Faculty Bibliography

Purpose Our aim was to evaluate the accuracy of a new algorithm to automatically delineate the breast region from the chest on T1-weighted, non-fat-suppressed MR images. This process is also referred to as the chest wall detection. There is a general agreement that this step is very difficult to automate. At the same time it is crucially needed for clinically important processing workflows [1]. These workflows include 3D measurement of breast density and of the breast parenchymal enhancement. Both measures reveal patients at risk of breast cancer [2]. Manually traced chest wall was used as the ground truth when estimating the segmentation errors. Segmentation accuracy was evaluated using the Hausdorff distance and the volumetric error. We also estimated the inter-observer agreement in defining the chest wall surface. Methods The program starts by generating the mid-sagittal 2D section by averaging the signal across 20 mm thick mid-sagittal slab. We determine the chest wall boundary on this image by modeling the signal profiles along the antero-posterior direction as a sequence of three tissues: background air, skin and fat layer, muscle. Non-uniformity correction is then applied to the entire 3D volume. The mid-sagittal boundary, represented as a polyline P, is then propagated in two opposite (left and right) directions. At each sagittal section the algorithm adjusts the control points of the polyline received from an adjacent slice. The adjustment is estimated from the weighted sum of six measures that combine specific local and global signal statistics. These include: the local gradient, the signal uniformity, the gradient similarity, the contour-gradient consistency, the global contour uniformity and the normal vector consistency. At each iteration we form a candidate shift vector, we apply it to shift P to its new position, and then we smooth the resulting polyline. The process terminates when the magnitude of the shift becomes negligible or when the specified number of iterations is exceeded. Two metrics were used to estimate accuracy. The conventional volumetric error was obtained by dividing the volume DV of misclassified breast voxels over the true breast volume V. The Hausdorff distance, HD, is the distance between each voxel on the true breast/ chest wall border and the closest boundary voxel produced by the algorithm. HD is averaged over the entire chest wall surface. From a clinical database of screening breast MRIs acquired at our medical center we have randomly selected 16 test exams. The selection was constrained to enforce that there were four exams in each of the four breast density categories [3]. Bilateral breasts were imaged on Siemens 3T Magnetom Trio equipped with a 7-element surface breast coil. The parameters of the T1-weighted non-fat-suppressed sequence were: TR = 4.74 ms, TE = 1.79 ms, FOV = 320 mm2, matrix = 448 9 358 9*150, 0.7 9 0.7 9 1.1 mm voxels, TA = 2-3 min. Three experts in breast and chest anatomy drew contours to separate the chest wall from the breast (Fig. 1). The pectoralis fascia and pectoralis muscles were used as reference points for the anterolateral borders. The medial border of the axilla was the posterolateral boundary. The axillary tail was considered as the breast tissue. The ground truth references were constructed by a software designed to perform voxel-based ROI averaging [4]. (Figure Presented) Results The border distance error HD was 0.84 +/- 0.8 mm (average +/- standard deviation) and ranged from 0.57 to 2.45 mm. The volume error DV/V was 6.43 +/- 6.82%. There was no correlation between the HD and DV/V (R2 = 0.23, p = 0.12). The test cases covered a wide range 411-3439 ml of breast volumes. There was a significant positive correlation (R2 = 0.40, p = 0.02) between volumetric error and the true breast volume V, but there was no correlation between HD and V (R2 = 0.08, p = 0.44). The average execution time was under 1.5 min per case on a standard 8-core workstation. The inter-observer agreement measured in term of HD was 0.56 +/- 0.15 mm (average +/- standard deviation). The agreement expressed in terms of volumetric discrepancy (relative to breast volume) was 1.61% +/- 0.71%. Conclusion Breast density, defined as fraction of fibroglandular tissue, and postcontrast enhancement, are considered significant risk factors for breast cancer. These MRI measures are recommended for radiologic reports and are promising cancer biomarkers. Radiologists currently visually estimate these measure. Unfortunately, readers agreement for qualitative evaluation is only fair, requiring better standardization and reproducibility. Computer-assisted quantitative assessment is needed, but the task is challenging due to image nonuniformity (breast coils cause loss of MR signal in remote regions) and to the anatomical complexity of chest wall boundary (Fig. 2). (Figure Presented) Given its accuracy and speed, our breast segmentation method appears to be ready for clinical use as a part of larger workflow to generate routine diagnostic reports

Alzheimer's disease (AD) is known to be associated with loss of cholinergic neurons in the nucleus basalis of Meynert, located in the posterior basal forebrain. Structural changes of septal nuclei, located in the anterior basal forebrain, have not been well studied in AD. Using a validated algorithm, we manually traced septal nuclei on high-resolution coronal magnetic resonance imaging (MRI) in 40 subjects with mild cognitive impairment (MCI) or AD, 89 healthy controls, and 18 subjects who were cognitively normal at the time of MRI but went on to develop AD an average of 2.8 years later. We found that cognitively normal subjects destined to develop AD in the future had enlarged septal nuclei as compared to both healthy controls and patients with current MCI or AD. To our knowledge, this is the first time a brain structure has been found to be enlarged in association with risk of AD. Further research is needed to determine if septal enlargement reflects neuroplastic compensation, amyloid deposition, inflammation, or another process and to determine whether it can serve as an early MRI biomarker of AD.

Cerebrospinal fluid (CSF) studies consistently show that CSF levels of amyloid-beta 1-42 (Aβ42) are reduced and tau levels increased prior to the onset of cognitive decline related to Alzheimer's disease (AD). However, the preclinical prediction accuracy for low CSF Aβ42 levels, a surrogate for brain Aβ42 deposits, is not high. Moreover, the pathology data suggests a course initiated by tauopathy contradicting the contemporary clinical view of an Aβ initiated cascade. CSF Aβ42 and tau data from 3 normal aging cohorts (45-90 years) were combined to test both cross-sectional (n = 766) and longitudinal (n = 651) hypotheses: 1) that the relationship between CSF levels of Aβ42 and tau are not linear over the adult life-span; and 2) that non-linear models improve the prediction of cognitive decline. Supporting the hypotheses, the results showed that a u-shaped quadratic fit (Aβ2) best describes the relationship for CSF Aβ42 with CSF tau levels. Furthermore we found that the relationship between Aβ42 and tau changes with age-between 45 and 70 years there is a positive linear association, whereas between 71 and 90 years there is a negative linear association between Aβ42 and tau. The quadratic effect appears to be unique to Aβ42, as Aβ38 and Aβ40 showed only positive linear relationships with age and CSF tau. Importantly, we observed the prediction of cognitive decline was improved by considering both high and low levels of Aβ42. Overall, these data suggest an earlier preclinical stage than currently appreciated, marked by CSF elevations in tau and accompanied by either elevations or reductions in Aβ42. Future studies are needed to examine potential mechanisms such as failing CSF clearance as a common factor elevating CSF Aβxx analyte levels prior to Aβ42 deposition in brain.

RATIONALE AND OBJECTIVES: This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas. MATERIALS AND METHODS: This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV. RESULTS: Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964). CONCLUSIONS: Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.

RATIONALE AND OBJECTIVES: This study aimed to assess a novel method of three-dimensional (3D) co-registration of prostate magnetic resonance imaging (MRI) examinations performed before and after prostate cancer focal therapy. MATERIALS AND METHODS: We developed a software platform for automatic 3D deformable co-registration of prostate MRI at different time points and applied this method to 10 patients who underwent focal ablative therapy. MRI examinations were performed preoperatively, as well as 1 week and 6 months post treatment. Rigid registration served as reference for assessing co-registration accuracy and precision. RESULTS: Segmentation of preoperative and postoperative prostate revealed a significant postoperative volume decrease of the gland that averaged 6.49 cc (P = .017). Applying deformable transformation based on mutual information from 120 pairs of MRI slices, we refined by 2.9 mm (max. 6.25 mm) the alignment of the ablation zone, segmented from contrast-enhanced images on the 1-week postoperative examination, to the 6-month postoperative T2-weighted images. This represented a 500% improvement over the rigid approach (P = .001), corrected by volume. The dissimilarity by Dice index of the mapped ablation zone using deformable transformation vs rigid control was significantly (P = .04) higher at the ablation site than in the whole gland. CONCLUSIONS: Our findings illustrate our method's ability to correct for deformation at the ablation site. The preliminary analysis suggests that deformable transformation computed from mutual information of preoperative and follow-up MRI is accurate in co-registration of MRI examinations performed before and after focal therapy. The ability to localize the previously ablated tissue in 3D space may improve targeting for image-guided follow-up biopsy within focal therapy protocols.

Purpose To assess the diagnostic performance of the callosal angle (CA) and Evans index (EI) measures and to determine their role versus automated volumetric methods in clinical radiology. Materials and Methods Magnetic resonance (MR) examinations performed before surgery (within 1-5 months of the MR examination) in 36 shunt-responsive patients with normal-pressure hydrocephalus (NPH; mean age, 75 years; age range, 58-87 years; 26 men, 10 women) and MR examinations of age- and sex-matched patients with Alzheimer disease (n = 34) and healthy control volunteers (n = 36) were studied. Three blinded observers independently measured EI and CA for each patient. Volumetric segmentation of global gray matter, white matter, ventricles, and hippocampi was performed by using software. These measures were tested by using multivariable logistic regression models to determine which combination of metrics is most accurate in diagnosis. Results The model that used CA and EI demonstrated 89.6%-93.4% accuracy and average area under the curve of 0.96 in differentiating patients with NPH from patients without NPH (ie, Alzheimer disease and healthy control). The regression model that used volumetric predictors of gray matter and white matter was 94.3% accurate. Conclusion CA and EI may serve as a screening tool to help the radiologist differentiate patients with NPH from patients without NPH, which would allow for designation of patients for further volumetric assessment. (c) RSNA, 2017.

Evidence supporting the hypothesis that reduced cerebrospinal fluid (CSF) clearance is involved in the pathophysiology of Alzheimer's disease (AD) comes from primarily from rodent models. However, unlike rodents where predominant extra-cranial CSF egress is via olfactory nerves traversing the cribriform plate, human CSF clearance pathways are not well characterized. Using dynamic Positron Emission Tomography (PET) with 18F-THK5117 a tracer for tau pathology, the ventricular CSF time activity was used as a biomarker for CSF clearance. We tested three hypotheses: 1. Extra-cranial CSF is detected at the superior turbinates; 2. CSF clearance is reduced in AD; and 3. CSF clearance is inversely associated with amyloid deposition. Methods: 15 subjects, 8 with AD and 7 normal control volunteers were examined with 18F-THK5117. 10 subjects additionally received 11C-PiB PET scans and 8 were PiB positive. Ventricular time activity curves (TAC) of 18F-THK5117 were used to identify highly correlated TAC from extra-cranial voxels. Results: For all subjects, the greatest density of CSF positive extra-cranial voxels was in the nasal turbinates. Tracer concentration analyses validated the superior nasal turbinate CSF signal intensity. AD patients showed ventricular tracer clearance reduced by 23% and 66% fewer superior turbinate CSF egress sites. Ventricular CSF clearance was inversely associated with amyloid deposition. Conclusion: The human nasal turbinate is part of the CSF clearance system. Lateral ventricle and superior nasal turbinates CSF clearance abnormalities are found in AD. Ventricular CSF clearance reductions are associated with increased brain amyloid depositions. These data suggest that PET measured CSF clearance is a biomarker of potential interest in AD and other neurodegenerative diseases.

Although MRI assessment of white matter lesions is essential for the clinical management of multiple sclerosis, the processes leading to the formation of lesions and underlying their subsequent MRI appearance are incompletely understood. We used proton MR spectroscopy to study the evolution of N-acetyl-aspartate (NAA), creatine (Cr), choline (Cho), and myo-inositol (mI) in pre-lesional tissue, persistent and transient new lesions, as well as in chronic lesions, and related the results to quantitative MRI measures of T1-hypointensity and T2-volume. Within 10 patients with relapsing-remitting course, there were 180 regions-of-interest consisting of up to seven semi-annual follow-ups of normal-appearing white matter (NAWM, n = 10), pre-lesional tissue giving rise to acute lesions which resolved (n = 3) or persisted (n = 3), and of moderately (n = 9) and severely hypointense (n = 6) chronic lesions. Compared with NAWM, pre-lesional tissue had higher Cr and Cho, while compared with lesions, pre-lesional tissue had higher NAA. Resolving acute lesions showed similar NAA levels pre- and post-formation, suggesting no long-term axonal damage. In chronic lesions, there was an increase in mI, suggesting accumulating astrogliosis. Lesion volume was a better predictor of axonal health than T1-hypointensity, with lesions larger than 1.5 cm3 uniformly exhibiting very low (<4.5 millimolar) NAA concentrations. A positive correlation between longitudinal changes in Cho and in lesion volume in moderately hypointense lesions implied that lesion size is mediated by chronic inflammation. These and other results are integrated in a discussion on the steady-state metabolism of lesion evolution in multiple sclerosis, viewed in the context of conventional MRI measures. Hum Brain Mapp, 2017. (c) 2017 Wiley Periodicals, Inc.