Daniele Marin

Overview:

Liver Imaging
Dual Energy CT
CT Protocol Optimization
Dose Reduction Strategies for Abdominal CT Applications

Positions:

Associate Professor of Radiology

Radiology, Abdominal Imaging
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

M.D. 2003

Sapienza University of Rome (Italy)

Grants:

LowEr Administered Dose with highEr Relaxivity: Gadovist vs Dotarem (LEADER 75)

Administered By
Radiology, Abdominal Imaging
Awarded By
Bayer Healthcare Pharmaceuticals Inc
Role
Principal Investigator
Start Date
End Date

Dual-Shot NCOM Power Contract Injector Study

Administered By
Radiology, Abdominal Imaging
Awarded By
Nemoto Kyorindo Co., Ltd.
Role
Principal Investigator
Start Date
End Date

Optimization of a Frequency-Based Fusion Technique for Improving the Image Quality on Low Energy Virtual Monochromatic Images from Dual Energy CT

Administered By
Radiology, Abdominal Imaging
Awarded By
Radiological Society of North America
Role
Principal Investigator
Start Date
End Date

Toward Precise and Accurate Assessment of Dose Reduction Using Iterative Reconstruction Methods for Abdominal Imaging Applications

Administered By
Radiology, Abdominal Imaging
Awarded By
Society of Abdominal Radiology
Role
Principal Investigator
Start Date
End Date

CT Research Fellowship in Dual Energy and Deep Learning Image Reconstruction

Administered By
Radiology, Abdominal Imaging
Awarded By
GE Healthcare
Role
Principal Investigator
Start Date
End Date

Publications:

Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.

PURPOSE: Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. METHODS: An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis. RESULTS: MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = -0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean = -0.04%, 95% limits of agreement = [-0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = -0.21%, 95% limits of agreement = [-1.47%, 1.06%]; CT: mean = -0.18HU, 95% limits of agreement = [-1.96HU, 1.6HU]) were demonstrated. CONCLUSIONS: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.
Authors
Zhao, R; Hernando, D; Harris, DT; Hinshaw, LA; Li, K; Ananthakrishnan, L; Bashir, MR; Duan, X; Ghasabeh, MA; Kamel, IR; Lowry, C; Mahesh, M; Marin, D; Miller, J; Pickhardt, PJ; Shaffer, J; Yokoo, T; Brittain, JH; Reeder, SB
MLA Citation
Zhao, Ruiyang, et al. “Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.Med Phys, vol. 48, no. 8, Aug. 2021, pp. 4375–86. Pubmed, doi:10.1002/mp.15038.
URI
https://scholars.duke.edu/individual/pub1484213
PMID
34105167
Source
pubmed
Published In
Med Phys
Volume
48
Published Date
Start Page
4375
End Page
4386
DOI
10.1002/mp.15038

Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.

OBJECTIVE: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. METHODS: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. RESULTS: For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. CONCLUSIONS: The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.
Authors
Lyu, P; Neely, B; Solomon, J; Rigiroli, F; Ding, Y; Schwartz, FR; Thomsen, B; Lowry, C; Samei, E; Marin, D
MLA Citation
Lyu, Peijie, et al. “Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence.Eur J Radiol, vol. 141, Aug. 2021, p. 109825. Pubmed, doi:10.1016/j.ejrad.2021.109825.
URI
https://scholars.duke.edu/individual/pub1484424
PMID
34144309
Source
pubmed
Published In
Eur J Radiol
Volume
141
Published Date
Start Page
109825
DOI
10.1016/j.ejrad.2021.109825

Automated coronary calcium scoring using deep learning with multicenter external validation.

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80-100% and 87-100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71-94% and positive predictive values in the range of 88-100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions.
Authors
Eng, D; Chute, C; Khandwala, N; Rajpurkar, P; Long, J; Shleifer, S; Khalaf, MH; Sandhu, AT; Rodriguez, F; Maron, DJ; Seyyedi, S; Marin, D; Golub, I; Budoff, M; Kitamura, F; Takahashi, MS; Filice, RW; Shah, R; Mongan, J; Kallianos, K; Langlotz, CP; Lungren, MP; Ng, AY; Patel, BN
MLA Citation
Eng, David, et al. “Automated coronary calcium scoring using deep learning with multicenter external validation.Npj Digit Med, vol. 4, no. 1, June 2021, p. 88. Pubmed, doi:10.1038/s41746-021-00460-1.
URI
https://scholars.duke.edu/individual/pub1484336
PMID
34075194
Source
pubmed
Published In
Npj Digital Medicine
Volume
4
Published Date
Start Page
88
DOI
10.1038/s41746-021-00460-1

Left lateral segment liver volume is not correlated with anthropometric measures.

BACKGROUND: Liver transplantation is definitive therapy for end stage liver disease in pediatric patients. Living donor liver transplantation (LDLT) with the left lateral segment (LLS) is often a feasible option. However, the size of LLS is an important factor in donor suitability - particularly when the recipient weighs less than 10 kg. In the present study, we sought to define a formula for estimating left lateral segment volume (LLSV) in potential LLS donors. METHODS: We obtained demographic and anthropometric measurements on 50 patients with Computed Tomography (CT) scans to determine whole liver volume (WLV), right liver volume (RLV), and LLSV. We performed univariable and multivariable linear regression with backwards stepwise variable selection (p < 0.10) to determine final models. RESULTS: Our study found that previously reported anthropometric and demographics variables correlated with volume were significantly associated with WLV and RLV. On univariable analysis, no demographic or anthropometric measures were correlated with LLSV. On multivariable analysis, LLSV was poorly predicted by the final model (R2 = 0.10, Coefficient of Variation [CV] = 42.2) relative to WLV (R2 = 0.33, CV = 18.8) and RLV (R2 = 0.41, CV = 15.8). CONCLUSION: Potential LLS living donors should not be excluded based on anthropometric data: all potential donors should be evaluated regardless of their size.
Authors
Shaw, BI; Schwartz, FR; Samoylova, ML; Barbas, AS; McElroy, LM; Berg, C; Sudan, DL; Marin, D; Ravindra, KV
MLA Citation
Shaw, Brian I., et al. “Left lateral segment liver volume is not correlated with anthropometric measures.Hpb (Oxford), Apr. 2021. Pubmed, doi:10.1016/j.hpb.2021.04.018.
URI
https://scholars.duke.edu/individual/pub1481904
PMID
33980477
Source
pubmed
Published In
Hpb (Oxford)
Published Date
DOI
10.1016/j.hpb.2021.04.018

Diagnostic performance of single-phase dual-energy CT to differentiate vascular and nonvascular incidental renal lesions on portal venous phase: comparison with CT.

OBJECTIVES: To determine whether single-phase dual-energy CT (DECT) differentiates vascular and nonvascular renal lesions in the portal venous phase (PVP). Optimal iodine threshold was determined and compared to Hounsfield unit (HU) measurements. METHODS: We retrospectively included 250 patients (266 renal lesions) who underwent a clinically indicated PVP abdominopelvic CT on a rapid-kilovoltage-switching single-source DECT (rsDECT) or a dual-source DECT (dsDECT) scanner. Iodine concentration and HU measurements were calculated by four experienced readers. Diagnostic accuracy was determined using biopsy results and follow-up imaging as reference standard. Area under the curve (AUC) was calculated for each DECT scanner to differentiate vascular from nonvascular lesions and vascular lesions from hemorrhagic/proteinaceous cysts. Univariable and multivariable logistic regression analyses evaluated the association between variables and the presence of vascular lesions. RESULTS: A normalized iodine concentration threshold of 0.25 mg/mL yielded high accuracy in differentiating vascular and nonvascular lesions (AUC 0.93, p < 0.001), with comparable performance to HU measurements (AUC 0.93). Both iodine concentration and HU measurements were independently associated with vascular lesions when adjusted for age, gender, body mass index, and lesion size (AUC 0.95 and 0.95, respectively). When combined, diagnostic performance was higher (AUC 0.96). Both absolute and normalized iodine concentrations performed better than HU measurements (AUC 0.92 vs. AUC 0.87) in differentiating vascular lesions from hemorrhagic/proteinaceous cysts. CONCLUSION: A single-phase (PVP) DECT scan yields high accuracy to differentiate vascular from nonvascular renal lesions. Iodine concentration showed a slightly higher performance than HU measurements in differentiating vascular lesions from hemorrhagic/proteinaceous cysts. KEY POINTS: • A single-phase dual-energy CT scan in the portal venous phase differentiates vascular from nonvascular renal lesions with high accuracy (AUC 0.93). • When combined, iodine concentration and HU measurements showed the highest diagnostic performance (AUC 0.96) to differentiate vascular from nonvascular renal lesions. • Compared to HU measurements, iodine concentration showed a slightly higher performance in differentiating vascular lesions from hemorrhagic/proteinaceous cysts.
Authors
Mastrodicasa, D; Willemink, MJ; Madhuripan, N; Chima, RS; Ho, AA; Ding, Y; Marin, D; Patel, BN
MLA Citation
Mastrodicasa, Domenico, et al. “Diagnostic performance of single-phase dual-energy CT to differentiate vascular and nonvascular incidental renal lesions on portal venous phase: comparison with CT.Eur Radiol, vol. 31, no. 12, Dec. 2021, pp. 9600–11. Pubmed, doi:10.1007/s00330-021-08097-0.
URI
https://scholars.duke.edu/individual/pub1484721
PMID
34114058
Source
pubmed
Published In
Eur Radiol
Volume
31
Published Date
Start Page
9600
End Page
9611
DOI
10.1007/s00330-021-08097-0