| 1. |
- Abramian, David, 1992-, et al.
(författare)
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Evaluation of inverse treatment planning for gamma knife radiosurgery using fMRI brain activation maps as organs at risk
- 2023
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Ingår i: Medical physics (Lancaster). - : WILEY. - 0094-2405. ; 50:9, s. 5297-5311
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Tidskriftsartikel (refereegranskat)abstract
- Background: Stereotactic radiosurgery (SRS) can be an effective primary or adjuvant treatment option for intracranial tumors. However, it carries risks of various radiation toxicities, which can lead to functional deficits for the patients. Current inverse planning algorithms for SRS provide an efficient way for sparing organs at risk (OARs) by setting maximum radiation dose constraints in the treatment planning process.Purpose: We propose using activation maps from functional MRI (fMRI) to map the eloquent regions of the brain and define functional OARs (fOARs) for Gamma Knife SRS treatment planning.Methods: We implemented a pipeline for analyzing patient fMRI data, generating fOARs from the resulting activation maps, and loading them onto the GammaPlan treatment planning software. We used the Lightning inverse planner to generate multiple treatment plans from open MRI data of five subjects, and evaluated the effects of incorporating the proposed fOARs.Results: The Lightning optimizer designs treatment plans with high conformity to the specified parameters. Setting maximum dose constraints on fOARs successfully limits the radiation dose incident on them, but can have a negative impact on treatment plan quality metrics. By masking out fOAR voxels surrounding the tumor target it is possible to achieve high quality treatment plans while controlling the radiation dose on fOARs.Conclusions: The proposed method can effectively reduce the radiation dose incident on the eloquent brain areas during Gamma Knife SRS of brain tumors.
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| 2. |
- Abramian, David, 1992-
(författare)
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Modern multimodal methods in brain MRI
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic resonance imaging (MRI) is one of the pillars of modern medical imaging, providing a non-invasive means to generate 3D images of the body with high soft-tissue contrast. Furthermore, the possibilities afforded by the design of MRI sequences enable the signal to be sensitized to a multitude of physiological tissue properties, resulting in a wide variety of distinct MRI modalities for clinical and research use. This thesis presents a number of advanced brain MRI applications, which fulfill, to differing extents, two complementary aims. On the one hand, they explore the benefits of a multimodal approach to MRI, combining structural, functional and diffusion MRI, in a variety of contexts. On the other, they emphasize the use of advanced mathematical and computational tools in the analysis of MRI data, such as deep learning, Bayesian statistics, and graph signal processing. Paper I introduces an anatomically-adapted extension to previous work in Bayesian spatial priors for functional MRI data, where anatomical information is introduced from a T1-weighted image to compensate for the low anatomical contrast of functional MRI data. It has been observed that the spatial correlation structure of the BOLD signal in brain white matter follows the orientation of the underlying axonal fibers. Paper II argues about the implications of this fact on the ideal shape of spatial filters for the analysis of white matter functional MRI data. By using axonal orientation information extracted from diffusion MRI, and leveraging the possibilities afforded by graph signal processing, a graph-based description of the white matter structure is introduced, which, in turn, enables the definition of spatial filters whose shape is adapted to the underlying axonal structure, and demonstrates the increased detection power resulting from their use. One of the main clinical applications of functional MRI is functional localization of the eloquent areas of the brain prior to brain surgery. This practice is widespread for various invasive surgeries, but is less common for stereotactic radiosurgery (SRS), a non-invasive surgical procedure wherein tissue is ablated by concentrating several beams of high-energy radiation. Paper III describes an analysis and processing pipeline for functional MRI data that enables its use for functional localization and delineation of organs-at-risk for Elekta GammaKnife SRS procedures. Paper IV presents a deep learning model for super-resolution of diffusion MRI fiber ODFs, which outperforms standard interpolation methods in estimating local axonal fiber orientations in white matter. Finally, Paper V demonstrates that some popular methods for anonymizing facial data in structural MRI volumes can be partially reversed by applying generative deep learning models, highlighting one way in which the enormous power of deep learning models can potentially be put to use for harmful purposes.
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| 3. |
- Akbar, Muhammad Usman, 1990-, et al.
(författare)
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Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models
- 2024
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Ingår i: Scientific Data. - : Nature Publishing Group. - 2052-4463. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1–3) and a diffusion model for the task of brain tumor segmentation (using two segmentation networks, U-Net and a Swin transformer). Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80%–90% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Our conclusion is that sharing synthetic medical images is a viable option to sharing real images, but that further work is required. The trained generative models and the generated synthetic images are shared on AIDA data hub.
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| 4. |
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| 5. |
- Blystad, Ida, 1972-
(författare)
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Clinical Applications of Synthetic MRI of the Brain
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Magnetic Resonance Imaging (MRI) has a high soft-tissue contrast with a high sensitivity for detecting pathological changes in the brain. Conventional MRI is a time-consuming method with multiple scans that relies on the visual assessment of the neuroradiologist. Synthetic MRI uses one scan to produce conventional images, but also quantitative maps based on relaxometry, that can be used to quantitatively analyse tissue properties and pathological changes. The studies presented here apply the use of synthetic MRI of the brain in different clinical settings.In the first study, synthetic MR images were compared to conventional MR images in 22 patients. The contrast, the contrast-to-noise ratio, and the diagnostic quality were assessed. Image quality was perceived to be inferior in the synthetic images, but synthetic images agreed with the clinical diagnoses to the same extent as the conventional images.Patients with early multiple sclerosis were analysed in the second study. In patients with multiple sclerosis, contrast-enhancing white matter lesions are a sign of active disease and can indicate a need for a change in therapy. Gadolinium-based contrast agents are used to detect active lesions, but concern has been raised regarding the long-term effects of repeated use of gadolinium. In this study, relaxometry was used to evaluate whether pre-contrast injection tissue-relaxation rates and proton density can identify active lesions without gadolinium. The findings suggest that active lesions often have relaxation times and proton density that differ from non-enhancing lesions, but with some overlap. This makes it difficult to replace gadolinium-based contrast agent injection with synthetic MRI in the monitoring of MS patients.Malignant gliomas are primary brain tumours with contrast enhancement due to a defective blood-brain barrier. However, they also grow in an infiltrative, diffuse manner, making it difficult to clearly delineate them from surrounding normal brain tissue in the diagnostic workup, at surgery, and during follow-up. The contrast-enhancing part of the tumour is easily visualised, but not the diffuse infiltration. In studies three and four, synthetic MRI was used to analyse the peritumoral area of malignant gliomas, and revealed quantitative findings regarding peritumoral relaxation changes and non-visible contrast enhancement suggestive of non-visible infiltrative tumour growth.In conclusion, synthetic MRI provides quantitative information about the brain tissue and this could improve the diagnosis and treatment for patients.
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| 6. |
- Blystad, Ida, 1972-, et al.
(författare)
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Quantitative MRI for analysis of peritumoral edema in malignant gliomas
- 2017
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R-1, transverse relaxation R-2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images. Methods In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R-1, R-2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model. Results R-1, R-2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (P<.0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor. Conclusion Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R-1 and R-2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.
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| 7. |
- Blystad, Ida, 1972-, et al.
(författare)
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Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema
- 2020
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R-1), transverse relaxation (R-2) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R-1-difference-map. The quantitative R-1-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.
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| 8. |
- Boito, Deneb, 1993-
(författare)
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Diffusion MRI with generalised gradient waveforms : methods, models, and neuroimaging applications
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The incessant, random motion of water molecules within biological tissues reveals unique information about the tissues’ structural and functional characteristics. Diffusion magnetic resonance imaging is sensitive to this random motion, and since the mid-1990s it has been extensively employed for studying the human brain. Most notably, measurements of water diffusion allow for the early detection of ischaemic stroke and for the unveiling of the brain’s wiring via reconstruction of the neuronal connections. Ultimately, the goal is to employ this imaging technique to perform non-invasive, in vivo virtual histology to directly characterise both healthy and diseased tissue. Recent developments in the field have introduced new ways to measure the diffusion process in clinically feasible settings. These new measurements, performed by employing generalised magnetic field gradient waveforms, grant access to specific features of the cellular composition and structural organisation of the tissue. Methods based on them have already proven beneficial for the assessment of different brain diseases, sparking interest in translating such techniques into clinical practice. This thesis focuses on improving the methods currently employed for the analysis of such diffusion MRI data, with the aim of facilitating their clinical adoption. The first two publications introduce constrained frameworks for the estimation of parameters from diffusion MRI data acquired with generalised gradient waveforms. The constraints are dictated by mathematical and physical properties of a multi-compartment model used to represent the brain tissue, and can be efficiently enforced by employing a relatively new optimisation scheme called semidefinite programming. The developed routines are demonstrated to improve robustness to noise and imperfect data collection. Moreover, constraining the fit is shown to relax the requirements on the number of points needed for the estimation, thus allowing for faster data acquisition. In the third paper, the developed frameworks are employed to study the brain’s white matter in patients previously hospitalised for COVID-19 and who still suffer from neurological symptoms months after discharge. The results show widespread alterations to the structural integrity of their brain, with the metrics available through the advanced diffusion measurements providing new insights into the damage to the white matter. The fourth paper revisits the modelling paradigm currently adopted for the analysis of diffusion MRI data acquired with generalised gradient waveforms. Hitherto, the assumption of free diffusion has been employed to represent each domain in a multi-compartmental picture of the brain tissue. In this work, a model for restricted diffusion is considered instead to alleviate the paradoxical assumption of free but compartmentalised diffusion. The model is shown to perfectly capture restricted diffusion as measured with the generalised diffusion gradient waveforms, thus endorsing its use for representing each domain in the multi-compartmental model of the tissue.
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| 9. |
- Boito, Deneb, 1993-, et al.
(författare)
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Diffusivity-limited q-space trajectory imaging
- 2023
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Ingår i: Magnetic Resonance Letters. - : KeAi Publishing Communications. - 2772-5162. ; 3:2, s. 187-196
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Tidskriftsartikel (refereegranskat)abstract
- Q-space trajectory imaging (QTI) allows non-invasive estimation of microstructural features of heterogeneous porous media via diffusion magnetic resonance imaging performed with generalised gradient waveforms. A recently proposed constrained estimation framework, called QTI+, improved QTI’s resilience to noise and data sparsity, thus increasing the reliability of the method by enforcing relevant positivity constraints. In this work we consider expanding the set of constraints to be applied during the fitting of the QTI model. We show that the additional conditions, which introduce an upper bound on the diffusivity values, further improve the retrieved parameters on a publicly available human brain dataset as well as on data acquired from healthy volunteers using a scanner-ready protocol.
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| 10. |
- Boito, Deneb, 1993-, et al.
(författare)
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MRI with generalized diffusion encoding reveals damaged white matter in patients previously hospitalized for COVID-19 and with persisting symptoms at follow-up
- 2023
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Ingår i: Brain Communications. - : Oxford University Press. - 2632-1297. ; 5:6
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Tidskriftsartikel (refereegranskat)abstract
- There is mounting evidence of the long-term effects of COVID-19 on the central nervous system, with patients experiencing diverse symptoms, often suggesting brain involvement. Conventional brain MRI of these patients shows unspecific patterns, with no clear connection of the symptomatology to brain tissue abnormalities, whereas diffusion tensor studies and volumetric analyses detect measurable changes in the brain after COVID-19. Diffusion MRI exploits the random motion of water molecules to achieve unique sensitivity to structures at the microscopic level, and new sequences employing generalized diffusion encoding provide structural information which are sensitive to intravoxel features. In this observational study, a total of 32 persons were investigated: 16 patients previously hospitalized for COVID-19 with persisting symptoms of post-COVID condition (mean age 60 years: range 41–79, all male) at 7-month follow-up and 16 matched controls, not previously hospitalized for COVID-19, with no post-COVID symptoms (mean age 58 years, range 46–69, 11 males). Standard MRI and generalized diffusion encoding MRI were employed to examine the brain white matter of the subjects. To detect possible group differences, several tissue microstructure descriptors obtainable with the employed diffusion sequence, the fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity, microscopic anisotropy, orientational coherence (Cc) and variance in compartment’s size (CMD) were analysed using the tract-based spatial statistics framework. The tract-based spatial statistics analysis showed widespread statistically significant differences (P < 0.05, corrected for multiple comparisons using the familywise error rate) in all the considered metrics in the white matter of the patients compared to the controls. Fractional anisotropy, microscopic anisotropy and Cc were lower in the patient group, while axial diffusivity, radial diffusivity, mean diffusivity and CMD were higher. Significant changes in fractional anisotropy, microscopic anisotropy and CMD affected approximately half of the analysed white matter voxels located across all brain lobes, while changes in Cc were mainly found in the occipital parts of the brain. Given the predominant alteration in microscopic anisotropy compared to Cc, the observed changes in diffusion anisotropy are mostly due to loss of local anisotropy, possibly connected to axonal damage, rather than white matter fibre coherence disruption. The increase in radial diffusivity is indicative of demyelination, while the changes in mean diffusivity and CMD are compatible with vasogenic oedema. In summary, these widespread alterations of white matter microstructure are indicative of vasogenic oedema, demyelination and axonal damage. These changes might be a contributing factor to the diversity of central nervous system symptoms that many patients experience after COVID-19.
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