Publications by Year: 2017

E George, P Liacouras, TC Lee, and D Mitsouras. 2017. “3D-Printed Patient-Specific Models for CT-and MRI-Guided Procedure Planning.” AJNR Am J Neuroradiol, 38, 7, Pp. E46-7.
Andre Mastmeyer, Guillaume Pernelle, Ruibin Ma, Lauren Barber, and Tina Kapur. 2017. “Accurate Model-based Segmentation of Gynecologic Brachytherapy Catheter Collections in MRI-images.” Med Image Anal, 42, Pp. 173-88.Abstract
The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue. We describe here a precise, accurate method for achieving catheter segmentation and visualization. The algorithm, with the assistance of manually provided tip locations, performs segmentation using image-features, and is guided by a catheter-specific, estimated mechanical model. A final quality control step removes outliers or conflicting catheter trajectories. The mean Hausdorff error on a 54 patient, 760 catheter reference database was 1.49  mm; 51 of the outliers deviated more than two catheter widths (3.4  mm) from the gold standard, corresponding to catheter identification accuracy of 93% in a Syed-Neblett template. In a multi-user simulation experiment for evaluating RMS precision by simulating varying manually-provided superior tip positions, 3σ maximum errors were 2.44  mm. The average segmentation time for a single catheter was 3 s on a standard PC. The segmentation time, accuracy and precision, are promising indicators of the value of this method for clinical translation of MR-guidance in gynecologic brachytherapy and other catheter-based interventional procedures.
Marc Niethammer, Kilian M Pohl, Firdaus Janoos, and William M Wells. 2017. “Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models.” SIAM J. Imaging Sci., 10, 3, Pp. 1069–1103.Abstract
Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating this uncertainty is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. However, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the active mean fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model, in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the tt icgbench dataset.
Rahul Sastry, Wenya Linda Bi, Steve Pieper, Sarah Frisken, Tina Kapur, William Wells, and Alexandra J Golby. 2017. “Applications of Ultrasound in the Resection of Brain Tumors.” J Neuroimaging, 27, 1, Pp. 5-15.Abstract

Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.

C Chennubhotla, LP Clarke, A Fedorov, D Foran, G Harris, E Helton, R Nordstrom, F Prior, D. Rubin, JH Saltz, E Shalley, and A Sharma. 2017. “An Assessment of Imaging Informatics for Precision Medicine in Cancer.” Yearb Med Inform, 26, 1, Pp. 110-9.Abstract
Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine.
David Black, Julian Hettig, Maria Luz, Christian Hansen, Ron Kikinis, and Horst Hahn. 2017. “Auditory Feedback to Support Image-Guided Medical Needle Placement.” Int J Comput Assist Radiol Surg, 12, 9, Pp. 1655-63.Abstract
PURPOSE: During medical needle placement using image-guided navigation systems, the clinician must concentrate on a screen. To reduce the clinician's visual reliance on the screen, this work proposes an auditory feedback method as a stand-alone method or to support visual feedback for placing the navigated medical instrument, in this case a needle. METHODS: An auditory synthesis model using pitch comparison and stereo panning parameter mapping was developed to augment or replace visual feedback for navigated needle placement. In contrast to existing approaches which augment but still require a visual display, this method allows view-free needle placement. An evaluation with 12 novice participants compared both auditory and combined audiovisual feedback against existing visual methods. RESULTS: Using combined audiovisual display, participants show similar task completion times and report similar subjective workload and accuracy while viewing the screen less compared to using the conventional visual method. The auditory feedback leads to higher task completion times and subjective workload compared to both combined and visual feedback. CONCLUSION: Audiovisual feedback shows promising results and establishes a basis for applying auditory feedback as a supplement to visual information to other navigated interventions, especially those for which viewing a patient is beneficial or necessary.
Lauren J O'Donnell, Yannick Suter, Laura Rigolo, Pegah Kahali, Fan Zhang, Isaiah Norton, Angela Albi, Olutayo Olubiyi, Antonio Meola, Walid I Essayed, Prashin Unadkat, Pelin Aksit Ciris, William M Wells, Yogesh Rathi, Carl-Fredrik Westin, and Alexandra J Golby. 2017. “Automated White Matter Fiber Tract Identification in Patients with Brain Tumors.” Neuroimage Clin, 13, Pp. 138-53.Abstract

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

PB Shyn, S Tremblay-Paquet, K Palmer, S Tatli, K Tuncali, OI Olubiyi, N Hata, and SG Silverman. 2017. “Breath-hold PET/CT-guided Tumor Ablation under General Anesthesia: Accuracy of Tumor Image Registration and Projected Ablation Zone Overlap.” Clin Radiol, 72, 3, Pp. 223-9.Abstract

AIM: To assess single-breath-hold combined positron-emission tomography/computed tomography (PET/CT) for accuracy of tumour image registration and projected ablation volume overlap in patients undergoing percutaneous PET/CT-guided tumour-ablation procedures under general anaesthesia. MATERIALS AND METHODS: Eight patients underwent 12 PET/CT-guided tumour-ablation procedures to treat 20 tumours in the lung, liver, or adrenal gland. Using breath-hold PET/CT, the centre of the tumour was marked on each PET and CT acquisition by four readers to assess two- (2D) and three-dimensional (3D) spatial misregistration. Overlap of PET and CT projected ablation volumes were compared using the Dice similarity coefficient (DSC). Interobserver differences were assessed with repeated measure analysis of variance (ANOVA). Technical success and local progression rates were noted. RESULTS: Mean tumour 2D PET/CT misregistrations were 1.02 mm (range 0.01-5.02), 1.89 (0.03-7.85), and 3.05 (0-10) in the x, y, and z planes. Mean 3D misregistration was 4.4 mm (0.36-10.74). Mean projected PET/CT ablation volume DSC was 0.72 (±0.19). No significant interobserver differences in 3D misregistration (p=0.73) or DSC (p=0.54) were observed. Technical success of ablations was 100%; one (5.3%) of 19 tumours progressed. CONCLUSION: Accurate spatial registration of tumours and substantial overlap of projected ablation volumes are achievable when comparing PET and CT acquisitions from single-breath-hold PET/CT. The results suggest that tumours visible only at PET could be accurately targeted and ablated using this technique.

Klaus H Maier-Hein, Peter F Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji, Wilburn E Reddick, John O Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, Ye Wu, Jieyan Ma, H Renjie, Qiang Li, Carl-Fredrik Westin, Samuel Deslauriers-Gauthier, Omar Ocegueda J González, Michael Paquette, Samuel St-Jean, Gabriel Girard, François Rheault, Jasmeen Sidhu, Chantal MW Tax, Fenghua Guo, Hamed Y Mesri, Szabolcs Dávid, Martijn Froeling, Anneriet M Heemskerk, Alexander Leemans, Arnaud Boré, Basile Pinsard, Christophe Bedetti, Matthieu Desrosiers, Simona Brambati, Julien Doyon, Alessia Sarica, Roberta Vasta, Antonio Cerasa, Aldo Quattrone, Jason Yeatman, Ali R Khan, Wes Hodges, Simon Alexander, David Romascano, Muhamed Barakovic, Anna Auría, Oscar Esteban, Alia Lemkaddem, Jean-Philippe Thiran, Ertan H Cetingul, Benjamin L Odry, Boris Mailhe, Mariappan S Nadar, Fabrizio Pizzagalli, Gautam Prasad, Julio E Villalon-Reina, Justin Galvis, Paul M Thompson, Francisco De Santiago Requejo, Pedro Luque Laguna, Luis Miguel Lacerda, Rachel Barrett, Flavio Dell'Acqua, Marco Catani, Laurent Petit, Emmanuel Caruyer, Alessandro Daducci, Tim B Dyrby, Tim Holland-Letz, Claus C Hilgetag, Bram Stieltjes, and Maxime Descoteaux. 2017. “The Challenge of Mapping the Human Connectome Based on Diffusion Tractography.” Nat Commun, 8, 1, Pp. 1349.Abstract
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
Michael A Silva, Alfred P See, Walid I Essayed, Alexandra J Golby, and Yanmei Tie. 2017. “Challenges and Techniques for Presurgical Brain Mapping with Functional MRI.” Neuroimage Clin, 17, Pp. 794-803.Abstract
Functional magnetic resonance imaging (fMRI) is increasingly used for preoperative counseling and planning, and intraoperative guidance for tumor resection in the eloquent cortex. Although there have been improvements in image resolution and artifact correction, there are still limitations of this modality. In this review, we discuss clinical fMRI's applications, limitations and potential solutions. These limitations depend on the following parameters: foundations of fMRI, physiologic effects of the disease, distinctions between clinical and research fMRI, and the design of the fMRI study. We also compare fMRI to other brain mapping modalities which should be considered as alternatives or adjuncts when appropriate, and discuss intraoperative use and validation of fMRI. These concepts direct the clinical application of fMRI in neurosurgical patients.
Mizuki Nishino, Adrian G Sacher, Leena Gandhi, Zhao Chen, Esra Akbay, Andriy Fedorov, Carl F Westin, Hiroto Hatabu, Bruce E Johnson, Peter Hammerman, and Kwok-Kin Wong. 2017. “Co-clinical Quantitative Tumor Volume Imaging in ALK-rearranged NSCLC Treated with Crizotinib.” Eur J Radiol, 88, Pp. 15-20.Abstract
PURPOSE: To evaluate and compare the volumetric tumor burden changes during crizotinib therapy in mice and human cohorts with ALK-rearranged non-small-cell lung cancer (NSCLC). METHODS: Volumetric tumor burden was quantified on serial imaging studies in 8 bitransgenic mice with ALK-rearranged adenocarcinoma treated with crizotinib, and in 33 human subjects with ALK-rearranged NSCLC treated with crizotinib. The volumetric tumor burden changes and the time to maximal response were compared between mice and humans. RESULTS: The median tumor volume decrease (%) at the maximal response was -40.4% (range: -79.5%-+11.7%) in mice, and -72.9% (range: -100%-+72%) in humans (Wilcoxon p=0.03). The median time from the initiation of therapy to maximal response was 6 weeks in mice, and 15.7 weeks in humans. Overall volumetric response rate was 50% in mice and 97% in humans. Spider plots of tumor volume changes during therapy demonstrated durable responses in the human cohort, with a median time on therapy of 13.1 months. CONCLUSION: The present study described an initial attempt to evaluate quantitative tumor burden changes in co-clinical imaging studies of genomically-matched mice and human cohorts with ALK-rearranged NSCLC treated with crizotinib. Differences are noted in the degree of maximal volume response between the two cohorts in this well-established paradigm of targeted therapy, indicating a need for further studies to optimize co-clinical trial design and interpretation.
Sophia C Kamran, Matthias M Manuel, Linda P Cho, Antonio L Damato, Ehud J Schmidt, Clare Tempany, Robert A Cormack, and Akila N Viswanathan. 2017. “Comparison of Outcomes for MR-guided versus CT-guided High-dose-rate Interstitial Brachytherapy in Women with Locally Advanced Carcinoma of the Cervix.” Gynecol Oncol, 145, 2, Pp. 284-90.Abstract

OBJECTIVE: The purpose was to compare local control (LC), overall survival (OS) and dose to the organs at risk (OAR) in women with locally advanced cervical cancer treated with MR-guided versus CT-guided interstitial brachytherapy (BT). METHODS: 56 patients (29 MR, 27 CT) were treated with high-dose-rate (HDR) interstitial BT between 2005-2015. The MR patients had been prospectively enrolled on a Phase II clinical trial. Data were analyzed using Kaplan-Meier (K-M) and Cox proportional hazards statistical modeling in JMP® & R®. RESULTS: Median follow-up time was 19.7months (MR group) and 18.4months (CT group). There were no statistically significant differences in patient age at diagnosis, histology, percent with tumor size >4cm, grade, FIGO stage or lymph node involvement between the groups. Patients in the MR group had more lymphovascular involvement compared to patients in the CT group (p<0.01). When evaluating plans generated, there were no statistically significant differences in median cumulative dose to the high-risk clinical target volume or the OAR. 2-year K-M LC rates for MR-based and CT-based treatments were 96% and 87%, respectively (log-rank p=0.65). At 2years, OS was significantly better in the MR-guided cohort (84% vs. 56%, p=0.036). On multivariate analysis, squamous histology was associated with longer OS (HR 0.23, 95% CI 0.07-0.72) in a model with MR BT (HR 0.35, 95% CI 0.08-1.18). There was no difference in toxicities between CT and MR BT. CONCLUSION: In this population of locally advanced cervical-cancer patients, MR-guided HDR BT resulted in estimated 96% 2-year local control and excellent survival and toxicity rates.

Ming Li, Vivek Narayan, Ritu R Gill, Jyothi P Jagannathan, Maria F Barile, Feng Gao, Raphael Bueno, and Jagadeesan Jayender. 2017. “Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity.” AJR Am J Roentgenol, 209, 6, Pp. 1216-27.Abstract
OBJECTIVE: The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type. MATERIALS AND METHODS: The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups. The heterogeneity metrics were used to train a SVM to learn and predict the lesion type. RESULTS: Fifty of 57 and 51 of 57 heterogeneity metrics showed statistically significant differences among the four GGN groups on unenhanced and contrast-enhanced CT scans, respectively. The SVM predicted lesion type with greater accuracy than did three expert radiologists. The accuracy of classifying the GGNs into the four groups on the basis of the SVM algorithm was 70.9%, whereas the accuracy of the radiologists was 39.6%. The accuracy of SVM in classifying the AIS and MIA nodules was 73.1%, and the accuracy of the radiologists was 35.7%. For indolent versus invasive lesions, the accuracy of the SVM was 88.1%, and the accuracy of the radiologists was 60.8%. We found that contrast enhancement does not significantly improve the differential diagnosis of GGNs. CONCLUSION: Compared with the GGN classification done by the three radiologists, the SVM trained regarding all the heterogeneity metrics showed significantly higher accuracy in classifying the lesions into the four groups, differentiating between AIS and MIA and between indolent and invasive lesions. Contrast enhancement did not improve the differential diagnosis of GGNs.
Shinn-Huey S Chou, Eva C Gombos, Sona A Chikarmane, Catherine S Giess, and Jagadeesan Jayender. 2017. “Computer-Aided Heterogeneity Analysis in Breast MR Imaging Assessment of Ductal Carcinoma In Situ: Correlating Histologic Grade and Receptor Status.” J Magn Reson Imaging, 46, 6, Pp. 1748-59.Abstract

PURPOSE: To identify breast MR imaging biomarkers to predict histologic grade and receptor status of ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: Informed consent was waived in this Health Insurance Portability and Accountability Act-compliant Institutional Review Board-approved study. Case inclusion was conducted from 7332 consecutive breast MR studies from January 1, 2009, to December 31, 2012. Excluding studies with benign diagnoses, studies without visible abnormal enhancement, and pathology containing invasive disease yielded 55 MR-imaged pathology-proven DCIS seen on 54 studies. Twenty-eight studies (52%) were performed at 1.5 Tesla (T); 26 (48%) at 3T. Regions-of-interest representing DCIS were segmented for precontrast, first and fourth postcontrast, and subtracted first and fourth postcontrast images on the open-source three-dimensional (3D) Slicer software. Fifty-seven metrics of each DCIS were obtained, including distribution statistics, shape, morphology, Renyi dimensions, geometrical measure, and texture, using the 3D Slicer HeterogeneityCAD module. Statistical correlation of heterogeneity metrics with DCIS grade and receptor status was performed using univariate Mann-Whitney test. RESULTS: Twenty-four of the 55 DCIS (44%) were high nuclear grade (HNG); 44 (80%) were estrogen receptor (ER) positive. Human epidermal growth factor receptor-2 (HER2) was amplified in 10/55 (18%), nonamplified in 34/55 (62%), unknown/equivocal in 8/55 (15%). Surface area-to-volume ratio showed significant difference (P < 0.05) between HNG and non-HNG DCIS. No metric differentiated ER status (0.113 < p ≤ 1.000). Seventeen metrics showed significant differences between HER2-positive and HER2-negative DCIS (0.016 < P < 0.050). CONCLUSION: Quantitative heterogeneity analysis of DCIS suggests the presence of MR imaging biomarkers in classifying DCIS grade and HER2 status. Validation with larger samples and prospective studies is needed to translate these results into clinical applications. LEVEL OF EVIDENCE: 3 J. Magn. Reson. Imaging 2017.

Sadhna Verma, Peter L Choyke, Steven C Eberhardt, Aytekin Oto, Clare M Tempany, Baris Turkbey, and Andrew B Rosenkrantz. 2017. “The Current State of MR Imaging-targeted Biopsy Techniques for Detection of Prostate Cancer.” Radiology, 285, 2, Pp. 343-56.Abstract
Systematic transrectal ultrasonography (US)-guided biopsy is the standard approach for histopathologic diagnosis of prostate cancer. However, this technique has multiple limitations because of its inability to accurately visualize and target prostate lesions. Multiparametric magnetic resonance (MR) imaging of the prostate is more reliably able to localize significant prostate cancer. Targeted prostate biopsy by using MR imaging may thus help to reduce false-negative results and improve risk assessment. Several commercial devices are now available for targeted prostate biopsy, including in-gantry MR imaging-targeted biopsy and real-time transrectal US-MR imaging fusion biopsy systems. This article reviews the current status of MR imaging-targeted biopsy platforms, including technical considerations, as well as advantages and challenges of each technique.
Daniel I Glazer, Elmira Hassanzadeh, Andriy Fedorov, Olutayo I Olubiyi, Shayna S Goldberger, Tobias Penzkofer, Trevor A Flood, Paul Masry, Robert V. Mulkern, Michelle S Hirsch, Clare M Tempany, and Fiona M Fennessy. 2017. “Diffusion-weighted Endorectal MR Imaging at 3T for Prostate Cancer: Correlation with Tumor Cell Density and Percentage Gleason Pattern on Whole Mount Pathology.” Abdom Radiol (NY), 42, 3, Pp. 918-25.Abstract

OBJECTIVE: To determine if tumor cell density and percentage of Gleason pattern within an outlined volumetric tumor region of interest (TROI) on whole-mount pathology (WMP) correlate with apparent diffusion coefficient (ADC) values on corresponding TROIs outlined on pre-operative MRI. METHODS: Men with biopsy-proven prostate adenocarcinoma undergoing multiparametric MRI (mpMRI) prior to prostatectomy were consented to this prospective study. WMP and mpMRI images were viewed using 3D Slicer and each TROI from WMP was contoured on the high b-value ADC maps (b0, 1400). For each TROI outlined on WMP, TCD (tumor cell density) and the percentage of Gleason pattern 3, 4, and 5 were recorded. The ADCmean, ADC10th percentile, ADC90th percentile, and ADCratio were also calculated in each case from the ADC maps using 3D Slicer. RESULTS: Nineteen patients with 21 tumors were included in this study. ADCmean values for TROIs were 944.8 ± 327.4 vs. 1329.9 ± 201.6 mm(2)/s for adjacent non-neoplastic prostate tissue (p < 0.001). ADCmean, ADC10th percentile, and ADCratio values for higher grade tumors were lower than those of lower grade tumors (mean 809.71 and 1176.34 mm(2)/s, p = 0.014; 10th percentile 613.83 and 1018.14 mm(2)/s, p = 0.009; ratio 0.60 and 0.94, p = 0.005). TCD and ADCmean (ρ = -0.61, p = 0.005) and TCD and ADC10th percentile (ρ = -0.56, p = 0.01) were negatively correlated. No correlation was observed between percentage of Gleason pattern and ADC values. CONCLUSION: DWI MRI can characterize focal prostate cancer using ADCratio, ADC10th percentile, and ADCmean, which correlate with pathological tumor cell density.

Melissa A Mallory, Yasuaki Sagara, Fatih Aydogan, Stephen Desantis, Jagadeesan Jayender, Diana Caragacianu, Eva Gombos, Kirby G. Vosburgh, Ferenc A Jolesz, and Mehra Golshan. 2017. “Feasibility of Intraoperative Breast MRI and the Role of Prone Versus Supine Positioning in Surgical Planning for Breast-Conserving Surgery.” Breast J, 23, 6, Pp. 713-7.Abstract
We assessed the feasibility of supine intraoperative MRI (iMRI) during breast-conserving surgery (BCS), enrolling 15 patients in our phase I trial between 2012 and 2014. Patients received diagnostic prone MRI, BCS, pre-excisional supine iMRI, and postexcisional supine iMRI. Feasibility was assessed based on safety, sterility, duration, and image-quality. Twelve patients completed the study; mean duration = 114 minutes; all images were adequate; no complications, safety, or sterility issues were encountered. Substantial tumor-associated changes occurred (mean displacement = 67.7 mm, prone-supine metric, n = 7). We have demonstrated iMRI feasibility for BCS and have identified potential limitations of prone breast MRI that may impact surgical planning.
Nick Todd, Oliver Josephs, Peter Zeidman, Guillaume Flandin, Steen Moeller, and Nikolaus Weiskopf. 2017. “Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise.” Front Neurosci, 11, Pp. 158.Abstract
Accelerated data acquisition with simultaneous multi-slice (SMS) imaging for functional MRI studies leads to interacting and opposing effects that influence the sensitivity to blood oxygen level-dependent (BOLD) signal changes. Image signal to noise ratio (SNR) is decreased with higher SMS acceleration factors and shorter repetition times (TR) due to g-factor noise penalties and saturation of longitudinal magnetization. However, the lower image SNR is counteracted by greater statistical power from more samples per unit time and a higher temporal Nyquist frequency that allows for better removal of spurious non-BOLD high frequency signal content. This study investigated the dependence of the BOLD sensitivity on these main driving factors and their interaction, and provides a framework for evaluating optimal acceleration of SMS-EPI sequences. functional magnetic resonance imaging (fMRI) data from a scenes/objects visualization task was acquired in 10 healthy volunteers at a standard neuroscience resolution of 3 mm on a 3T MRI scanner. SMS factors 1, 2, 4, and 8 were used, spanning TRs of 2800 ms to 350 ms. Two data processing methods were used to equalize the number of samples over the SMS factors. BOLD sensitivity was assessed using g-factors maps, temporal SNR (tSNR), and t-score metrics. tSNR results show a dependence on SMS factor that is highly non-uniform over the brain, with outcomes driven by g-factor noise amplification and the presence of high frequency noise. The t-score metrics also show a high degree of spatial dependence: the lower g-factor noise area of V1 shows significant improvements at higher SMS factors; the moderate-level g-factor noise area of the parahippocampal place area shows only a trend of improvement; and the high g-factor noise area of the ventral-medial pre-frontal cortex shows a trend of declining t-scores at higher SMS factors. This spatial variability suggests that the optimal SMS factor for fMRI studies is region dependent. For task fMRI studies done with similar parameters as were used here (3T scanner, 32-channel RF head coil, whole brain coverage at 3 mm isotropic resolution), we recommend SMS accelerations of 4x (conservative) to 8x (aggressive) for most studies and a more conservative acceleration of 2x for studies interested in anterior midline regions.
Stephanie M Wong, Rachel A Freedman, Yasuaki Sagara, Fatih Aydogan, William T Barry, and Mehra Golshan. 2017. “Growing Use of Contralateral Prophylactic Mastectomy Despite no Improvement in Long-term Survival for Invasive Breast Cancer.” Ann Surg, 265, 3, Pp. 581-9.Abstract
OBJECTIVE: To update and examine national temporal trends in contralateral prophylactic mastectomy (CPM) and determine whether survival differed for invasive breast cancer patients based on hormone receptor (HR) status and age. METHODS: We identified women diagnosed with unilateral stage I to III breast cancer between 1998 and 2012 within the Surveillance, Epidemiology, and End Results registry. We compared characteristics and temporal trends between patients undergoing breast-conserving surgery, unilateral mastectomy, and CPM. We then performed Cox proportional-hazards regression to examine breast cancer-specific survival (BCSS) and overall survival (OS) in women diagnosed between 1998 and 2007, who underwent breast-conserving surgery with radiation (breast-conserving therapy), unilateral mastectomy, or CPM, with subsequent subgroup analysis stratifying by age and HR status. RESULTS: Of 496,488 women diagnosed with unilateral invasive breast cancer, 59.6% underwent breast-conserving surgery, 33.4% underwent unilateral mastectomy, and 7.0% underwent CPM. Overall, the proportion of women undergoing CPM increased from 3.9% in 2002 to 12.7% in 2012 (P < 0.001). Reconstructive surgery was performed in 48.3% of CPM patients compared with only 16.0% of unilateral mastectomy patients, with rates of reconstruction with CPM rising from 35.3% in 2002 to 55.4% in 2012 (P < 0.001). When compared with breast-conserving therapy, we found no significant improvement in BCSS or OS for women undergoing CPM (BCSS: HR 1.08, 95% confidence interval 1.01-1.16; OS: HR 1.08, 95% confidence interval 1.03-1.14), regardless of HR status or age. CONCLUSIONS: The use of CPM more than tripled during the study period despite evidence suggesting no survival benefit over breast conservation. Further examination on how to optimally counsel women about surgical options is warranted.
Alison M Pouch, Ahmed H Aly, Andras Lasso, Alexander V Nguyen, Adam B Scanlan, Francis X McGowan, Gabor Fichtinger, Robert C Gorman, Joseph H Gorman, Paul A Yushkevich, and Matthew A Jolley. 2017. “Image Segmentation and Modeling of the Pediatric Tricuspid Valve in Hypoplastic Left Heart Syndrome.” Funct Imaging Model Heart, 10263, Pp. 95-105.Abstract
Hypoplastic left heart syndrome (HLHS) is a single-ventricle congenital heart disease that is fatal if left unpalliated. In HLHS patients, the tricuspid valve is the only functioning atrioventricular valve, and its competence is therefore critical. This work demonstrates the first automated strategy for segmentation, modeling, and morphometry of the tricuspid valve in transthoracic 3D echocardiographic (3DE) images of pediatric patients with HLHS. After initial landmark placement, the automated segmentation step uses multi-atlas label fusion and the modeling approach uses deformable modeling with medial axis representation to produce patient-specific models of the tricuspid valve that can be comprehensively and quantitatively assessed. In a group of 16 pediatric patients, valve segmentation and modeling attains an accuracy (mean boundary displacement) of 0.8 ± 0.2 mm relative to manual tracing and shows consistency in annular and leaflet measurements. In the future, such image-based tools have the potential to improve understanding and evaluation of tricuspid valve morphology in HLHS and guide strategies for patient care.