CT) images are often acquired for patients. The MR image highlights soft tissue detail while the CT image highlights bone detail. It is desirable to present both modalities on a single fused image containing the clinically relevant detail. The fusion problem was encoded in an energy functional balancing three competing goals for the fused image: 1) similarity to the MR image, 2) similarity to the CT image and 3) smoothness (containing natural transitions). Graph-Cut and convex solutions have been developed. They have similar performance to each other and outperform other fusion methods from recent literature. The convex solution has real-time performance on modern graphics processing units, allowing for interactive control of the fused image. Clinical validation has been conducted on the convex solution based on 15 patient images. The fused images have been shown to increase confidence of diagnosis compared to unregistered MR and CT images, with no change in time for diagnosis based on readings from 5 radiologists. Spinal vertebrae serve as a reference for the location of surrounding tissues, but vertebrae have a very similar appearance to each other, making it time consume for radiologist to keep track of their locations. To automate this, an axial MR labeling algorithm was developed that runs in near real-time. Probability product kernels and fast integral images combined with simple geometric rules were used to classify pixels, slices and vertebrae. Evaluation was conducted on 32 lumbar spine images and 24 cervical spine images. The algorithm demonstrated 99% and 79% accuracy on the lumbar and cervical spine respectively

Assessment of 3D movements in the lumbar and cervical spine with a new CT based method

Background: Numerous methods for measuring segmental motion in spine have been described. However, because of the inaccessibility of the spine and the complexity of segmental movements, most of the noninvasive methods in use today have low accuracy or are unable to detect movements in all three cardinal axes. Almost all in vivo methods used for analysing segmental motion are based on twodimensional (2D) radiographic examinations. Radiostereometris Analysis is so far the most accurate method to detect three-dimensional (3D) motion. Specific aim: To develop and evaluate a non-invasive method for motion analysis of the spine using computed tomography (CT). Methods: We studied segmental motion in a custom-made spine model, healthy subjects, and a small series of patients operated with total disc replacement. The subjects and patients were examined in flexion and extension on a fourth generation spiral CT unit. Analyses of the segmental movements in lumbar and cervical spine were done with a in-house developed software tool. Results: In the lumbar spine the accuracy was 0.6 mm for translation and 1 degree for rotation in the model study. Movements of more than 1 mm could be visual detected. The repeatability on healthy subjects was 2.8 degrees in rotation and 1.8 mm in translation in vertebral segment. The mean facet joint 3D movement was for the right 6.1 mm and for the left 6.9 mm in L4-L5 segment and for the L5-S1 segment for the right facet 4.5 mm and 4.8 mm for the left. Mean rotation in the sagittal plane was 14.3 degrees in L4-L5 and 10.2 degrees in L5-S1. In patients with total disc replacement the mean rotation in the sagittal plane at the operated level (L5-S1) was 5.4 degrees before surgery and 6.8 after surgery. In the adjacent level (L4-L5) the mean rotation (degrees) was 7.7 before and 9.2 after surgery. The 3D translation in the operated level the left facet was 3.6 mm before and 4.5 mm after surgery and for the right facet joint 3.4 mm before to 3.6 mm after surgery. In the cervical spine the accuracy was 0.7 degrees in rotation and 0.5 mm in translation in the model study. The repeatability on the model was 1.1 degrees in rotation and 0.3 mm in translation. The repeatability on patients was 2.3 degrees in rotation and 1.4 mm in translation. The median movement for the patient was in the sagittal plane for rotation 6.28 and translation 0.1mm, coronal plane 1.68 and 0.6 mm, and for the transverse plane 1.38 and 0.6 mm in translation Conclusion: We have developed a non-invasive CT based method to study the 3D segmental movement in the spine. It has been tested in a model study, on healthy subjects and on patients with total disc replacement in cervical and lumbar spine. We believe that this method for detecting movements in the spine is useful both in research and for clinical use

DeepRetroMoCo:deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Background and purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor.Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing.Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco.Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data

Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning

This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation

A combination of functional magnetic resonance imaging and diffusion tensor image to explore structure-function relationship in healthy and myelopathic spinal cord

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Registration accuracy for MR images of the prostate using a subvolume based registration protocol

<p>Abstract</p> <p>Background</p> <p>In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate.</p> <p>Methods</p> <p>Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances.</p> <p>Results</p> <p>We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series.</p> <p>Conclusions</p> <p>Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.</p