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1.
Sci Rep ; 11(1): 18923, 2021 09 23.
Artigo em Inglês | MEDLINE | ID: mdl-34556678

RESUMO

Advances in imaging and early cancer detection have increased interest in magnetic resonance (MR) guided focused ultrasound (MRgFUS) technologies for cancer treatment. MRgFUS ablation treatments could reduce surgical risks, preserve organ tissue and function, and improve patient quality of life. However, surgical resection and histological analysis remain the gold standard to assess cancer treatment response. For non-invasive ablation therapies such as MRgFUS, the treatment response must be determined through MR imaging biomarkers. However, current MR biomarkers are inconclusive and have not been rigorously evaluated against histology via accurate registration. Existing registration methods rely on anatomical features to directly register in vivo MR and histology. For MRgFUS applications in anatomies such as liver, kidney, or breast, anatomical features that are not caused by the treatment are often insufficient to drive direct registration. We present a novel MR to histology registration workflow that utilizes intermediate imaging and does not rely on anatomical MR features being visible in histology. The presented workflow yields an overall registration accuracy of 1.00 ± 0.13 mm. The developed registration pipeline is used to evaluate a common MRgFUS treatment assessment biomarker against histology. Evaluating MR biomarkers against histology using this registration pipeline will facilitate validating novel MRgFUS biomarkers to improve treatment assessment without surgical intervention. While the presented registration technique has been evaluated in a MRgFUS ablation treatment model, this technique could be potentially applied in any tissue to evaluate a variety of therapeutic options.


Assuntos
Ablação por Ultrassom Focalizado de Alta Intensidade/métodos , Processamento de Imagem Assistida por Computador , Imagem por Ressonância Magnética Intervencionista , Neoplasias/terapia , Animais , Linhagem Celular Tumoral/transplante , Modelos Animais de Doenças , Estudos de Viabilidade , Humanos , Necrose/diagnóstico , Necrose/patologia , Neoplasias/diagnóstico por imagem , Neoplasias/patologia , Coelhos , Resultado do Tratamento
2.
IEEE Trans Biomed Eng ; 68(5): 1737-1747, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-32946378

RESUMO

Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarker immediately after treatment from contrast-enhanced MRI. The NPV has variable accuracy, and the use of contrast agent prevents continuing MRgFUS treatment if tumor coverage is inadequate. This work presents a novel, noncontrast, learned multiparametric MR biomarker that can be used during treatment for intratreatment assessment, validated in a VX2 rabbit tumor model. A deep convolutional neural network was trained on noncontrast multiparametric MR images using the NPV biomarker from follow-up MR imaging (3-5 days after MRgFUS treatment) as the accurate label of nonviable tissue. A novel volume-conserving registration algorithm yielded a voxel-wise correlation between treatment and follow-up NPV, providing a rigorous validation of the biomarker. The learned noncontrast multiparametric MR biomarker predicted the follow-up NPV with an average DICE coefficient of 0.71, substantially outperforming the current clinical standard (DICE coefficient = 0.53). Noncontrast multiparametric MR imaging integrated with a deep convolutional neural network provides a more accurate prediction of MRgFUS treatment outcome than current contrast-based techniques.


Assuntos
Ablação por Ultrassom Focalizado de Alta Intensidade , Neoplasias , Animais , Biomarcadores , Imageamento por Ressonância Magnética , Neoplasias/diagnóstico por imagem , Neoplasias/terapia , Coelhos , Resultado do Tratamento , Ultrassonografia
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