Christopher C. Conlin
University of California, San Diego
SignalCancerUrologyDiffusion (business)Biomedical engineeringRenal blood flowMagnetic resonance imagingPerfusionDiffusion MRIResamplingArtificial intelligenceRenal functionBayesian information criterionEstimating equationsBiopsyProstateCirrhosisKidneyCorrelation coefficientProstate cancerCreatinineCalf musclesNuclear medicineCalf muscleDynamic contrastArterial diseaseArterial spin labelingConfidence intervalVoxelReceiver operating characteristicEffective diffusion coefficientMedicine
17Publications
3H-index
35Citations
Publications 18
Newest
#1Zhong Ay (UCSD: University of California, San Diego)
#2Leonardino A. Digma (UCSD: University of California, San Diego)H-Index: 2
Last. Tyler M. SeibertH-Index: 18
view all 16 authors...
PurposeMultiparametric MRI (mpMRI) improves detection of clinically significant prostate cancer (csPCa), but the qualitative PI-RADS system and quantitative apparent diffusion coefficient (ADC) yield inconsistent results. An advanced Restrictrion Spectrum Imaging (RSI) model may yield a better quantitative marker for csPCa, the RSI restriction score (RSIrs). We evaluated RSIrs for patient-level detection of csPCa. Materials and MethodsRetrospective analysis of men who underwent mpMRI with RSI an...
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#1Christine H. Feng (UCSD: University of California, San Diego)H-Index: 5
#2Christopher C. Conlin (UCSD: University of California, San Diego)H-Index: 3
Last. Tyler M. Seibert (UCSD: University of California, San Diego)H-Index: 18
view all 11 authors...
BACKGROUND Diffusion magnetic resonance imaging (MRI) is integral to detection of prostate cancer (PCa), but conventional apparent diffusion coefficient (ADC) cannot capture the complexity of prostate tissues and tends to yield noisy images that do not distinctly highlight cancer. A four-compartment restriction spectrum imaging (RSI4 ) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4 -C1 , yielded g...
1 CitationsSource
#1Maren M. Sjaastad Andreassen (NTNU: Norwegian University of Science and Technology)H-Index: 1
#2Ana E. Rodríguez-Soto (UCSD: University of California, San Diego)H-Index: 7
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 18 authors...
Purpose: Diffusion-weighted MRI (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between predefined benign and malignant breast lesions. However, how well DW-MRI discriminates cancer from all other breast tissue voxels in a clinical setting is unknown. Here we explore the voxelwise ability to distinguish cancer from healthy breast tissue using signal contributions from the newly developed three-component multi-b-value DW-MRI model. Experimental Design: Patients ...
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#1Christopher C. Conlin (UCSD: University of California, San Diego)H-Index: 3
#2Christine H. Feng (UCSD: University of California, San Diego)H-Index: 5
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 10 authors...
BACKGROUND Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. PURPOSE To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. STUDY TYPE Retrospective. SUBJECTS Forty-six patients who underwent MRI examination for suspected prostate cancer; 2...
1 CitationsSource
#1Leonardino A. Digma (UCSD: University of California, San Diego)H-Index: 2
#2Christine H. Feng (UCSD: University of California, San Diego)H-Index: 5
Last. Tyler M. Seibert (UCSD: University of California, San Diego)H-Index: 18
view all 12 authors...
BackgroundAccurate imaging of bone metastases is necessary for treatment planning and assessing treatment response. Diffusion-weighted magnetic resonance imaging (DWI) can detect bone metastases, but DWI acquired with echo-planar imaging is susceptible to distortions due to static magnetic field inhomogeneities. PurposeEstimate spatial displacements of bone lesions on DWI. Examine whether distortion-corrected DWI more accurately reflects underlying anatomy. Study TypeRetrospective. Subjects18 pa...
Source
#1Maren M. Sjaastad Andreassen (NTNU: Norwegian University of Science and Technology)H-Index: 1
#2Ana E. Rodríguez-Soto (UCSD: University of California, San Diego)H-Index: 7
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 18 authors...
Purpose: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a contrast-free modality that has demonstrated ability to discriminate between pre-defined benign and malignant breast lesions. However, the ability of DW-MRI to discriminate cancer tissue from all other breast tissues on a voxel-level in a clinical setting is unknown. Here we explore the ability to distinguish breast cancer from healthy breast tissues using signal contributions from the newly developed three-component multi-b-va...
Source
#1Jeff L. Zhang (Harvard University)H-Index: 18
#2Christopher C. Conlin (UofU: University of Utah)H-Index: 3
Last. Vivian S. LeeH-Index: 72
view all 7 authors...
Exercise-induced hyperemia in calf muscles was recently shown to be quantifiable with high-resolution magnetic resonance imaging (MRI). However, processing of the MRI data to obtain muscle-perfusion maps is time-consuming. This study proposes to substantially accelerate the mapping of muscle perfusion using a deep-learning method called artificial neural network (NN). Forty-eight MRI scans were acquired from 21 healthy subjects and patients with peripheral artery disease (PAD). For optimal train...
Source
#1Christine H. Feng (UCSD: University of California, San Diego)H-Index: 5
#2Christopher C. Conlin (UCSD: University of California, San Diego)H-Index: 3
Last. Tyler M. Seibert (UCSD: University of California, San Diego)H-Index: 18
view all 11 authors...
Purpose: Diffusion MRI is integral to detection of prostate cancer (PCa), but conventional ADC cannot capture the complexity of prostate tissues. A four-compartment restriction spectrum imaging (RSI4) model was recently found to optimally characterize pelvic diffusion signals, and the model coefficient for the slowest diffusion compartment, RSI4-C1, yielded greatest tumor conspicuity. In this study, RSI4-C1 was evaluated as a quantitative voxel-level classifier of PCa. Methods: This was a retros...
Source
#1Christopher C. Conlin (UCSD: University of California, San Diego)H-Index: 3
#2Christine H. Feng (UCSD: University of California, San Diego)H-Index: 5
Last. Anders M. Dale (UCSD: University of California, San Diego)H-Index: 166
view all 10 authors...
Background Optimizing a restriction spectrum imaging (RSI) model for the prostate could lead to improved characterization of diffusion in the prostate and better discrimination of tumors. Purpose To determine optimal apparent diffusion coefficients (ADCs) for prostate RSI models and evaluate the number of tissue compartments required to best describe diffusion in prostate tissue. Study Type Retrospective. Population/Subjects Thirty-six patients who underwent an extended MRI examination for suspe...
1 CitationsSource
#1Xiaowan Li (UofU: University of Utah)H-Index: 1
#2Christopher C. Conlin (UofU: University of Utah)H-Index: 3
Last. Jeff L. Zhang (UofU: University of Utah)H-Index: 18
view all 10 authors...
Abstract It is often difficult to accurately localize small arteries in images of peripheral organs, and even more so with vascular abnormality vasculatures, including collateral arteries, in peripheral artery disease (PAD). This poses a challenge for manually sampling arterial input function (AIF) in quantifying dynamic contrast-enhanced (DCE) MRI data of peripheral organs. In this study, we designed a multi-step screening approach that utilizes both the temporal and spatial information of the ...
1 CitationsSource