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Magn Reson Med ; 2021 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-34652819


PURPOSE: To design and manufacture a pelvis phantom for magnetic resonance (MR)-guided prostate interventions, such as MRGB (MR-guided biopsy) or brachytherapy seed placement. METHODS: The phantom was designed to mimic the human pelvis incorporating bones, bladder, prostate with four lesions, urethra, arteries, veins, and six lymph nodes embedded in ballistic gelatin. A hollow rectum enables transrectal access to the prostate. To demonstrate the feasibility of the phantom for minimal invasive MRI-guided interventions, a targeted inbore MRGB was performed. The needle probe was rectally inserted and guided using an MRI-compatible remote controlled manipulator (RCM). RESULTS: The presented pelvis phantom has realistic imaging properties for MR imaging (MRI), computed tomography (CT) and ultrasound (US). In the targeted inbore MRGB, a prostate lesion was successfully hit with an accuracy of 3.5 mm. The experiment demonstrates that the limited size of the rectum represents a realistic impairment for needle placements. CONCLUSION: The phantom provides a valuable platform for evaluating the performance of MRGB systems. Interventionalists can use the phantom to learn how to deal with challenging situations, without risking harm to patients.

Radiologe ; 61(9): 829-838, 2021 Sep.
Artigo em Alemão | MEDLINE | ID: mdl-34251481


CLINICAL/METHODOLOGICAL ISSUE: Multiparametric magnetic resonance imaging (mpMRI) of the prostate plays a crucial role in the diagnosis and local staging of primary prostate cancer. STANDARD RADIOLOGICAL METHODS: Image-guided biopsy techniques such as MRI-ultrasound fusion not only allow guidance for targeted tissue sampling of index lesions for diagnostic confirmation, but also improve the detection of clinically significant prostate cancer. METHODOLOGICAL INNOVATIONS: Minimally invasive, focal therapies of localized prostate cancer complement the treatment spectrum, especially for low- and intermediate-risk patients. PERFORMANCE: In patients of low and intermediate risk, MR-guided, minimally invasive therapies could enable local tumor control, improved functional outcomes and possible subsequent therapy escalation. Further study results related to multimodal approaches and the application of artificial intelligence (AI) by machine and deep learning algorithms will help to leverage the full potential of focal therapies for prostate cancer in the upcoming era of precision medicine. ACHIEVEMENTS: Completion of ongoing randomized trials comparing each minimally invasive therapy approach with established whole-gland procedures is needed before minimally invasive therapies can be implemented into existing treatment guidelines. PRACTICAL RECOMMENDATIONS: This review article highlights minimally invasive therapies of prostate cancer and the key role of mpMRI for planning and conducting these therapies.

Inteligência Artificial , Neoplasias da Próstata , Humanos , Biópsia Guiada por Imagem , Imageamento por Ressonância Magnética , Masculino , Neoplasias da Próstata/diagnóstico por imagem , Neoplasias da Próstata/cirurgia
Int J Comput Assist Radiol Surg ; 16(8): 1277-1285, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33934313


PURPOSE: Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. METHODS: We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. RESULTS: Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. CONCLUSION: Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.

Algoritmos , Simulação por Computador , Tomografia Computadorizada de Feixe Cônico/métodos , Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Imagens de Fantasmas , Humanos
IEEE Trans Biomed Eng ; 68(5): 1518-1526, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33275574


OBJECTIVE: Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment in various clinical scenarios. We present a fully automatic method for the extraction and differentiation of the arterial and venous vessel trees from abdominal contrast enhanced computed tomography (CE-CT) volumes using convolutional neural networks (CNNs). METHODS: We used a novel ratio-based sampling method to train 2D and 3D versions of the U-Net, the V-Net and the DeepVesselNet. Networks were trained with a combination of the Dice and cross entropy loss. Performance was evaluated on 20 IRCAD subjects. Best performing networks were combined into an ensemble. We investigated seven different weighting schemes. Trained networks were additionally applied to 26 BTCV cases to validate the generalizability. RESULTS: Based on our experiments, the optimal configuration is an equally weighted ensemble of 2D and 3D U- and V-Nets. Our method achieved Dice similarity coefficients of 0.758 ± 0.050 (veins) and 0.838 ± 0.074 (arteries) on the IRCAD data set. Application to the BTCV data set showed a high transfer ability. CONCLUSION: Abdominal vascular structures can be segmented more accurately using ensembles than individual CNNs. 2D and 3D networks have complementary strengths and weaknesses. Our ensemble of 2D and 3D U-Nets and V-Nets in combination with ratio-based sampling achieves a high agreement with manual annotations for both artery and vein segmentation. Our results surpass other state-of-the-art methods. SIGNIFICANCE: Our segmentation pipeline can provide valuable information for the planning of living donor organ transplantations.

Redes Neurais de Computação , Tomografia Computadorizada por Raios X , Abdome/diagnóstico por imagem , Artérias , Humanos , Processamento de Imagem Assistida por Computador
Int J Comput Assist Radiol Surg ; 14(10): 1741-1750, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31378841


PURPOSE: The potential of medical image analysis with neural networks is limited by the restricted availability of extensive data sets. The incorporation of synthetic training data is one approach to bypass this shortcoming, as synthetic data offer accurate annotations and unlimited data size. METHODS: We evaluated eleven CycleGAN for the synthesis of computed tomography (CT) images based on XCAT body phantoms. The image quality was assessed in terms of anatomical accuracy and realistic noise properties. We performed two studies exploring various network and training configurations as well as a task-based adaption of the corresponding loss function. RESULTS: The CycleGAN using the Res-Net architecture and three XCAT input slices achieved the best overall performance in the configuration study. In the task-based study, the anatomical accuracy of the generated synthetic CTs remained high ([Formula: see text] and [Formula: see text]). At the same time, the generated noise texture was close to real data with a noise power spectrum correlation coefficient of [Formula: see text]. Simultaneously, we observed an improvement in annotation accuracy of 65% when using the dedicated loss function. The feasibility of a combined training on both real and synthetic data was demonstrated in a blood vessel segmentation task (dice similarity coefficient [Formula: see text]). CONCLUSION: CT synthesis using CycleGAN is a feasible approach to generate realistic images from simulated XCAT phantoms. Synthetic CTs generated with a task-based loss function can be used in addition to real data to improve the performance of segmentation networks.

Redes Neurais de Computação , Tomografia Computadorizada por Raios X/métodos , Aprendizado Profundo , Humanos , Imagens de Fantasmas