Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.

BACKGROUND: The segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively. RESULTS: The dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches. CONCLUSIONS: The use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.

Brain Volumes and Abnormalities in Adults Born Preterm at Very Low Birth Weight.

OBJECTIVES: To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls. STUDY DESIGN: We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings. A neuroradiologist, masked to participants' prematurity status, reviewed the images for parenchymal and structural abnormalities, and FreeSurfer software 6.0 was used to conduct volumetric analyses. Data were analyzed by linear mixed models. RESULTS: We found more structural abnormalities in very low birth weight participants than in siblings (37% vs 13%). The most common finding was periventricular leukomalacia, present in 15% of very low birth weight participants and in 3% of siblings. The very low birth weight group had smaller absolute brain volumes (-0.4 SD) and, after adjusting for estimated intracranial volume, less gray matter (-0.2 SD), larger ventricles (1.5 SD), smaller thalami (-0.6 SD), caudate nuclei (-0.4 SD), right hippocampus (-0.4 SD), and left pallidum (-0.3 SD). We saw no volume differences in total white matter (-0.04 SD; 95% CI, -0.13 to 0.09). CONCLUSIONS: Preterm very low birth weight adults had a higher prevalence of brain abnormalities than their term-born siblings. They also had smaller absolute brain volumes, less gray but not white matter, and smaller volumes in several gray matter structures.

Motion modeling from 4D MR images of liver simulating phantom.

BACKGROUND AND PURPOSE: A novel method of retrospective liver modeling was developed based on four-dimensional magnetic resonance (4D-MR) images. The 4D-MR images will be utilized in generation of the subject-specific deformable liver model to be used in radiotherapy planning (RTP). The purpose of this study was to test and validate the developed 4D-magnetic resonance imaging (MRI) method with extensive phantom tests. We also aimed to build a motion model with image registration methods from liver simulating phantom images. MATERIALS AND METHODS: A deformable phantom was constructed by combining deformable tissue-equivalent material and a programmable 4D CIRS-platform. The phantom was imaged in 1.5 T MRI scanner with T2-weighted 4D SSFSE and T1-weighted Ax dual-echo Dixon SPGR sequences, and in computed tomography (CT). In addition, geometric distortion of the 4D sequence was measured with a GRADE phantom. The motion model was developed; the phases of the 4D-MRI were used as surrogate data, and displacement vector fields (DVF's) were used as a motion measurement. The motion model and the developed 4D-MRI method were evaluated and validated with extensive tests. RESULT: The 4D-MRI method enabled an accuracy of 2 mm using our deformable phantom compared to the 4D-CT. Results showed a mean accuracy of <2 mm between coordinates and DVF's measured from the 4D images. Three-dimensional geometric accuracy results with the GRADE phantom were: 0.9-mm mean and 2.5 mm maximum distortion within a 100 mm distance, and 2.2 mm mean, 5.2 mm maximum distortion within a 150 mm distance from the isocenter. CONCLUSIONS: The 4D-MRI method was validated with phantom tests as a necessary step before patient studies. The subject-specific motion model was generated and will be utilized in the generation of the deformable liver model of patients to be used in RTP.

Automatic CT Angiography Lesion Segmentation Compared to CT Perfusion in Ischemic Stroke Detection: a Feasibility Study.

In stroke imaging, CT angiography (CTA) is used for detecting arterial occlusions. These images could also provide information on the extent of ischemia. The study aim was to develop and evaluate a convolutional neural network (CNN)-based algorithm for detecting and segmenting acute ischemic lesions from CTA images of patients with suspected middle cerebral artery stroke. These results were compared to volumes reported by widely used CT perfusion-based RAPID software (IschemaView). A 42-layer-deep CNN was trained on 50 CTA volumes with manually delineated targets. The lower bound for predicted lesion size to reliably discern stroke from false positives was estimated. The severity of false positives and false negatives was reviewed visually to assess the clinical applicability and to further guide the method development. The CNN model corresponded to the manual segmentations with voxel-wise sensitivity 0.54 (95% confidence interval: 0.44-0.63), precision 0.69 (0.60-0.76), and Sørensen-Dice coefficient 0.61 (0.52-0.67). Stroke/nonstroke differentiation accuracy 0.88 (0.81-0.94) was achieved when only considering the predicted lesion size (i.e., regardless of location). By visual estimation, 46% of cases showed some false findings, such as CNN highlighting chronic periventricular white matter changes or beam hardening artifacts, but only in 9% the errors were severe, translating to 0.91 accuracy. The CNN model had a moderately strong correlation to RAPID-reported Tmax > 10 s volumes (Pearson's r = 0.76 (0.58-0.86)). The results suggest that detecting anterior circulation ischemic strokes from CTA using a CNN-based algorithm can be feasible when accompanied with physiological knowledge to rule out false positives.

Retrospective four-dimensional magnetic resonance imaging of liver: Method development.

Purpose of our research was to develop a four-dimensional (4D) magnetic resonance imaging (MRI) method of liver. Requirements of the method were to create a clinical procedure with acceptable imaging time and sufficient temporal and spatial accuracy. The method should produce useful planning image sets for stereotactic body radiation therapy delivery both during breath-hold and in free breathing. The purpose of the method was to improve the localization of liver metastasis. The method was validated with phantom tests. Imaging parameters were optimized to create a 4D dataset compressed to one respiratory cycle of the whole liver with clinically reasonable level of image contrast and artifacts. Five healthy volunteers were imaged with T2-weighted SSFSE research sequence. The respiratory surrogate signal was observed by the linear navigator interleaved with the anatomical liver images. The navigator was set on head-feet - direction on the superior surface of the liver to detect the edge of diaphragm. The navigator signal and 2D liver image data were retrospectively processed with a self-developed MATLAB algorithm. A deformable phantom for 4D imaging tests was constructed by combining deformable tissue-equivalent material and a commercial programmable motor unit of the 4D phantom with a clinically relevant range of deformation patterns. 4D Computed Tomography images were used as reference to validate the MRI protocol. The best compromise of reasonable accuracy and imaging time was found with 2D T2-weighted SSFSE imaging sequence using parameters: TR = 500-550 ms, images/slices = 20, slice thickness = 3 mm. Then, image processing with number of respiratory phases = 8 constructed accurate 4D images of liver. We have developed the 4D-MRI method visualizing liver motions three-dimensionally in one representative respiratory cycle. From phantom tests it was found that the spatial agreement to 4D-CT is within 2 mm that is considered sufficient for clinical applications.

MRI quality assurance based on 3D FLAIR brain images.

OBJECTIVE: Quality assurance (QA) of magnetic resonance imaging (MRI) often relies on imaging phantoms with suitable structures and uniform regions. However, the connection between phantom measurements and actual clinical image quality is ambiguous. Thus, it is desirable to measure objective image quality directly from clinical images. MATERIALS AND METHODS: In this work, four measurements suitable for clinical image QA were presented: image resolution, contrast-to-noise ratio, quality index and bias index. The methods were applied to a large cohort of clinical 3D FLAIR volumes over a test period of 9.5 months. The results were compared with phantom QA. Additionally, the effect of patient movement on the presented measures was studied. RESULTS: A connection between the presented clinical QA methods and scanner performance was observed: the values reacted to MRI equipment breakdowns that occurred during the study period. No apparent correlation with phantom QA results was found. The patient movement was found to have a significant effect on the resolution and contrast-to-noise ratio values. DISCUSSION: QA based on clinical images provides a direct method for following MRI scanner performance. The methods could be used to detect problems, and potentially reduce scanner downtime. Furthermore, with the presented methodologies comparisons could be made between different sequences and imaging settings. In the future, an online QA system could recognize insufficient image quality and suggest an immediate re-scan.

An Automatic Image Processing Workflow for Daily Magnetic Resonance Imaging Quality Assurance.

The performance of magnetic resonance imaging (MRI) equipment is typically monitored with a quality assurance (QA) program. The QA program includes various tests performed at regular intervals. Users may execute specific tests, e.g., daily, weekly, or monthly. The exact interval of these measurements varies according to the department policies, machine setup and usage, manufacturer's recommendations, and available resources. In our experience, a single image acquired before the first patient of the day offers a low effort and effective system check. When this daily QA check is repeated with identical imaging parameters and phantom setup, the data can be used to derive various time series of the scanner performance. However, daily QA with manual processing can quickly become laborious in a multi-scanner environment. Fully automated image analysis and results output can positively impact the QA process by decreasing reaction time, improving repeatability, and by offering novel performance evaluation methods. In this study, we have developed a daily MRI QA workflow that can measure multiple scanner performance parameters with minimal manual labor required. The daily QA system is built around a phantom image taken by the radiographers at the beginning of day. The image is acquired with a consistent phantom setup and standardized imaging parameters. Recorded parameters are processed into graphs available to everyone involved in the MRI QA process via a web-based interface. The presented automatic MRI QA system provides an efficient tool for following the short- and long-term stability of MRI scanners.

Feasibility of MRI-based reference images for image-guided radiotherapy of the pelvis with either cone-beam computed tomography or planar localization images.

PURPOSE: This study introduces methods to conduct image-guided radiotherapy (IGRT) of the pelvis with either cone-beam computed tomography (CBCT) or planar localization images by relying solely on magnetic resonance imaging (MRI)-based reference images. MATERIAL AND METHODS: Feasibility of MRI-based reference images for IGRT was evaluated against kV CBCT (50 scans, 5 prostate cancer patients) and kV & MV planar (5 & 5 image pairs and patients) localization images by comparing the achieved patient position corrections to those obtained by standard CT-based reference images. T1/T2*-weighted in-phase MRI, Hounsfield unit conversion-based heterogeneous pseudo-CT, and bulk pseudo-CT images were applied for reference against localization CBCTs, and patient position corrections were obtained by automatic image registration. IGRT with planar localization images was performed manually by 10 observers using reference digitally reconstructed radiographs (DRRs) reconstructed from the pseudo-CTs and standard CTs. Quality of pseudo-DRRs against CT-DRRs was evaluated with image similarity metrics. RESULTS: The SDs of differences between CBCT-to-MRI and CBCT-to-CT automatic gray-value registrations were ≤1.0 mm & ≤0.8° and ≤2.5 mm & ≤3.6° with 10 cm diameter cubic VOI and prostate-shaped VOI, respectively. The corresponding values for reference heterogeneous pseudo-CT were ≤1.0 mm & ≤0.7° and ≤2.2 mm & ≤3.3°, respectively. Heterogeneous pseudo-CT was the only type of MRI-based reference image working reliably with automatic bone registration (SDs were ≤0.9 mm & ≤0.7°). The differences include possible residual errors from planning CT to MRI registration. The image similarity metrics were significantly (p≤0.01) better in agreement between heterogeneous pseudo-DRRs and CT-DRRs than between bulk pseudo-DRRs and CT-DRRs. The SDs of differences in manual registrations (3D) with planar kV and MV localization images were ≤1.0 mm and ≤1.7 mm, respectively, between heterogeneous pseudo-DRRs and CT-DRRs, and ≤1.4 mm and ≤2.1 mm between bulk pseudo-DRRs and CT-DRRs. CONCLUSION: This study demonstrated that it is feasible to conduct IGRT of the pelvis with MRI-based reference images.

Applicability of nTMS in locating the motor cortical representation areas in patients with epilepsy.

BACKGROUND: Transcranial magnetic stimulation (TMS) is increasingly used for non-invasive functional mapping in preoperative evaluation for brain surgery, and the reliability of navigated TMS (nTMS) motor representation maps has been studied in the healthy population and in brain tumor patients. The lesions behind intractable epilepsy differ from typical brain tumors, ranging from developmental cortical malformations to injuries early in development, and may influence the functional organization of the cortical areas. Moreover, the interictal cortical epileptic activity and antiepileptic medication may affect the nTMS motor threshold. The reliability of the nTMS motor representation localization in epilepsy patients has not been addressed. METHODS: We compared the nTMS motor cortical representation maps of hand and arm muscles with the results of invasive electrical cortical stimulation (ECS) in 13 patients with focal epilepsy. The nTMS maps were projected to the cortical surface segmented from preoperative magnetic resonance images (MRI), and the positions of the subdural electrodes were extracted from the postoperative low-dose computed tomography (CT) images registered with preoperative MRI. RESULTS: The 3D distance between the average nTMS site and average ECS electrode location was 11 ± 4 mm for the hand and 16 ± 7 mm for arm muscle representation areas. In all patients the representation areas defined with nTMS and ECS were located on the same gyrus, also in patients with abundant interictal epileptic activity on the motor gyrus. CONCLUSIONS: nTMS can reliably locate the hand motor cortical representation area with the accuracy needed for pre-surgical evaluation in patients with epilepsy.

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.

BACKGROUND: Guidelines recommend that aortic dimension measurements in aortic dissection should include the aortic wall. This study aimed to evaluate two-dimensional (2D)- and three-dimensional (3D)-based deep learning approaches for extraction of outer aortic surface in computed tomography angiography (CTA) scans of Stanford type B aortic dissection (TBAD) patients and assess the speed of different whole aorta (WA) segmentation approaches. METHODS: A total of 240 patients diagnosed with TBAD between January 2007 and December 2019 were retrospectively reviewed for this study; 206 CTA scans from 206 patients with acute, subacute, or chronic TBAD acquired with various scanners in multiple different hospital units were included. Ground truth (GT) WAs for 80 scans were segmented by a radiologist using an open-source software. The remaining 126 GT WAs were generated via semi-automatic segmentation process in which an ensemble of 3D convolutional neural networks (CNNs) aided the radiologist. Using 136 scans for training, 30 for validation, and 40 for testing, 2D and 3D CNNs were trained to automatically segment WA. Main evaluation metrics for outer surface extraction and segmentation accuracy were normalized surface Dice (NSD) and Dice coefficient score (DCS), respectively. RESULTS: 2D CNN outperformed 3D CNN in NSD score (0.92 versus 0.90, p = 0.009), and both CNNs had equal DCS (0.96 versus 0.96, p = 0.110). Manual and semi-automatic segmentation times of one CTA scan were approximately 1 and 0.5 h, respectively. CONCLUSIONS: Both CNNs segmented WA with high DCS, but based on NSD, better accuracy may be required before clinical application. CNN-based semi-automatic segmentation methods can expedite the generation of GTs. RELEVANCE STATEMENT: Deep learning can speeds up the creation of ground truth segmentations. CNNs can extract the outer aortic surface in patients with type B aortic dissection. KEY POINTS: • 2D and 3D convolutional neural networks (CNNs) can extract the outer aortic surface accurately. • Equal Dice coefficient score (0.96) was reached with 2D and 3D CNNs. • Deep learning can expedite the creation of ground truth segmentations.

Organ dose calculation in CT based on scout image data and automatic image registration.

BACKGROUND: Computed tomography (CT) has become the main contributor of the cumulative radiation exposure in radiology. Information on cumulative exposure history of the patient should be available for efficient management of radiation exposures and for radiological justification. PURPOSE: To develop and evaluate automatic image registration for organ dose calculation in CT. MATERIAL AND METHODS: Planning radiograph (scout) image data describing CT scan ranges from 15 thoracic CT examinations (9 men and 6 women) and 10 abdominal CT examinations (6 men and 4 women) were co-registered with the reference trunk CT scout image. 2-D affine transformation and normalized correlation metric was used for image registration. Longitudinal (z-axis) scan range coordinates on the reference scout image were converted into slice locations on the CT-Expo anthropomorphic male and female models, following organ and effective dose calculations. RESULTS: The average deviation of z-location of studied patient images from the corresponding location in the reference scout image was 6.2 mm. The ranges of organ and effective doses with constant exposure parameters were from 0 to 28.0 mGy and from 7.3 to 14.5 mSv, respectively. The mean deviation of the doses for fully irradiated organs (inside the scan range), partially irradiated organs and non-irradiated organs (outside the scan range) was 1%, 5%, and 22%, respectively, due to image registration. CONCLUSION: The automated image processing method to registrate individual chest and abdominal CT scout radiograph with the reference scout radiograph is feasible. It can be used to determine the individual scan range coordinates in z-direction to calculate the organ dose values. The presented method could be utilized in automatic organ dose calculation in CT for radiation exposure tracking of the patients.

Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach.

Magnetic resonance (MR) imaging data can be used to develop computer-assisted diagnostic tools for neurodegenerative diseases such as aspartylglucosaminuria (AGU) and other lysosomal storage disorders. MR images contain features that are suitable for the classification and differentiation of affected individuals from healthy persons. Here, comparisons were made between MRI features extracted from different types of magnetic resonance images. Random forest classifiers were trained to classify AGU patients (n = 22) and healthy controls (n = 24) using volumetric features extracted from T1-weighted MR images, the zone variance of gray level size zone matrix (GLSZM) calculated from magnitude susceptibility-weighted MR images, and the caudate-thalamus intensity ratio computed from T2-weighted MR images. The leave-one-out cross-validation and area under the receiver operating characteristic curve were used to compare different models. The left-right-averaged, normalized volumes of the 25 nuclei of the thalamus and the zone variance of the thalamus demonstrated equal and excellent performance as classifier features for binary organization between AGU patients and healthy controls. Our findings show that texture-based features of susceptibility-weighted images and thalamic volumes can differentiate AGU patients from healthy controls with a very low error rate.

Stiffness-Controlled Hydrogels for 3D Cell Culture Models.

Nanofibrillated cellulose (NFC) hydrogel is a versatile biomaterial suitable, for example, for three-dimensional (3D) cell spheroid culturing, drug delivery, and wound treatment. By freeze-drying NFC hydrogel, highly porous NFC structures can be manufactured. We freeze-dried NFC hydrogel and subsequently reconstituted the samples into a variety of concentrations of NFC fibers, which resulted in different stiffness of the material, i.e., different mechanical cues. After the successful freeze-drying and reconstitution, we showed that freeze-dried NFC hydrogel can be used for one-step 3D cell spheroid culturing of primary mesenchymal stem/stromal cells, prostate cancer cells (PC3), and hepatocellular carcinoma cells (HepG2). No difference was observed in the viability or morphology between the 3D cell spheroids cultured in the freeze-dried and reconstituted NFC hydrogel and fresh NFC hydrogel. Furthermore, the 3D cultured spheroids showed stable metabolic activity and nearly 100% viability. Finally, we applied a convolutional neural network (CNN)-based automatic nuclei segmentation approach to automatically segment individual cells of 3D cultured PC3 and HepG2 spheroids. These results provide an application to culture 3D cell spheroids more readily with the NFC hydrogel and a step towards automatization of 3D cell culturing and analysis.

Inter- and intra-scanner variations in four magnetic resonance imaging image quality parameters.

Purpose: In addition to less frequent and more comprehensive tests, quality assurance (QA) protocol for a magnetic resonance imaging (MRI) scanner may include cursory daily or weekly phantom checks to verify equipment constancy. With an automatic image analysis workflow, the daily QA images can be further used to study scanner baseline performance and both long- and short-term variations in image quality. With known baselines and variation profiles, automatic error detection can be employed. Approach: Four image quality parameters were followed for 17 MRI scanners over six months: signal-to-noise ratio (SNR), image intensity uniformity, ghosting artifact, and geometrical distortions. Baselines and normal variations were determined. An automatic detection of abnormal QA images was compared with image deviations visually detected by human observers. Results: There were significant inter-scanner differences in the QA parameters. In some cases, the results exceeded commonly accepted tolerances. Scanner field strengths, or a unit being stationary versus mobile, did not have a clear relationship with the QA results. Conclusions: The variations and baseline levels of image QA parameters can differ significantly between MRI scanners. Scanner specific error thresholds based on parameter means and standard deviations are a viable option for detecting abnormal QA images.

Comparison of Manual Cross-Sectional Measurements and Automatic Volumetry of the Corpus Callosum, and Their Clinical Impact: A Study on Type 1 Diabetes and Healthy Controls.

Background and purpose: Degenerative change of the corpus callosum might serve as a clinically useful surrogate marker for net pathological cerebral impact of diabetes type 1. We compared manual and automatic measurements of the corpus callosum, as well as differences in callosal cross-sectional area between subjects with type 1 diabetes and healthy controls. Materials and methods: This is a cross-sectional study on 188 neurologically asymptomatic participants with type 1 diabetes and 30 healthy age- and sex-matched control subjects, recruited as part of the Finnish Diabetic Nephropathy Study. All participants underwent clinical work-up and brain MRI. Callosal area was manually measured and callosal volume quantified with FreeSurfer. The measures were normalized using manually measured mid-sagittal intracranial area and volumetric intracranial volume, respectively. Results: Manual and automatic measurements correlated well (callosal area vs. volume: ρ = 0.83, p < 0.001 and mid-sagittal area vs. intracranial volume: ρ = 0.82, p < 0.001). We found no significant differences in the callosal measures between cases and controls. In type 1 diabetes, the lowest quartile of normalized callosal area was associated with higher insulin doses (p = 0.029) and reduced insulin sensitivity (p = 0.033). In addition, participants with more than two cerebral microbleeds had smaller callosal area (p = 0.002). Conclusion: Manually measured callosal area and automatically segmented are interchangeable. The association seen between callosal size with cerebral microbleeds and insulin resistance is indicative of small vessel disease pathology in diabetes type 1.

3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke.

BACKGROUND: The aim of this study was to investigate the feasibility of ischemic stroke detection from computed tomography angiography source images (CTA-SI) using three-dimensional convolutional neural networks. METHODS: CTA-SI of 60 patients with a suspected acute ischemic stroke of the middle cerebral artery were randomly selected for this study; 30 patients were used in the neural network training, and the subsequent testing was performed using the remaining 30 patients. The training and testing were based on manually segmented lesions. Cerebral hemispheric comparison CTA and non-contrast computed tomography (NCCT) were studied as additional input features. RESULTS: All ischemic lesions in the testing data were correctly lateralized, and a high correspondence to manual segmentations was achieved. Patients with a diagnosed stroke had clinically relevant regions labeled infarcted with a 0.93 sensitivity and 0.82 specificity. The highest achieved voxel-wise area under receiver operating characteristic curve was 0.93, and the highest Dice similarity coefficient was 0.61. When cerebral hemispheric comparison was used as an input feature, the algorithm performance improved. Only a slight effect was seen when NCCT was included. CONCLUSION: The results support the hypothesis that an acute ischemic stroke lesion can be detected with 3D convolutional neural network-based software from CTA-SI. Utilizing information from the contralateral hemisphere appears to be beneficial for reducing false positive findings.

Boron neutron capture therapy in the treatment of locally recurred head and neck cancer.

PURPOSE: Head and neck carcinomas that recur locally after conventional irradiation pose a difficult therapeutic problem. We evaluated safety and efficacy of boron neutron capture therapy (BNCT) in the treatment of such cancers. METHODS AND MATERIALS: Twelve patients with inoperable, recurred, locally advanced (rT3, rT4, or rN2) head and neck cancer were treated with BNCT in a prospective, single-center Phase I-II study. Prior treatments consisted of surgery and conventionally fractionated photon irradiation to a cumulative dose of 56-74 Gy administered with or without concomitant chemotherapy. Tumor responses were assessed using the RECIST (Response Evaluation Criteria in Solid Tumors) criteria and adverse effects using the National Cancer Institute common toxicity grading v3.0. Intravenously administered boronophenylalanine-fructose (BPA-F, 400 mg/kg) was used as the boron carrier. Each patient was scheduled to be treated twice with BNCT. RESULTS: Ten patients received BNCT twice; 2 were treated once. Ten (83%) patients responded to BNCT, and 2 (17%) had tumor growth stabilization for 5.5 and 7.6 months. The median duration of response was 12.1 months; six responses were ongoing at the time of analysis or death (range, 4.9-19.2 months). Four (33%) patients were alive without recurrence with a median follow-up of 14.0 months (range, 12.8-19.2 months). The most common acute adverse effects were mucositis, fatigue, and local pain; 2 patients had a severe (Grade 3) late adverse effect (xerostomia, 1; dysphagia, 1). CONCLUSIONS: Boron neutron capture therapy is effective and safe in the treatment of inoperable, locally advanced head and neck carcinomas that recur at previously irradiated sites.

Multicellular dosimetric chain for molecular radiotherapy exemplified with dose simulations on 3D cell spheroids.

PURPOSE: Absorbed radiation dose-response relationships are not clear in molecular radiotherapy (MRT). Here, we propose a voxel-based dose calculation system for multicellular dosimetry in MRT. We applied confocal microscope images of a spherical cell aggregate i.e. a spheroid, to examine the computation of dose distribution within a tissue from the distribution of radiopharmaceuticals. METHODS: A confocal microscope Z-stack of a human hepatocellular carcinoma HepG2 spheroid was segmented using a support-vector machine algorithm and a watershed function. Heterogeneity in activity uptake was simulated by selecting a varying amount of the cell nuclei to contain 111In, 125I, or 177Lu. Absorbed dose simulations were carried out using vxlPen, a software application based on the Monte Carlo code PENELOPE. RESULTS: We developed a schema for radiopharmaceutical dosimetry. The schema utilizes a partially supervised segmentation method for cell-level image data together with a novel main program for voxel-based radiation dose simulations. We observed that for 177Lu, radiation cross-fire enabled full dose coverage even if the radiopharmaceutical had accumulated to only 60% of the spheroid cells. This effect was not found with 111In and 125I. Using these Auger/internal conversion electron emitters seemed to guarantee that only the cells with a high enough activity uptake will accumulate a lethal amount of dose, while neighboring cells are spared. CONCLUSIONS: We computed absorbed radiation dose distributions in a 3D-cultured cell spheroid with a novel multicellular dosimetric chain. Combined with pharmacological studies in different tissue models, our cell-level dosimetric calculation method can clarify dose-response relationships for radiopharmaceuticals used in MRT.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI.

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).

A dosimetric study on the use of bolus materials for treatment of superficial tumors with BNCT.

For treatment of superficially located tumors, such as head and neck cancers that invade the skin, the tumor dose may remain low on the skin when such tumors are treated with epithermal neutrons in boron neutron capture therapy (BNCT). The goal of this study was to examine the effects of bolus material for BNCT of superficial tumors, to verify the calculated (55)Mn(n, gamma) and the (197)Au(n, gamma) activation reaction rates and the neutron and the gamma doses in a phantom irradiated with a bolus, to measure the neutron activation of the bolus materials after irradiation, and according to depth dose distribution, to estimate when it is advantageous to use a bolus in BNCT. The present data show that both paraffin and water gel can be used as a bolus material for BNCT. However, we recommend paraffin for clinical use, since it is durable and can be easily shaped. A 5 mm paraffin bolus increases the surface dose approximately 50%, and its use may be advantageous for treatment of superficial tumors where the planning target volume (PTV) reaches to 6 cm or less in tissue depth.