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The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI-Dynamic Contrast-Enhanced challenge

Shalom, Eve S; Kim, Harrison; van der Heijden, Rianne A; Ahmed, Zaki; Patel, Reyna; Hormuth, David A; DiCarlo, Julie C; Yankeelov, Thomas E; Sisco, Nicholas J; Dortch, Richard D; Stokes, Ashley M; Inglese, Marianna; Grech-Sollars, Matthew; Toschi, Nicola; Sahoo, Prativa; Singh, Anup; Verma, Sanjay K; Rathore, Divya K; Kazerouni, Anum S; Partridge, Savannah C; LoCastro, Eve; Paudyal, Ramesh; Wolansky, Ivan A; Shukla-Dave, Amita; Schouten, Pepijn; Gurney-Champion, Oliver J; Jiřík, Radovan; Macíček, Ondřej; Bartoš, Michal; Vitouš, Jiří; Das, Ayesha Bharadwaj; Kim, S Gene; Bokacheva, Louisa; Mikheev, Artem; Rusinek, Henry; Berks, Michael; Hubbard Cristinacce, Penny L; Little, Ross A; Cheung, Susan; O'Connor, James P B; Parker, Geoff J M; Moloney, Brendan; LaViolette, Peter S; Bobholz, Samuel; Duenweg, Savannah; Virostko, John; Laue, Hendrik O; Sung, Kyunghyun; Nabavizadeh, Ali; Saligheh Rad, Hamidreza; Hu, Leland S; Sourbron, Steven; Bell, Laura C; Fathi Kazerooni, Anahita
PURPOSE/OBJECTIVE: METHODS: RESULTS: CONCLUSIONS:
PMID: 38115695
ISSN: 1522-2594
CID: 5612382

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function

Bae, Jonghyun; Li, Chenyang; Masurkar, Arjun; Ge, Yulin; Kim, Sungheon Gene
PURPOSE:In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD:-10min methods in estimating the PS values. RESULTS:-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS:We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.
PMCID:10475161
PMID: 37507078
ISSN: 1095-9572
CID: 5591772

Evaluation of cellular water exchange in a mouse glioma model using dynamic contrast-enhanced MRI with two flip angles

Kiser, Karl; Zhang, Jin; Das, Ayesha Bharadwaj; Tranos, James A; Wadghiri, Youssef Zaim; Kim, Sungheon Gene
This manuscript aims to evaluate the robustness and significance of the water efflux rate constant (kio) parameter estimated using the two flip-angle Dynamic Contrast-Enhanced (DCE) MRI approach with a murine glioblastoma model at 7 T. The repeatability of contrast kinetic parameters and kio measurement was assessed by a test-retest experiment (n = 7). The association of kio with cellular metabolism was investigated through DCE-MRI and FDG-PET experiments (n = 7). Tumor response to a combination therapy of bevacizumab and fluorouracil (5FU) monitored by contrast kinetic parameters and kio (n = 10). Test-retest experiments demonstrated compartmental volume fractions (ve and vp) remained consistent between scans while the vascular functional measures (Fp and PS) and kio showed noticeable changes, most likely due to physiological changes of the tumor. The standardized uptake value (SUV) of tumors has a linear correlation with kio (R2 = 0.547), a positive correlation with Fp (R2 = 0.504), and weak correlations with ve (R2 = 0.150), vp (R2 = 0.077), PS (R2 = 0.117), Ktrans (R2 = 0.088) and whole tumor volume (R2 = 0.174). In the treatment study, the kio of the treated group was significantly lower than the control group one day after bevacizumab treatment and decreased significantly after 5FU treatment compared to the baseline. This study results support the feasibility of measuring kio using the two flip-angle DCE-MRI approach in cancer imaging.
PMCID:9945648
PMID: 36810898
ISSN: 2045-2322
CID: 5448162

Measuring subtle Blood-Brain Barrier permeability changes with reduced scan time in DCE-MRI

Bae, Jonghyun; Ge, Yulin; Kim, Sungheon Gene
Background: Increasing evidence suggests the subtle changes of Blood-Brain Barrier (BBB) permeability in normal aging and in Alzheimer"™s disease using Dynamic Contrast-Enhanced MRI (DCE-MRI). However, measuring this subtle change poses great challenge for accurate measurement, resulting in inconsistent results among previous studies. Two major challenges are long scan times, as suggested by previous studies and selection of the arterial input function (AIF). In this study, we aim to estimate the capillary level input function (CIF) using a deep learning network to overcome these two challenges. Methods: Healthy volunteers (n= 8, ages: 21-76) were recruited for DCE-MRI scan for 28min. Golden-angle RAdial Sampling Parallel (GRASP) sequence was used to obtain the dynamic images at ∼5s/frame. Individual AIF was sampled from the superior sagittal sinus of the brain (Fig.1a). FSL was used to segment the gray and white matters (Fig.1b). Each voxel was fitted using the graphical Patlak model (Fig.2a) to assess the vascular permeability-surface area product (PS) for both 28-min data and 10-min truncated data. We used a 3x3 kernel sliding through the images (Fig.3) and feed each voxel"™s dynamic as the input to our vision-transformer. Training data were generated using individual AIFs with a mathematical model, consisting of two Gaussian and one exponential function, and used to simulate dynamic patches using the Extended Patlak model (Fig.2b). Result: When the 10-min data are used, the conventional approach with AIF results in overestimation of PS when the scan-time is reduced, while the network-predicted CIF allows more accurate estimation, with refence to the results using the 28-min data, as illustrated by an example in Figure 4. Figure 5 shows the regional permeability differences between young and old subjects, where the conventional approach with AIF does not show the difference, while the approach with CIF shows subtle increases in PS with aging. Conclusion: Our proposed CIF-based approach provides an appropriate input-function for DCE analysis, allowing assessment of subtle permeability changes in the BBB.
SCOPUS:85144432351
ISSN: 1552-5260
CID: 5393872

Estimation of Contrast Agent Concentration in DCE-MRI Using 2 Flip Angles

Bharadwaj Das, Ayesha; Tranos, James Andrew; Zhang, Jin; Zaim Wadghiri, Youssef; Kim, Sungheon Gene
PURPOSE/OBJECTIVE:The aim of this study was to investigate the feasibility of using 2 flip angles (FAs) with an ultrashort echo time during dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for estimation of plasma gadolinium (Gd) concentration without using a precontrast longitudinal relaxation time T1 (T10) measurement. METHODS:T1-weighted DCE-MRI experiments were carried out with C57BL/6J mice using the scan protocol with 2 FAs over 3 sequential segments during 1 scan. The data with 2 FAs were used to estimate T10 (T1T) during conversion of a time-intensity curve to the time-concentration curve. Three dosages of gadolinium-based contrast agent were used to achieve a wide range of variability in Gd concentrations when measured at 10 minutes postinjection: 0.05 mmol/kg (n = 6), 0.1 mmol/kg (n = 11), and 0.15 mmol/kg (n = 7). For comparison, the signal-to-concentration conversion was also conducted using the T10 measured from the precontrast scan (T1M) as well as a constant T10 (2.1 seconds) from the literature (T1C). The Gd concentrations ([Gd]) estimated using DCE-MRI data for the time of retro-orbital blood collection ([Gd]T1T, [Gd]T1M, and [Gd]T1C, respectively) were compared against the [Gd] of the blood samples measured by inductively coupled plasma mass spectrometry ([Gd]MS). In addition, contrast kinetic model analysis was conducted on mice with GL261 brain tumors (n = 5) using the 3 different methods for T10. RESULTS:T1T strongly correlated with T1M (r = 0.81). [Gd]T1M and [Gd]T1T were significantly different from [Gd]T1C. [Gd]T1M and [Gd]T1T were in good agreement with [Gd]MS with strong correlations (mean percentage error ± standard deviation) of r = 0.70 (16% ± 56%) and r = 0.85 (15% ± 44%), respectively. In contrast, [Gd]T1C had a weak correlation of r = 0.52 with larger errors of 33% ± 24%. The contrast kinetic model parameters of GL261 brain tumors using T1T were not significantly different from those using T1M. CONCLUSIONS:This study substantiates the feasibility of using the 2-FA approach during DCE-MRI scan to estimate [Gd] in the plasma without using an extra scan to perform precontrast T1 measurements.
PMID: 35025833
ISSN: 1536-0210
CID: 5118942

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach

Bae, Jonghyun; Huang, Zhengnan; Knoll, Florian; Geras, Krzysztof; Pandit Sood, Terlika; Feng, Li; Heacock, Laura; Moy, Linda; Kim, Sungheon Gene
PURPOSE/OBJECTIVE:To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. METHODS:A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT/RESULTS:The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION/CONCLUSIONS:This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
PMID: 35001423
ISSN: 1522-2594
CID: 5118282

Rapid in Vitro Quantification of a Sensitized Gadolinium Chelate via Photoinduced Triplet Harvesting

Tranos, James A.; Das, Ayesha; Zhang, Jin; Hafeez, Sonia; Arvanitakis, Georgios N.; Thomson, Stuart A.J.; Khan, Suleiman; Pandya, Neelam; Kim, Sungheon Gene; Wadghiri, Youssef Z.
Gadolinium (Gd) based contrast agents (GBCAs) are widely used in magnetic resonance imaging (MRI) and are paramount to cancer diagnostics and tumor pharmacokinetic analysis. Accurate quantification of gadolinium concentration is essential to monitoring the biodistribution, clearance, and pharmacodynamics of GBCAs. However, current methods of quantifying gadolinium in blood or plasma (biological media) are both low throughput and clinically unavailable. Here, we have demonstrated the use of a sensitized gadolinium chelate, Gd[DTPA-cs124], as an MRI contrast agent that can be used to measure the concentration of gadolinium via luminescence quantification in biological media following transmetalation with a terbium salt. Gd[DTPA-cs124] was synthesized by conjugating carbostyril-124 (cs124) to diethylenetriaminepentaacetic acid (DTPA) and chelating to gadolinium. We report increases in both stability and relaxivity compared to the clinically approved analog Gd[DTPA] (gadopentetic acid or Magnevist). In vivo MRI experiments were conducted using C57BL6 mice in order to further illustrate the performance of Gd[DTPA-cs124] as an MRI contrast agent in comparison to Magnevist. Our results indicate that similar chemical modification to existing clinically approved GBCA may likewise provide favorable property changes, with the ability to be used in a gadolinium quantification assay. Furthermore, our assay provides a straightforward and high-throughput method of measuring gadolinium in biological media using a standard laboratory plate reader.
SCOPUS:85146178614
ISSN: 2470-1343
CID: 5408602

Bilateral gradient-echo spectroscopic imaging with correction of frequency variations for measurement of fatty acid composition in mammary adipose tissue

Baboli, Mehran; Storey, Pippa; Sood, Terlika Pandit; Fogarty, Justin; Moccaldi, Melanie; Lewin, Alana; Moy, Linda; Kim, Sungheon Gene
PURPOSE/OBJECTIVE:To develop a simultaneous dual-slab three-dimensional gradient-echo spectroscopic imaging (GSI) technique with frequency drift compensation for rapid (<6 min) bilateral measurement of fatty acid composition (FAC) in mammary adipose tissue. METHODS:A bilateral GSI sequence was developed using a simultaneous dual-slab excitation followed by 128 monopolar echoes. A short train of navigator echoes without phase or partition encoding was included at the beginning of each pulse repetition time period to correct for frequency variation caused by respiration and heating of the cryostat. Voxel-wise spectral fitting was applied to measure the areas of the lipid spectral peaks to estimate the number of double-bond (ndb), number of methylene-interrupted double-bond (nmidb), and chain length (cl). The proposed method was tested in an oil phantom and 10 postmenopausal women to assess the influence of the frequency variation on FAC estimation. RESULTS:The frequency drift observed over 5:27 min during the phantom scan was about 10 Hz. Phase correction based on the navigator reduced the median error of ndb, nmidb, and cl from 9.7%, 17.6%, and 3.2% to 2.1%, 9.5%, and 2.8%, respectively. The in vivo data showed a mean ± standard deviation frequency drift of 17.4 ± 2.5 Hz, with ripples at 0.3 ± 0.1 Hz. Our reconstruction algorithm successfully separated signals from the left and right breasts with negligible residual aliasing. Phase correction reduced the interquartile range within each subject's adipose tissue of ndb, nmidb, and cl by 18.4 ± 10.6%, 18.5 ± 13.9%, and 18.4 ± 10.6%, respectively. CONCLUSION/CONCLUSIONS:This study shows the feasibility of obtaining bilateral spectroscopic imaging data in the breast and that incorporation of a frequency navigator improves the estimation of FAC.
PMID: 33533056
ISSN: 1522-2594
CID: 4788292

Measurement of cellular-interstitial water exchange time in tumors based on diffusion-time-dependent diffusional kurtosis imaging

Zhang, Jin; Lemberskiy, Gregory; Moy, Linda; Fieremans, Els; Novikov, Dmitry S; Kim, Sungheon Gene
PURPOSE/OBJECTIVE:) in tumors, both in animals and in humans. METHODS:) by adjusting the diffusion gradient strength. The tDKI data at each diffusion time t were used for a weighted linear least-squares fit method to estimate the diffusion-time-dependent diffusivity, D(t), and diffusional kurtosis, K(t). RESULTS:median and IQR of the two breast cancers were 70 (94) and 106 (92) ms. CONCLUSION/CONCLUSIONS:The results of this proof-of-concept study substantiate the feasibility of using tDKI to measure cellular-interstitial water exchange time without using an exogenous contrast agent.
PMID: 33634508
ISSN: 1099-1492
CID: 4795052

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

Shen, Yiqiu; Wu, Nan; Phang, Jason; Park, Jungkyu; Liu, Kangning; Tyagi, Sudarshini; Heacock, Laura; Kim, S Gene; Moy, Linda; Cho, Kyunghyun; Geras, Krzysztof J
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work well for natural images might not be applicable to medical image analysis. In this work, we propose a novel neural network model to address these unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a prediction. While existing methods often require lesion segmentation during training, our model is trained with only image-level labels and can generate pixel-level saliency maps indicating possible malignant findings. We apply the model to screening mammography interpretation: predicting the presence or absence of benign and malignant lesions. On the NYU Breast Cancer Screening Dataset, our model outperforms (AUC = 0.93) ResNet-34 and Faster R-CNN in classifying breasts with malignant findings. On the CBIS-DDSM dataset, our model achieves performance (AUC = 0.858) on par with state-of-the-art approaches. Compared to ResNet-34, our model is 4.1x faster for inference while using 78.4% less GPU memory. Furthermore, we demonstrate, in a reader study, that our model surpasses radiologist-level AUC by a margin of 0.11.
PMID: 33383334
ISSN: 1361-8423
CID: 4759232