Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas.

The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer's disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).

Phase 0 Study of Vandetanib-Eluting Radiopaque Embolics as a Preoperative Embolization Treatment in Patients with Resectable Liver Malignancies.

PURPOSE: To assess the safety and tolerability of a vandetanib-eluting radiopaque embolic (BTG-002814) for transarterial chemoembolization (TACE) in patients with resectable liver malignancies. MATERIALS AND METHODS: The VEROnA clinical trial was a first-in-human, phase 0, single-arm, window-of-opportunity study. Eligible patients were aged ≥18 years and had resectable hepatocellular carcinoma (HCC) (Child-Pugh A) or metastatic colorectal cancer (mCRC). Patients received 1 mL of BTG-002814 transarterially (containing 100 mg of vandetanib) 7-21 days prior to surgery. The primary objectives were to establish the safety and tolerability of BTG-002814 and determine the concentrations of vandetanib and the N-desmethyl vandetanib metabolite in the plasma and resected liver after treatment. Biomarker studies included circulating proangiogenic factors, perfusion computed tomography, and dynamic contrast-enhanced magnetic resonance imaging. RESULTS: Eight patients were enrolled: 2 with HCC and 6 with mCRC. There was 1 grade 3 adverse event (AE) before surgery and 18 after surgery; 6 AEs were deemed to be related to BTG-002814. Surgical resection was not delayed. Vandetanib was present in the plasma of all patients 12 days after treatment, with a mean maximum concentration of 24.3 ng/mL (standard deviation ± 13.94 ng/mL), and in resected liver tissue up to 32 days after treatment (441-404,000 ng/g). The median percentage of tumor necrosis was 92.5% (range, 5%-100%). There were no significant changes in perfusion imaging parameters after TACE. CONCLUSIONS: BTG-002814 has an acceptable safety profile in patients before surgery. The presence of vandetanib in the tumor specimens up to 32 days after treatment suggests sustained anticancer activity, while the low vandetanib levels in the plasma suggest minimal release into the systemic circulation. Further evaluation of this TACE combination is warranted in dose-finding and efficacy studies.

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

We present open-source tools for 3D analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (i) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (ii) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite "FreeSurfer" ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ).

Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology.

We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite 'FreeSurfer' (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).

Every year, thousands of human brains are donated to science. These brains are used to study normal aging, as well as neurological diseases like Alzheimer's or Parkinson's. Donated brains usually go to 'brain banks', institutions where the brains are dissected to extract tissues relevant to different diseases. During this process, it is routine to take photographs of brain slices for archiving purposes. Often, studies of dead brains rely on qualitative observations, such as 'the hippocampus displays some atrophy', rather than concrete 'numerical' measurements. This is because the gold standard to take three-dimensional measurements of the brain is magnetic resonance imaging (MRI), which is an expensive technique that requires high expertise ­ especially with dead brains. The lack of quantitative data means it is not always straightforward to study certain conditions. To bridge this gap, Gazula et al. have developed an openly available software that can build three-dimensional reconstructions of dead brains based on photographs of brain slices. The software can also use machine learning methods to automatically extract different brain regions from the three-dimensional reconstructions and measure their size. These data can be used to take precise quantitative measurements that can be used to better describe how different conditions lead to changes in the brain, such as atrophy (reduced volume of one or more brain regions). The researchers assessed the accuracy of the method in two ways. First, they digitally sliced MRI-scanned brains and used the software to compute the sizes of different structures based on these synthetic data, comparing the results to the known sizes. Second, they used brains for which both MRI data and dissection photographs existed and compared the measurements taken by the software to the measurements obtained with MRI images. Gazula et al. show that, as long as the photographs satisfy some basic conditions, they can provide good estimates of the sizes of many brain structures. The tools developed by Gazula et al. are publicly available as part of FreeSurfer, a widespread neuroimaging software that can be used by any researcher working at a brain bank. This will allow brain banks to obtain accurate measurements of dead brains, allowing them to cheaply perform quantitative studies of brain structures, which could lead to new findings relating to neurodegenerative diseases.

Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI.

The human thalamus is a highly connected subcortical grey-matter structure within the brain. It comprises dozens of nuclei with different function and connectivity, which are affected differently by disease. For this reason, there is growing interest in studying the thalamic nuclei in vivo with MRI. Tools are available to segment the thalamus from 1 mm T1 scans, but the contrast of the lateral and internal boundaries is too faint to produce reliable segmentations. Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions. Here we present the first CNN that can segment thalamic nuclei from T1 and diffusion data of any resolution without retraining or fine tuning. Our method builds on a public histological atlas of the thalamic nuclei and silver standard segmentations on high-quality diffusion data obtained with a recent Bayesian adaptive segmentation tool. We combine these with an approximate degradation model for fast domain randomisation during training. Our CNN produces a segmentation at 0.7 mm isotropic resolution, irrespective of the resolution of the input. Moreover, it uses a parsimonious model of the diffusion signal at each voxel (fractional anisotropy and principal eigenvector) that is compatible with virtually any set of directions and b-values, including huge amounts of legacy data. We show results of our proposed method on three heterogeneous datasets acquired on dozens of different scanners. An implementation of the method is publicly available at https://freesurfer.net/fswiki/ThalamicNucleiDTI.

Prostatic calcifications: Quantifying occurrence, radiodensity, and spatial distribution in prostate cancer patients.

BACKGROUND: To evaluate the prevalence, density, and distribution of prostate calcification in patients with prostate cancer. METHODS: Patients who underwent both Gallium-68 PSMA PET/CT and MRI of the prostate over the course of a year were selected for analysis. The CT images with visible calcifications within the prostate were included and calcifications automatically isolated using a threshold of 130 HU. The corresponding multiparametric MRI was assessed and the peripheral zone, transition zone, MRI-visible tumor, and urethra manually contoured. The contoured MRI and CT images were registered using rigid registration, and calcifications mapped automatically to the MRI contours. RESULTS: A total of 85 men (age range 50-88, mean 69 years, standard deviation 7.2 years) were assessed. The mean serum Prostate Specific Antigen PSA was 16.7, range 0.12 to 94.4. Most patients had intermediate-risk disease (68%; Gleason grade group 2 and 3), 26% had high-risk disease (Gleason grade group 4 and 5), and 6% had low-risk disease (Gleason grade group 1). Forty-six patients out of 85 (54%) had intraprostatic calcification. Calcification occurred more in transition zone than the peripheral zone (65% vs. 35%). The mean density of the calcification was 227 HU (min 133, max 1,966 HU). In 12 patients, the calcification was within an MRI-visible tumor, in 24 patients, there were calcifications within a 9 mm distance of the tumor border, and in 9 patients, there were calcifications located between the urethra and tumor. CONCLUSIONS: Calcifications are common in patients with prostate cancer. Their density and location may make them a significant consideration when planning treatment or retreatment with some types of minimally invasive therapy.

Registration of Untracked 2D Laparoscopic Ultrasound to CT Images of the Liver Using Multi-Labelled Content-Based Image Retrieval.

Laparoscopic Ultrasound (LUS) is recommended as a standard-of-care when performing laparoscopic liver resections as it images sub-surface structures such as tumours and major vessels. Given that LUS probes are difficult to handle and some tumours are iso-echoic, registration of LUS images to a pre-operative CT has been proposed as an image-guidance method. This registration problem is particularly challenging due to the small field of view of LUS, and usually depends on both a manual initialisation and tracking to compose a volume, hindering clinical translation. In this paper, we extend a proposed registration approach using Content-Based Image Retrieval (CBIR), removing the requirement for tracking or manual initialisation. Pre-operatively, a set of possible LUS planes is simulated from CT and a descriptor generated for each image. Then, a Bayesian framework is employed to estimate the most likely sequence of CT simulations that matches a series of LUS images. We extend our CBIR formulation to use multiple labelled objects and constrain the registration by separating liver vessels into portal vein and hepatic vein branches. The value of this new labeled approach is demonstrated in retrospective data from 5 patients. Results show that, by including a series of 5 untracked images in time, a single LUS image can be registered with accuracies ranging from 5.7 to 16.4 mm with a success rate of 78%. Initialisation of the LUS to CT registration with the proposed framework could potentially enable the clinical translation of these image fusion techniques.

VEROnA Protocol: A Pilot, Open-Label, Single-Arm, Phase 0, Window-of-Opportunity Study of Vandetanib-Eluting Radiopaque Embolic Beads (BTG-002814) in Patients With Resectable Liver Malignancies.

BACKGROUND: Transarterial chemoembolization (TACE) is the current standard of care for patients with intermediate-stage hepatocellular carcinoma (HCC) and is also a treatment option for patients with liver metastases from colorectal cancer. However, TACE is not a curative treatment, and tumor progression occurs in more than half of the patients treated. Despite advances and technical refinements of TACE, including the introduction of drug-eluting beads-TACE, the clinical efficacy of TACE has not been optimized, and improved arterial therapies are required. OBJECTIVE: The primary objectives of the VEROnA study are to evaluate the safety and tolerability of vandetanib-eluting radiopaque embolic beads (BTG-002814) in patients with resectable liver malignancies and to determine concentrations of vandetanib and the N-desmethyl metabolite in plasma and resected liver following treatment with BTG-002814. METHODS: The VEROnA study is a first-in-human, open-label, single-arm, phase 0, window-of-opportunity study of BTG-002814 (containing 100 mg vandetanib) delivered transarterially, 7 to 21 days before surgery in patients with resectable liver malignancies. Eligible patients have a diagnosis of colorectal liver metastases, or HCC (Childs Pugh A), diagnosed histologically or radiologically, and are candidates for liver surgery. All patients are followed up for 28 days following surgery. Secondary objectives of this study are to evaluate the anatomical distribution of BTG-002814 on noncontrast-enhanced imaging, to evaluate histopathological features in the surgical specimen, and to assess changes in blood flow on dynamic contrast-enhanced magnetic resonance imaging following treatment with BTG-002814. Exploratory objectives of this study are to study blood biomarkers with the potential to identify patients likely to respond to treatment and to correlate the distribution of BTG-002814 on imaging with pathology by 3-dimensional modeling. RESULTS: Enrollment for the study was completed in February 2019. Results of a planned interim analysis were reviewed by a safety committee after the first 3 patients completed follow-up. The recommendation of the committee was to continue the study without any changes to the dose or trial design, as there were no significant unexpected toxicities related to BTG-002814. CONCLUSIONS: The VEROnA study is studying the feasibility of administering BTG-002814 to optimize the use of this novel technology as liver-directed therapy for patients with primary and secondary liver cancer. TRIAL REGISTRATION: ClinicalTrial.gov NCT03291379; https://clinicaltrials.gov/ct2/show/NCT03291379. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/13696.

On the Feasibility of Automated Mechanical Ventilation Control Through EIT.

OBJECTIVE: This paper aims to demonstrate the feasibility of coupling electrical impedance tomography (EIT) with models of lung function in order to recover parameters and inform mechanical ventilation control. METHODS: A compartmental ordinary differential equation model of lung function is coupled to simulations of EIT, assuming accurate modeling and movement tracking, to generate time series values of bulk conductivity. These values are differentiated and normalized against the total air volume flux to recover regional volumes and flows. These ventilation distributions are used to recover regional resistance and elastance properties of the lung. Linear control theory is used to demonstrate how these parameters may be used to generate a patient-specific pressure mode control. RESULTS: Ventilation distributions are shown to be recoverable, with Euclidean norm errors in air flow below 9% and volume below 3%. The parameters are also shown to be recoverable, although errors are higher for resistance values than elastance. The control constructed is shown to have minimal seminorm resulting in bounded magnitudes and minimal gradients. CONCLUSION: The recovery of regional ventilation distributions and lung parameters is feasible with the use of EIT. These parameters may then be used in model based control schemes to provide patient-specific care. SIGNIFICANCE: For pulmonary-intensive-care patients mechanical ventilation is a life saving intervention, requiring careful calibration of pressure settings. Both magnitudes and gradients of pressure can contribute to ventilator induced lung injury. Retrieving regional lung parameters allows the design of patient-specific ventilator controls to reduce injury.