Framework for 2D-3D image fusion of infrared thermography with preoperative MRI

Hoffmann, Nico; Weidner, Florian; Meyer, Tobias; Schnabel, Christian; Radev, Yordan; Schackert, Gabriele; Petersohn, Uwe; Koch, Edmund; Gumhold, Stefan; Steiner, Gerald; Kirsch, Matthias

doi:10.1515/bmt-2016-0075

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Invasive techniques may produce local microlesions and inflammatory responses nearby the probes, which might influence brain temperature [48]. Infrared thermal imaging (thermography) is a noninvasive technique, it has been used to measure brain temperature under the name of intraoperative thermal imaging (ITI) to delineate brain tumor borders [39,[49][50][51], and this technique is used during surgical resection of brain lesions. Another technique used to measure the brain temperature noninvasively is named magnetic resonance spectroscopy (MRS), which has been used to measure brain temperature in patients with brain tumors [52] and acute ischemic stroke [53].…”

Section: Discussionmentioning

confidence: 99%

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model

Bousselham

Bouattane

Youssfi

et al. 2022

International Journal of Biomedical Imaging

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Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using k -fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.

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Section: Discussionmentioning

confidence: 99%

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model

Bousselham

Bouattane

Youssfi

et al. 2022

International Journal of Biomedical Imaging

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“…[3,5] Intra-operatory surveillance using thermography in neurosurgery has been described in the resection of tumors and in the monitoring of patency of bypass in arteriovenous malformations, in which it demonstrated adequate correlation compared with such conventional methods as postoperative CT, MR imaging (MRI), Doppler and angiography, and its potential use for 2D-3D image fusion with preoperative MRI. [4] ITM is a safe, efficient, and real-time non-invasive method that requires no manipulation of the tissues nor does it requires the use of contrasts or filters.…”

Section: Discussionmentioning

confidence: 99%

Infrared thermography mapping plus neuronavigation target location in an eloquent area cavernoma resection

Font-Réaulx

López

2020

Surgical Neurology International

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Background: Safety and efficacy are irrebuttable goals in neurosurgery. Methods: We performed a subcortical cavernoma resection in an eloquent area, where we recorded and compared the maximal and minimal brain temperature measured by an infrared thermographic camera and thermometer with the neuronavigation (NN) target location and real anatomical lesion location. Results: The hottest cortical point correlated to the subcortical cavernoma location. The NN located the target at 10 mm away from the hottest point. Conclusion: More studies are needed to better understand the thermic radiation of the brain in health and in disease, but we believe that evaluating brain temperature, it could be possible to improve accuracy in neurosurgery and generate more knowledge about brain metabolism in vivo.

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“…Various image enhancement techniques are associated with this step, such as iterative methods 20 , 21 and the application of artificial intelligence 21 . Although the described method is, in principle, applicable to all modalities that produce 3D images with cube-shaped voxels, it cannot be employed for fusing 3D data with 2D data, like an MRI volume combined with 2D infrared thermography 22 or fluorescence imaging, which may be relevant in imageguided surgery applications. The registration of 3D data does not correct for distortions, such as those that occur in MRI images at the coil edge.…”

Section: Limitationsmentioning

confidence: 99%

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Thamm,

Jeffery,

Zhang

et al. 2023

JoVE

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Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic resonance imaging (MRI) combined with PET, are widely available and typically provide robustly co-registered volumes. However, separate devices are often needed to combine a standalone MRI with an existing PET-CT or to incorporate additional data from optical tomography or high-resolution X-ray microtomography. This necessitates image co-registration, which involves complex aspects such as multimodal mouse bed design, fiducial marker inclusion, image reconstruction, and software-based image fusion. Fiducial markers often pose problems for in vivo data due to dynamic range issues, limitations on the imaging field of view, difficulties in marker placement, or marker signal loss over time (e.g., from drying or decay). These challenges must be understood and addressed by each research group requiring image co-registration, resulting in repeated efforts, as the relevant details are rarely described in existing publications.This protocol outlines a general workflow that overcomes these issues. Although a differential transformation is initially created using fiducial markers or visual structures, such markers are not required in production scans. The requirements for the volume data and the metadata generated by the reconstruction software are detailed. The discussion covers achieving and verifying requirements separately for each modality.A phantom-based approach is described to generate a differential transformation between the coordinate systems of two imaging modalities. This method showcases how to co-register production scans without fiducial markers. Each step is illustrated using available software, with recommendations for commercially available phantoms.

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Framework for 2D-3D image fusion of infrared thermography with preoperative MRI

Cited by 9 publications

References 19 publications

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model

Infrared thermography mapping plus neuronavigation target location in an eloquent area cavernoma resection

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

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