Faculty Bibliography

PURPOSE: To assess the correlation between each of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) metrics in renal parenchyma with renal function, in a cohort of patients with chronic liver disease. MATERIALS AND METHODS: Thirty patients with liver disease underwent abdominal MRI at 1.5T, including a coronal respiratory-triggered IVIM-DWI sequence and a coronal 3D FLASH DCE-MRI acquisition. Diffusion signals in the renal cortex and medulla were fitted to the IVIM model to estimate the diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF). The apparent diffusion coefficient (ADC) was calculated using all b-values. The glomerular filtration rate (GFR), cortical and medullary renal plasma flow (RPF), mean transit times (MTT) of vascular and tubular compartments and the whole kidney, were calculated from DCE-MRI data by fitting to a three-compartment model. The estimated GFR (eGFR) was calculated from serum creatinine measured 30 +/- 27 days of MRI. RESULTS: ADC, PF, and RPF were significantly higher in renal cortex vs. medulla (P < 10-5 ). DCE-MRI GFR significantly correlated with, but underestimated, eGFR (Spearman's r/P = 0.49/0.01). IVIM-DWI parameters were not significantly correlated with eGFR. DCE-MRI GFR correlated weakly with D (cortex, r/P = 0.3/0.03; medulla r/P = 0.27/0.05) and ADC (cortex r/P = 0.28/0.04; medulla r/P = 0.34/0.01). Weak correlations were observed for pooled cortical and medullar RPF with PF (r/P = 0.32/10-3 ) and with ADC (r/P = 0.29/0.0025). Significant negative correlations were observed for vascular MTT with cortical D* (r/P = -0.38/0.004) and D*xPF (r/P = -0.34/0.01). CONCLUSION: The weak correlations between renal IVIM and DCE-MRI perfusion parameters imply that these functional measures could be complementary. J. Magn. Reson. Imaging 2016.

Objectives In dynamic PET a plasma arterial input function (AIF) is required to quantify data with a compartment model. As this is cumbersome to acquire and causes discomfort to the subject, alternative methods based on an image derived input function (IDIF) have been explored. We report here a semi-automatic 2 stage algorithm for extracting the IDIF directly from dynamic PET images, without the need for a coregistered anatomical image. Methods Stage I Given a seed region R, find a sphere of diameter D adjacent to R with the highest peak value of its time activity curve (TAC). Stage II (vessel tracking): Take a step size S in either direction along a set of discretized lines passing through center of the current sphere Si and construct the next sphere Si+1 of diameter D. Select the direction that produces a TAC with the closest match to Si . Continue until the specified maximum length is reached or tracking fails. The algorithm was implemented in the software FireVoxel (wp.nyu.edu/FireVoxel) and applied to FDG PET (Siemens HR+) data for n=9 subjects for which a direct invasive measure of AIF was available (see below). Parameter estimates of K1, k2, k3 and KI for the FDG model were made using the IDIF as it was derived and using a linear correction technique (for partial volume and whole blood to plasma) (Zhou et al, 2011) that requires a few measured plasma samples. An exponential fit to the IDIF was used for smoothing and the ratios of the IDIF to measured samples at 3 points were fit to a straight line after 10 minutes. The IDIF was corrected using this linear function to form IDIFcorr. Results are reported for the average (over subjects and 7 brain ROIs) of K1, K1/k2, k3 and KI, the influx constant using AIF (plasma from left radial artery collected on the Ole-Dich blood sampler with 5 sec samples around the peak, 6mCi dose), IDIF and IDIFcorr. To assess the ability of the two IDIFs to correctly rank subjects, differences in rank position of the 9 subjects between invasively sampled AIF and the IDIFs were calculated for each ROI. Results A typical AIF, IDIF and IDIFcorr are shown in the figure. Average model parameters were K1: 0.099 +/- 0.013 (AIF), 0.16 +/- 0.078 (IDIFcorr),0.284 +/- .077 (IDIF);K1/k2: 0.58 +/- 0.15 (AIF), 0.50 +/- 0.14 (IDIFcorr), 1.40 +/- 0.32 (IDIF); k3: .069 +/- .013 (AIF), 0.074 +/- 0.017 (IDIFcorr), 0.039 +/- 0.007 (IDIF); KI: 0.0276 +/- 0.005 (AIF), 0.0284 +/- 0.0049 (IDIFcorr), 0.044 +/- 0.0088 (IDIF). KI AIF vs KI IDIFcorr slope 1.05, int=-.005 and correlation .989 (ICC=0.97). KI AIF vs IDIF slope 1.4, int .0053 and correlation 0.73 (ICC=-.32). Average displacements from AIF rank among 9 subjects were using IDIFcorr K1 1.41, k3 1.62, KI 0.44 and using IDIF K1 2.63 k3 1.52 KI 2.02. Conclusions Good agreement was found for KI for AIF and IDIFcorr. However due to underestimation of the AIF peak by the IDIF and IDIFcorr, K1 is higher for both. The underestimation of the peak is due to 30 second temporal sampling (compared to 5 sec for AIF) for first 5 min and low (with respect to vessel) spatial resolution of the HR+ PET camera which could not adequately capture AIF shape . However more important is the ability to correctly rank subjects and this is achieved using IDIFcorr for KI. For the IDIF the ranking was not as good. Better temporal sampling and spatial resolution will help in this respect. The combination of the automated IDIF and a calibration based on few venous plasma samples promises an adequate input function for dynamic FDG PET studies and may work for other PET tracers as well. (Figure Presented)

Objectives Small-animal PET imaging has been increasingly performed on mouse models for a variety of human CNS disorders, including all major neurodegenerative and psychiatric diseases. Despite the high demand, digital mouse brain atlases based on PET are still lacking. Furthermore, most of microPET imaging systems do not provide means of mapping mouse brain with atlas. For quantitative data analysis and accurate anatomical localization, the feasibility of automated atlas-based analysis on microPET mouse brain study data is presented. Methods MicroPET imaging studies were carried out after injection of PET radiotracers in isoflurane-anesthetized adult mice using Inveon PET/CT (Siemens Medical Solutions USA, Inc., Knoxville, TN, USA). The list mode dynamic PET data were collected for 30-60 minutes and rebinned using a Fourier rebinning algorithm. A CT scan was performed before each PET scan for the attenuation correction and anatomical co-registration. A 3D digital magnetic resonance microscopy (MRM)-based volume of interest (VOI) atlas generated from live C57BL/6J adult mouse brain was used for co-registration (Ma et al., 2008). Landmarks, including left and right centroids of midears and eyes (4 landmarks), were generated on atlas template and individual mouse CT images. Co-registration of atlas, CT and PET was performed using Firevoxel (https://urldefense.proofpoint.com/v2/url?u=https- 3A__wp.nyu.edu_Firevoxel&d=CwIBAg&c=j5oPpO0eBH1iio48DtsedbOBGmuw5jHLjgvtN2r4ehE&r=KRXe NoRy5_8lkSwAJG5vjS1yT0aFSItfe494dmkdSVs&m=m4p4If7jtxK4mApvrTCq8iNgX4cTetzdqV2S9E6aeVU& s=hOpst2v-A0_6qVCdD35fdl5nj-QMD1d8BZt95Wy5xr8&e= ) and the time-activity curves (TAC) for 20 specific 3D brain regions were generated. An expert in mouse neuroanatomy drew corresponding VOIs on PET-CT co-registered images derived from IRW (Inveon data analysis software without atlas). The TACs thus generated via both methods were compared. Results Automated co-registration of atlas template, CT and PET via either two sequential steps (co-register individual PET to CT, which in turn co-register to atlas template) or via a single step was tested using Firevoxel. Single step co-registration was accomplished in a few seconds and the TACs for specific ROIs of mouse brain were automatically generated after the co-registration (Supplement Data). In contrast, it took on an average 15 min to draw a single VOI (total 5 hours/mouse for 20 VOIs) directly on CT images using Inveon IRW without an atlas, a process that required an expert in mouse neuroanatomy to perform the task. Overall, the TACs for the corresponding VOIs derived from IRW and Firevoxel were similar in counts and shapes. Conclusions We have demonstrated the ability to map mouse brain with 20 VOI atlas for data analysis of microPET studies using Firevoxel. After the CT file with 4 landmarks is generated for individual mouse, the automated atlas-based co-registration process and the TAC generation can be achieved within one minute by one-button pressing. This novel time-saving data analysis methodology that can't be accomplished with the current microPET imaging system will facilitate accurate assessment and spatial localization of mouse brain function on mouse model studies for a variety of human CNS disorders

OBJECTIVE: To investigate the precision and accuracy of a new semi-automated method for kidney segmentation from single-breath-hold non-contrast MRI. MATERIALS AND METHODS: The user draws approximate kidney contours on every tenth slice, focusing on separating adjacent organs from the kidney. The program then performs a sequence of fully automatic steps: contour filling, interpolation, non-uniformity correction, sampling of representative parenchyma signal, and 3D binary morphology. Three independent observers applied the method to images of 40 kidneys ranging in volume from 94.6 to 254.5 cm3. Manually constructed reference masks were used to assess accuracy. RESULTS: The volume errors for the three readers were: 4.4 % +/- 3.0 %, 2.9 % +/- 2.3 %, and 3.1 % +/- 2.7 %. The relative discrepancy across readers was 2.5 % +/- 2.1 %. The interactive processing time on average was 1.5 min per kidney. CONCLUSIONS: Pending further validation, the semi-automated method could be applied for monitoring of renal status using non-contrast MRI.

OBJECTIVE: The purpose of this study was to determine whether qualitative and quantitative MRI feature analysis is useful for differentiating type 1 from type 2 papillary renal cell carcinoma (PRCC). MATERIALS AND METHODS: This retrospective study included 21 type 1 and 17 type 2 PRCCs evaluated with preoperative MRI. Two radiologists independently evaluated various qualitative features, including signal intensity, heterogeneity, and margin. For the quantitative analysis, a radiology fellow and a medical student independently drew 3D volumes of interest over the entire tumor on T2-weighted HASTE images, apparent diffusion coefficient parametric maps, and nephrographic phase contrast-enhanced MR images to derive first-order texture metrics. Qualitative and quantitative features were compared between the groups. RESULTS: For both readers, qualitative features with greater frequency in type 2 PRCC included heterogeneous enhancement, indistinct margin, and T2 heterogeneity (all, p < 0.035). Indistinct margins and heterogeneous enhancement were independent predictors (AUC, 0.822). Quantitative analysis revealed that apparent diffusion coefficient, HASTE, and contrast-enhanced entropy were greater in type 2 PRCC (p < 0.05; AUC, 0.682-0.716). A combined quantitative and qualitative model had an AUC of 0.859. Qualitative features within the model had interreader concordance of 84-95%, and the quantitative data had intraclass coefficients of 0.873-0.961. CONCLUSION: Qualitative and quantitative features can help discriminate between type 1 and type 2 PRCC. Quantitative analysis may capture useful information that complements the qualitative appearance while benefiting from high interobserver agreement.

BACKGROUND: To assess associations between whole-lesion apparent diffusion coefficient (ADC) metrics and pathologic findings of Likert score 3 prostate lesions at MRI/ultrasound fusion targeted biopsy. METHODS: This retrospective Institutional Review Board-approved study received a waiver of consent. We identified patients receiving a highest lesion score of 3 on 3 Tesla multiparametric MRI reviewed by a single experienced radiologist using a 5-point Likert scale and who underwent fusion biopsy. A total of 188 score 3 lesions in 158 patients were included. Three-dimensional volumes-of-interest encompassing each lesion were traced on ADC maps. Logistic regression was used to predict biopsy results based on whole-lesion ADC metrics and patient biopsy history. Biopsy yield was compared between metrics. RESULTS: By lesion, targeted biopsy identified tumor in 22.3% and Gleason score (GS) > 6 tumor in 8.5%, although results varied by biopsy history: biopsy-naive (n = 80), 20.0%/8.8%; prior negative biopsy (n = 53), 9.4%/1.9%; prior positive biopsy (n = 55): 40.0%/14.5%. Biopsy history, whole-lesion mean ADC, whole-lesion ADC10-25 , and whole-lesion ADC25-50 were each significantly associated with tumor or GS > 6 tumor at fusion biopsy (P 6 tumor, which was significantly higher (P < 0.001) than specificity of PSA (17.5%) at identical sensitivity. CONCLUSION: For score 3 lesions in patients without prior negative biopsy, whole-lesion ADC metrics help detect GS > 6 cancer while avoiding negative biopsies. However, deferral of fusion biopsy may be considered for score 3 lesions in patients with prior negative biopsy (without applying whole-lesion ADC metrics) given exceedingly low ( approximately 2%) frequency of GS > 6 tumor in this group. J. Magn. Reson. Imaging 2015.

INTRODUCTION: Hypertension, hypercholesterolemia, and obesity increase the risk of dementia. Although their detection is commonly followed by an introduction of treatment, little is known about how medications frequently used to treat vascular risk affect amyloid deposition. METHODS: A cross-sectional study of 156 subjects who underwent positron emission tomography with PiB. Using linear regression, we tested whether blood pressure, cholesterol, overweight/obese status, angiotensin receptor blockers (ARBs), beta-blockers, diuretics, angiotensin converting enzyme inhibitors, and statins predicted amyloid deposition. RESULTS: The use of ARBs (beta = -.15, P = .044) and diuretics (beta = -.20, P = .006) predicted less amyloid accumulation; older age (beta = .29, P < .001) and statins (beta = .23, P = .004) were related to greater amyloid deposition. Overweight and/or obese women had more cortical amyloid than their peers. DISCUSSION: Prospective studies should confirm effects of drugs and increased body weight on amyloid accumulation and establish whether they translate into measurable clinical outcomes. Women may be more susceptible to harmful effects of obesity.

Dynamic contrast enhanced (DCE) MRI has emerged as a reliable and diagnostically useful functional imaging technique. DCE protocol typically lasts 3-15 minutes and results in a time series of N volumes. For automated analysis, it is important that volumes acquired at different times be spatially coregistered. We have recently introduced a novel 4D, or volume time series, coregistration tool based on a user-specified target volume of interest (VOI). However, the relationship between coregistration accuracy and target VOI size has not been investigated. In this study, coregistration accuracy was quantitatively measured using various sized target VOIs. Coregistration of 10 DCE-MRI mouse head image sets were performed with various sized VOIs targeting the mouse brain. Accuracy was quantified by measures based on the union and standard deviation of the coregistered volume time series. Coregistration accuracy was determined to improve rapidly as the size of the VOI increased and approached the approximate volume of the target (mouse brain). Further inflation of the VOI beyond the volume of the target (mouse brain) only marginally improved coregistration accuracy. The CPU time needed to accomplish coregistration is a linear function of N that varied gradually with VOI size. From the results of this study, we recommend the optimal size of the VOI to be slightly overinclusive, approximately by 5 voxels, of the target for computationally efficient and accurate coregistration.

OBJECTIVE: The purpose of this study was to investigate the additional value of whole-lesion histogram apparent diffusion coefficient (ADC) metrics, when combined with standard pathologic features, in prediction of biochemical recurrence (BCR) after radical prostatectomy for prostate cancer. MATERIALS AND METHODS: The study included 193 patients (mean age, 61 +/- 7 years) who underwent 3-T MRI with DWI (b values, 50 and 1000 s/mm(2)) before prostatectomy. Histogram metrics were derived from 3D volumes of interest encompassing the entire lesion on ADC maps. Pathologic features from radical prostatectomy and subsequent BCR were recorded for each patient. The Fisher exact test and Mann-Whitney test were used to compare ADC-based metrics and pathologic features between patients with and patients without BCR. Stepwise logistic regression analysis was used to construct multivariable models for prediction of BCR, which were assessed by ROC analysis. RESULTS: BCR occurred in 16.6% (32/193) of patients. Variables significantly associated with BCR included primary Gleason grade, Gleason score, extraprostatic extension, seminal vesicle invasion, positive surgical margin, preoperative prostate-specific antigen level, MRI tumor volume, mean whole-lesion ADC, entropy ADC, and mean ADC of the bottom 10th, 10-25th, and 25-50th percentiles (p