Faculty Bibliography

Objectives: MicroPET imaging has been increasingly performed on mouse models for a variety of human CNS disorders. Despite high demand, digital mouse brain atlases based on PET are still lacking. Further, most microPET systems do not provide means of mapping mouse brain with atlas. For quantitative data analysis and accurate anatomical localization, the development and evaluation of an automated atlas-based data analysis on microPET mouse brain studies is presented. Methods: MicroPET imaging studies were performed after injection of F-18 labeled Amyvid (a tracer for imaging amyloid (Aa) plaques) in isoflurane-anesthetized adult mice using Inveon PET/CT (Siemens). The list mode dynamic PET data were collected for 30-60 min and rebinned using a Fourier rebinning algorithm. A CT scan was also performed for 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 brain mapping (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 (FVX) (https://urldefense.proofpoint.com/v2/url?u=https- 3A__wp.nyu.edu_Firevoxel&d=DgIBAg&c=j5oPpO0eBH1iio48DtsedbOBGmuw5jHLjgvtN2r4ehE&r=KRXe NoRy5_8lkSwAJG5vjS1yT0aFSItfe494dmkdSVs&m=B4bFtJccWjUzJ- dbK1qURkxJmihDqjf87yIgZlYKTMk&s=soyp2V3_QGPs--q8qXcfkDHjv7kMngxeekpEknOQoi8&e= ) and time-activity curves (TAC) for 20 specific 3D brain regions were generated. For comparison, an expert in mouse neuroanatomy manually 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. For further evaluation, the tracer uptake and kinetics in both tau and Aa transgenic mouse models were also compared. Results: Using FVX, single step co-registration of atlas, CT and PET was accomplished in seconds (by one-button pressing) and the TACs for specific ROIs of mouse brain were automatically generated after co-registration. In contrast, it took an average of 15 min to manually 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. Overall, the TACs for the corresponding VOIs derived from IRW and FVX were similar in counts and shapes. Most importantly, this VOI atlas-based method can provide unbiased measures of radioactivity concentration from PET studies. The results from studies of tau vs. Aa transgenic mouse models after injection of Amyvid showed an apparent difference in the tracer uptake and kinetics (Fig. 1). Conclusions: We have demonstrated the feasibility to map mouse brain with an automated atlas-based co-registration for data analysis of microPET brain studies using FVX. This novel time-saving data analysis methodology, unachievable with current microPET imaging systems, will facilitate accurate assessment and spatial localization of brain signals in mouse model studies for a variety of human CNS disorders

Purpose To identify the ability of computer-derived three-dimensional (3D) computed tomographic (CT) segmentation techniques to help differentiate lung adenocarcinoma subtypes. Materials and Methods This study had institutional research board approval and was HIPAA compliant. Pathologically classified resected lung adenocarcinomas (n = 23) with thin-section CT data were identified. Two readers independently placed over-inclusive volumes around nodules from which automated computer measurements were generated: mass (total mass) and volume (total volume) of the nodule and of any solid portion, in addition to the solid percentage of the nodule volume (percentage solid volume) or mass (percentage solid mass). Interobserver agreement and differences in measurements among pathologic entities were evaluated by using t tests. A multinomial logistic regression model was used to differentiate the probability of three diagnoses: invasive non-lepidic-predominant adenocarcinoma (INV), lepidic-predominant adenocarcinoma (LPA), and adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA). Results Mean percentage solid volume of INV was 35.4% (95% confidence interval [CI]: 26.2%, 44.5%)-higher than the 14.5% (95% CI: 10.3%, 18.7%) for LPA (P = .002). Mean percentage solid volume of AIS/MIA was 8.2% (95% CI: 2.7%, 13.7%) and had a trend toward being lower than that for LPA (P = .051). Accuracy of the model based on total volume and percentage solid volume was 73.2%; accuracy of the model based on total mass and percentage solid mass was 75.6%. Conclusion Computer-assisted 3D measurement of nodules at CT had good reproducibility and helped differentiate among subtypes of lung adenocarcinoma. (c) RSNA, 2016.

OBJECTIVE: The aim of this study was to validate computed tomography (CT)-based longitudinal markers of the progression of Alzheimer disease (AD). MATERIALS AND METHODS: We retrospectively studied 33 AD patients and 39 nondemented patients with other neurological illnesses (non-AD) having 4 to 12 CT examinations of the head, with over a mean (SD) of 3.9 (1.7) years. At each time point, we applied an automatic software to measure whole brain, cerebrospinal fluid, and intracranial space volumes. Longitudinal measures were then related to disease status and time since the first scan using hierarchical models. RESULTS: Absolute brain volume loss accelerated for non-AD patients by 0.86 mL/y (95% confidence interval [CI], 0.64-1.08 mL/y) and 1.5x faster, that is, 1.32 mL/y (95% CI, 1.09-1.56 mL/y) for AD patients (P = 0.006). In terms of brain volume normalized to intracranial space, the acceleration in atrophy rate for non-AD patients was 0.0578%/y (95% CI, 0.0389%/y to 0.0767%/y), again 1.5x faster, that is, 0.0919%/y (95% CI, 0.0716%/y to 0.1122%/y) for AD patients (P = 0.017). This translates to an increase in atrophy rate from 0.5% to 1.4% in AD versus to 1.1% in non-AD group after 10 years. CONCLUSIONS: Brain volumetry on CT reliably detected accelerated volume loss in AD and significantly lower acceleration factor in age-matched non-AD patients, leading to the possibility of its use to monitor the progression of cognitive decline and dementia.

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.