Search Results - "Onofrey"

Deep learning-based attenuation map generation for myocardial perfusion SPECT by Shi, Luyao, Onofrey, John A., Liu, Hui, Liu, Yi-Hwa, Liu, Chi

“…Purpose Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables…”
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An investigation of quantitative accuracy for deep learning based denoising in oncological PET by Lu, Wenzhuo, Onofrey, John A, Lu, Yihuan, Shi, Luyao, Ma, Tianyu, Liu, Yaqiang, Liu, Chi

“…Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR),…”
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Adaptive data-driven motion detection and optimized correction for brain PET by Revilla, Enette Mae, Gallezot, Jean-Dominique, Naganawa, Mika, Toyonaga, Takuya, Fontaine, Kathryn, Mulnix, Tim, Onofrey, John A., Carson, Richard E., Lu, Yihuan

“…Head motion during PET scans causes image quality degradation, decreased concentration in regions with high uptake and incorrect outcome measures from kinetic…”
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Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging by Gross, Moritz, Spektor, Michael, Jaffe, Ariel, Kucukkaya, Ahmet S, Iseke, Simon, Haider, Stefan P, Strazzabosco, Mario, Chapiro, Julius, Onofrey, John A

“…Accurate liver segmentation is key for volumetry assessment to guide treatment decisions. Moreover, it is an important pre-processing step for cancer detection…”
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Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application by Shi, Luyao, Zhang, Jiazhen, Toyonaga, Takuya, Shao, Dan, Onofrey, John A, Lu, Yihuan

“… In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause…”
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Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET by Liu, Hui, Wu, Jing, Lu, Wenzhuo, Onofrey, John A, Liu, Yi-Hwa, Liu, Chi

“…Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron emission…”
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An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution by Sun, Chen, Revilla, Enette Mae, Zhang, Jiazhen, Fontaine, Kathryn, Toyonaga, Takuya, Gallezot, Jean-Dominique, Mulnix, Tim, Onofrey, John A., Carson, Richard E., Lu, Yihuan

“…Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both…”
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Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET by Lu, Yihuan, Gallezot, Jean-Dominique, Naganawa, Mika, Ren, Silin, Fontaine, Kathryn, Wu, Jing, Onofrey, John A, Toyonaga, Takuya, Boutagy, Nabil, Mulnix, Tim, Panin, Vladimir Y, Casey, Michael E, Carson, Richard E, Liu, Chi

“…PET has the potential to perform absolute in vivo radiotracer quantitation. This potential can be compromised by voluntary body motion (BM), which degrades…”
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Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data by Lu, Yihuan, Fontaine, Kathryn, Mulnix, Tim, Onofrey, John A, Ren, Silin, Panin, Vladimir, Jones, Judson, Casey, Michael E, Barnett, Robert, Kench, Peter, Fulton, Roger, Carson, Richard E, Liu, Chi

“…Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT image and a…”
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Reliable Prostate Cancer Risk Mapping from MRI Using Targeted and Systematic Core Needle Biopsy Histopathology by Zeevi, Tal, Leapman, Michael S., Sprenkle, Preston C., Venkataraman, Rajesh, Staib, Lawrence H., Onofrey, John A.

“…Objective: To compute a dense prostate cancer risk map for the individual patient post-biopsy from magnetic resonance imaging (MRI) and to provide a more…”
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Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning by Onofrey, John A., Staib, Lawrence H., Papademetris, Xenophon

“…The accurate segmentation of the brain surface in post-surgical computed tomography (CT) images is critical for image-guided neurosurgical procedures in…”
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Deep learning–based attenuation correction for whole-body PET — a multi-tracer study with 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine by Toyonaga, Takuya, Shao, Dan, Shi, Luyao, Zhang, Jiazhen, Revilla, Enette Mae, Menard, David, Ankrah, Joseph, Hirata, Kenji, Chen, Ming-Kai, Onofrey, John A., Lu, Yihuan

“…A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18 F-FDG, 68  Ga-DOTATATE, and…”
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Bias in medical AI: Implications for clinical decision-making by Cross, James L., Choma, Michael A., Onofrey, John A.

“…Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences,…”
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DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT by Chen, Xiongchao, Zhou, Bo, Xie, Huidong, Guo, Xueqi, Zhang, Jiazhen, Duncan, James S., Miller, Edward J., Sinusas, Albert J., Onofrey, John A., Liu, Chi

“…Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely applied for the diagnosis of cardiovascular diseases…”
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MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study by Gonzales, Ricardo A, Seemann, Felicia, Lamy, Jérôme, Mojibian, Hamid, Atar, Dan, Erlinge, David, Steding-Ehrenborg, Katarina, Arheden, Håkan, Hu, Chenxi, Onofrey, John A, Peters, Dana C, Heiberg, Einar

“…Mitral annular plane systolic excursion (MAPSE) and left ventricular (LV) early diastolic velocity (e') are key metrics of systolic and diastolic function, but…”
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Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics by Gross, Moritz, Huber, Steffen, Arora, Sandeep, Ze’evi, Tal, Haider, Stefan P., Kucukkaya, Ahmet S., Iseke, Simon, Kuhn, Tom Niklas, Gebauer, Bernhard, Michallek, Florian, Dewey, Marc, Vilgrain, Valérie, Sartoris, Riccardo, Ronot, Maxime, Jaffe, Ariel, Strazzabosco, Mario, Chapiro, Julius, Onofrey, John A.

“…Objectives To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on…”
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LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis by Gross, Moritz, Arora, Sandeep, Huber, Steffen, Kücükkaya, Ahmet S., Onofrey, John A.

“…Accurate segmentation of liver and tumor regions in medical imaging is crucial for the diagnosis, treatment, and monitoring of hepatocellular carcinoma (HCC)…”
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Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization by Onofrey, John A., Casetti-Dinescu, Dana I., Lauritzen, Andreas D., Sarkar, Saradwata, Venkataraman, Rajesh, Fan, Richard E., Sonn, Geoffrey A., Sprenkle, Preston C., Staib, Lawrence H., Papademetris, Xenophon

“…The ability of medical image analysis deep learning algorithms to generalize across multiple sites is critical for clinical adoption of these methods. Medical…”
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Low-Dimensional Non-Rigid Image Registration Using Statistical Deformation Models From Semi-Supervised Training Data by Onofrey, John A., Papademetris, Xenophon, Staib, Lawrence H.

“…Accurate and robust image registration is a fundamental task in medical image analysis applications, and requires non-rigid transformations with a large number…”
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Integrating Prostate Specific Antigen Density Biomarker Into Deep Learning Prostate MRI Lesion Segmentation Models by Zhong, Jiayang, Staib, Lawrence H., Venkataraman, Rajesh, Onofrey, John A.

“…Prostate cancer lesion segmentation in multi-parametric magnetic resonance imaging (mpMRI) is crucial for pre-biopsy diagnosis and targeted biopsy guidance…”
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