Purpose: Pathology from trans-perineal template mapping biopsy (TTMB) can be used as labels to train prostate cancer classifiers. In this work, we propose a framework to register TTMB cores to advanced volumetric ultrasound data such as multi-parametric transrectal ultrasound (mpTRUS).
Methods: The framework has mainly two steps. First, needle trajectories are calculated with respect to the needle guiding template-considering deflections in their paths. In standard TTMB, a sparsely sampled ultrasound volume is taken prior to the procedure which contains the template overlaid on top of it. The position of this template is detected automatically, and the cores are mapped following the calculated needle trajectories. Second, the TTMB volume is aligned to the mpTRUS volume by a two-step registration method. Using the same transformations from the registration step, the cores are registered from the TTMB volume to the mpTRUS volume.
Results: TTMB and mpTRUS of 10 patients were available for this work. The target registration errors (TRE) of the volumes using landmarks picked by three research assistants (RA) and one radiation oncologist (RO) were on average 1.32 ± 0.7 mm and 1.03 ± 0.6 mm, respectively. Additionally, on average, our framework takes only 97 s to register the cores.
Conclusion: Our proposed framework allows a quick way to find the spatial location of the cores with respect to volumetric ultrasound. Furthermore, knowing the correct location of the pathology will facilitate focal treatment and will aid in training imaging-based cancer classifiers.
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http://dx.doi.org/10.1007/s11548-022-02604-4 | DOI Listing |
Otol Neurotol
February 2025
Department of Radiology, Yale School of Medicine, New Haven, CT.
Background: Vestibular schwannoma (VS) is a common intracranial tumor that affects patients' quality of life. Reliable imaging techniques for tumor volume assessment are essential for guiding management decisions. The study aimed to compare the ABC/2 method to the gold standard planimetry method for volumetric assessment of VS.
View Article and Find Full Text PDFOral Maxillofac Surg
January 2025
Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053, Regensburg, Germany.
Purpose: This study aimed to clarify the applicability of smartphone-based three-dimensional (3D) surface imaging for clinical use in oral and maxillofacial surgery, comparing two smartphone-based approaches to the gold standard.
Methods: Facial surface models (SMs) were generated for 30 volunteers (15 men, 15 women) using the Vectra M5 (Canfield Scientific, USA), the TrueDepth camera of the iPhone 14 Pro (Apple Inc., USA), and the iPhone 14 Pro with photogrammetry.
Investig Clin Urol
January 2025
Department of Urology, Mansoura Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.
Purpose: To create a computer-aided prediction (CAP) system to predict Wilms tumor (WT) responsiveness to preoperative chemotherapy (PC) using pre-therapy contrast-enhanced computed tomography (CECT).
Materials And Methods: A single-center database was reviewed for children <18 years diagnosed with WT and received PC between 2001 and 2021. Patients were excluded if pre- and post-PC CECT were not retrievable.
Sci Rep
January 2025
Dept. of Neurology, University of Ulm, Oberer Eselsberg 45, 89081, Ulm, Germany.
Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more slowly progressive than amyotrophic laterals sclerosis (ALS). `Classical` ALS is characterized by catabolism and abnormal energy metabolism preceding onset of motor symptoms, and previous studies indicated that the disease progression of ALS involves hypothalamic atrophy.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Engineering, Inha University, Incheon, Republic of Korea.
The most prevalent form of malignant tumors that originate in the brain are known as gliomas. In order to diagnose, treat, and identify risk factors, it is crucial to have precise and resilient segmentation of the tumors, along with an estimation of the patients' overall survival rate. Therefore, we have introduced a deep learning approach that employs a combination of MRI scans to accurately segment brain tumors and predict survival in patients with gliomas.
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