Wei Zhou
University of Colorado Denver
Imaging phantomOphthalmologyOpticsPhysicsBiomedical engineeringArtificial intelligenceNoise reductionTomographyMaterials scienceImage resolutionVisual fieldGlaucomaScannerNuclear medicinePhoton counting detectorComputer visionImage noiseComputer scienceImage qualityMedicineDetector
33Publications
13H-index
338Citations
Publications 33
Newest
#1Xiang Fang (UWM: University of Wisconsin–Milwaukee)H-Index: 6
#1Xiang Fang (UWM: University of Wisconsin–Milwaukee)
Last. Donglai Huo (University of Colorado Denver)
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To explore the feasibility of an automatic machine-learning algorithm-based quality control system for the practice of diagnostic radiography, performance of a convolutional neural networks (CNN)-based algorithm for identifying radiographic (X-ray) views at different levels was examined with a retrospective, HIPAA-compliant, and IRB-approved study performed on 15,046 radiographic images acquired between 2013 and 2018 from nine clinical sites affiliated with our institution. Images were labeled a...
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#1Hao Gong (Mayo Clinic)H-Index: 8
#2Shengzhen Tao (Mayo Clinic)H-Index: 13
Last. Shuai Leng (Mayo Clinic)H-Index: 53
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PURPOSE To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi-energy computed tomography (CT) images without performing conventional material decomposition. METHODS The proposed CNN (denoted as Incept-net) followed the general framework of encoder-decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi-energy CT. Incept-net was implemented with a customized l...
2 CitationsSource
#1Shengzhen Tao (Mayo Clinic)H-Index: 13
#2Kishore Rajendran (Mayo Clinic)H-Index: 11
Last. Shuai Leng (Mayo Clinic)H-Index: 53
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The clinical demand for low image noise often limits the slice thickness used in many CT applications. However, a thick-slice image is more susceptible to longitudinal partial volume effects, which can blur key anatomic structures and pathologies of interest. In this work, we develop a prior-knowledge-aware iterative denoising (PKAID) framework that utilizes spatial data redundancy in the slice increment direction to generate low-noise, thin-slice images, and demonstrate its application in non-c...
1 CitationsSource
#1Zaiyang Long (Mayo Clinic)H-Index: 10
#2Wei Zhou (Anschutz Medical Campus)H-Index: 13
Last. Nicholas J. Hangiandreou (Mayo Clinic)H-Index: 19
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PURPOSE Ultrasound grayscale imaging preset optimization has often been qualitative and dependent upon vendor application specialists. This study aimed to propose a systematic approach for grayscale imaging preset optimization and apply the approach in a clinical abdominal scan setting. METHODS A six-step approach was detailed including identification of clinical task, adjustment of basic parameters, fine-tuning of advanced parameters, image performance metrics confirmation, clinical evaluation ...
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#1Wei Zhou (Mayo Clinic)H-Index: 13
#2Gregory MichalakH-Index: 12
Last. Shuai LengH-Index: 53
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OBJECTIVE: The aims of this study were to investigate the feasibility of using a universal abdominal acquisition protocol on a photon-counting detector computed tomography (PCD-CT) system and to compare its performance to that of single-energy (SE) and dual-energy (DE) CT using energy-integrating detectors (EIDs). METHODS: Iodine inserts of various concentrations and sizes were embedded into different sizes of adult abdominal phantoms. Phantoms were scanned on a research PCD-CT and a clinical EI...
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#1Kishore RajendranH-Index: 11
#2Benjamin A VossH-Index: 1
Last. Shuai LengH-Index: 53
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ObjectiveThe aim of this study was to quantitatively demonstrate radiation dose reduction for sinus and temporal bone examinations using high-resolution photon-counting detector (PCD) computed tomography (CT) with an additional tin (Sn) filter.Materials and MethodsA multienergy CT phantom, an anthro
9 CitationsSource
#1Wei Zhou (Mayo Clinic)H-Index: 13
#2Gregory Michalak (Mayo Clinic)H-Index: 12
Last. Shuai Leng (Mayo Clinic)H-Index: 53
view all 8 authors...
In addition to low-energy-threshold images (TLIs), photon-counting detector (PCD) computed tomography (CT) can generate virtual monoenergetic images (VMIs) and iodine maps. Our study sought to determine the image type that maximizes iodine detectability. Adult abdominal phantoms with iodine inserts of various concentrations and lesion sizes were scanned on a PCD-CT system. TLIs, VMIs at 50 keV, and iodine maps were generated, and iodine contrast-to-noise ratio (CNR) was measured. A channelized H...
2 CitationsSource
#1Wei Zhou (Mayo Clinic)H-Index: 13
#2Zaiyang Long (Mayo Clinic)H-Index: 10
Last. Nicholas J. Hangiandreou (Mayo Clinic)H-Index: 19
view all 7 authors...
PURPOSE: It is unclear if a 3D transducer with the special design of mechanical swing or 2D array could provide acceptable 2D grayscale image quality for the general diagnosis purpose. The aim of this study is to compare the 2D image quality of a 3D intracavitary transducer with a conventional 2D intracavitary transducer using clinically relevant phantom experiments. METHODS: All measurements were performed on a GE Logiq E9 scanner with both a 2D (IC5-9-D) and a 3D (RIC5-9-D) transducer used in ...
1 CitationsSource
#1Shengzhen TaoH-Index: 13
#2Kishore RajendranH-Index: 11
Last. Shuai LengH-Index: 53
view all 6 authors...
: Multi-energy CT acquires simultaneous multiple x-ray attenuation measurements from different energy spectra which facilitates the computation of virtual monoenergetic images (VMI) at a specific photon energy (keV). Since the contrast between iodine attenuation and the attenuation of surrounding soft tissues increases at lower x-ray energies, VMIs in the range of 40-70 keV can be used to improve iodine visualization. However, at lower energy levels, image noise in VMIs is substantially increase...
2 CitationsSource
#1Hao Gong (Mayo Clinic)H-Index: 8
#2Lifeng Yu (Mayo Clinic)H-Index: 48
Last. Cynthia H. McCollough (Mayo Clinic)H-Index: 76
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PURPOSE: This work aims to develop a new framework of image quality assessment using deep learning-based model observer (DL-MO) and to validate it in a low-contrast lesion detection task that involves CT images with patient anatomical background. METHODS: The DL-MO was developed using the transfer learning strategy to incorporate a pretrained deep convolutional neural network (CNN), a partial least square regression discriminant analysis (PLS-DA) model and an internal noise component. The CNN wa...
10 CitationsSource