VIDA: A Voxel-Based Dosimetry Method for Targeted Radionuclide Therapy Using Geant4

We have developed the Voxel-Based Internal Dosimetry Application (VIDA) to provide patient-specific dosimetry in targeted radionuclide therapy performing Monte Carlo simulations of radiation transport with the Geant4 toolkit. The code generates voxel-level dose rate maps using anatomical and physiological data taken from individual patients. Voxel level dose rate curves are then fit and integrated to yield a spatial map of radiation absorbed dose. In this article, we present validation studies using established dosimetry results, including self-dose factors (DFs) from the OLINDA/EXM program for uniform activity in unit density spheres and organ self- and cross-organ DFs in the Radiation Dose Assessment Resource (RADAR) reference adult phantom. The comparison with reference data demonstrated agreement within 5% for self-DFs to spheres and reference phantom source organs for four common radionuclides used in targeted therapy (131I, 90Y, 111In, 177Lu). Agreement within 9% was achieved for cross-organ DFs. We also present dose estimates to normal tissues and tumors from studies of two non-Hodgkin Lymphoma patients treated by 131I radioimmunotherapy, with comparison to results generated independently with another dosimetry code. A relative difference of 12% or less was found between methods for mean absorbed tumor doses accounting for tumor regression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140327/1/cbr.2014.1713.pd

Early Detection of Ovarian Cancer with Conventional and Contrast-Enhanced Transvaginal Sonography: Recent Advances and Potential Improvements

Recently, there have been several major technical advances in the sonographic diagnosis of ovarian cancer in its early stages. These include improved assessment of tumor morphology with transvaginal sonography (TVS), and detection and characterization of tumor neovascularity with transvaginal color Doppler sonography (TV-CDS) and contrast-enhanced transvaginal sonography (CE-TVS). This paper will discuss and illustrate these improvements and describe how they enhance detection of early-stage ovarian cancer. Our initial experience with parametric mapping of CE-TVS will also be mentioned

Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging

To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue

UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation

Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis. Transformer reformats the image into separate patches and realizes global communication via the self-attention mechanism. However, positional information between patches is hard to preserve in such 1D sequences, and loss of it can lead to sub-optimal performance when dealing with large amounts of heterogeneous tissues of various sizes in 3D medical image segmentation. Additionally, current methods are not robust and efficient for heavy-duty medical segmentation tasks such as predicting a large number of tissue classes or modeling globally inter-connected tissue structures. To address such challenges and inspired by the nested hierarchical structures in vision transformer, we proposed a novel 3D medical image segmentation method (UNesT), employing a simplified and faster-converging transformer encoder design that achieves local communication among spatially adjacent patch sequences by aggregating them hierarchically. We extensively validate our method on multiple challenging datasets, consisting of multiple modalities, anatomies, and a wide range of tissue classes, including 133 structures in the brain, 14 organs in the abdomen, 4 hierarchical components in the kidneys, inter-connected kidney tumors and brain tumors. We show that UNesT consistently achieves state-of-the-art performance and evaluate its generalizability and data efficiency. Particularly, the model achieves whole brain segmentation task complete ROI with 133 tissue classes in a single network, outperforming prior state-of-the-art method SLANT27 ensembled with 27 networks.Comment: 19 pages, 17 figures. arXiv admin note: text overlap with arXiv:2203.0243