A Review of Low-Cost Ultrasound Compatible Phantoms.

Ultrasound-compatible phantoms are used to develop novel US-based systems and train simulated medical interventions. The price difference between lab-made and commercially available ultrasound-compatible phantoms lead to the publication of many papers categorized as low-cost in the literature. The aim of this review was to improve the phantom selection process by summarizing the pertinent literature. We compiled papers on US-compatible spine, prostate, vascular, breast, kidney, and li ver phantoms. We reviewed papers for cost and accessibility, providing an overview of the materials, construction time, shelf life, needle insertion limits, and manufacturing and evaluation methods. This information was summarized by anatomy. The clinical application associated with each phantom was also reported for those interested in a particular intervention. Techniques and common practices for building low-cost phantoms were provided. Overall, this article aims to summarize a breadth of ultrasound-compatible phantom research to enable informed phantom methods selection.

Anatomical assessment of trigeminal nerve tractography using diffusion MRI: A comparison of acquisition b-values and single- and multi-fiber tracking strategies.

BACKGROUND: The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm2 and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. OBJECTIVE: We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN. METHODS: We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm2, b = 2000 s/mm2 and b = 3000 s/mm2, using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result. RESULTS: The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result. CONCLUSIONS: Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.

Multimodal image registration for liver radioembolization planning and patient assessment.

PURPOSE: Multimodal imaging plays a key role in patient assessment and treatment planning in liver radioembolization. It will reach its full potential for convenient use in combination with deformable image registration methods. A registration framework is proposed for multimodal liver image registration of multi-phase CT, contrast-enhanced late-phase T1, T2, and DWI MRI sequences. METHODS: A chain of four pair-wise image registrations based on a variational registration framework using normalized gradient fields as distance measure and curvature regularization is introduced. A total of 103 cases of 35 patients was evaluated based on anatomical landmarks and deformation characteristics. RESULTS: Good anatomical correspondence and physical plausibility of the deformation fields were attained. The global mean landmark errors vary from 3.20 to 5.36 mm, strongly influenced by low resolved images in z-direction. Moderate volume changes are indicated by mean minimum and maximum Jacobian determinants of 0.44 up to 1.88. No deformation foldings were detected. The mean average divergence of the deformation fields range from 0.08 to 0.16 and the mean harmonic energies vary from 0.08 to 0.58. CONCLUSION: The proposed registration solutions enable the combined use of information from multimodal imaging and provide an excellent basis for patient assessment and primary planning for liver radioembolization.

Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury.

Available approaches to the investigation of traumatic brain injury (TBI) are frequently hampered, to some extent, by the unsatisfactory abilities of existing methodologies to efficiently define and represent affected structural connectivity and functional mechanisms underlying TBI-related pathology. In this paper, we describe a patient-tailored framework which allows mapping and characterization of TBI-related structural damage to the brain via multimodal neuroimaging and personalized connectomics. Specifically, we introduce a graphically driven approach for the assessment of trauma-related atrophy of white matter connections between cortical structures, with relevance to the quantification of TBI chronic case evolution. This approach allows one to inform the formulation of graphical neurophysiological and neuropsychological TBI profiles based on the particular structural deficits of the affected patient. In addition, it allows one to relate the findings supplied by our workflow to the existing body of research that focuses on the functional roles of the cortical structures being targeted. A graphical means for representing patient TBI status is relevant to the emerging field of personalized medicine and to the investigation of neural atrophy.

Lung extraction, lobe segmentation and hierarchical region assessment for quantitative analysis on high resolution computed tomography images.

Regional assessment of lung disease (such as chronic obstructive pulmonary disease) is a critical component to accurate patient diagnosis. Software tools than enable such analysis are also important for clinical research studies. In this work, we present an image segmentation and data representation framework that enables quantitative analysis specific to different lung regions on high resolution computed tomography (HRCT) datasets. We present an offline, fully automatic image processing chain that generates airway, vessel, and lung mask segmentations in which the left and right lung are delineated. We describe a novel lung lobe segmentation tool that produces reproducible results with minimal user interaction. A usability study performed across twenty datasets (inspiratory and expiratory exams including a range of disease states) demonstrates the tool's ability to generate results within five to seven minutes on average. We also describe a data representation scheme that involves compact encoding of label maps such that both "regions" (such as lung lobes) and "types" (such as emphysematous parenchyma) can be simultaneously represented at a given location in the HRCT.

Volumetric assessment of tumor infiltration of adjacent white matter based on anatomic MRI and diffusion tensor tractography.

RATIONALE AND OBJECTIVES: To perform a retrospective, quantitative assessment of the anatomic relationship between intra-axial, supratentorial, primary brain tumors, and adjacent white matter fiber tracts based on anatomic and diffusion tensor magnetic resonance imaging (MRI). We hypothesized that white matter infiltration may be common among different types of tumor. MATERIAL AND METHODS: Preoperative, anatomic (T1- and T2-weighted), and LINESCAN diffusion tensor MRI were obtained in 12 patients harboring supratentorial gliomas (World Health Organization [WHO] Grades II and III). The two imaging modalities were rigidly registered. The tumors were manually segmented from the T1- and T2-weighted MRI, and their volume calculated. A three-dimensional tractography was performed in each case. A second segmentation and volume measurement was performed on the tumor regions intersecting adjacent white matter fiber tracts. Statistical methods included summary statistics to examine the fraction of tumor volume infiltrating adjacent white matter. RESULTS: There were five patients with low-grade oligodendroglioma (WHO Grade II), one with low-grade mixed oligoastrocytoma (WHO Grade II), one with ganglioglioma, two with low-grade astrocytoma (WHO Grade II), and three with anaplastic astrocytoma (WHO Grade III). We identified white matter tracts infiltrated by tumor in all 12 cases. The median tumor volume (+/- standard deviation) in our patient population was 42.5 +/- 28.9 mL. The median tumor volume (+/- standard deviation) infiltrating white matter fiber tracts was 5.2 +/- 9.9 mL. The median percentage of tumor volume infiltrating white matter fiber tracts was 21.4% +/- 9.7%. CONCLUSIONS: The information provided by diffusion tensor imaging combined with anatomic MRI might be useful for neurosurgical planning and intraoperative guidance. Our results confirm previous reports that extensive white matter infiltration by primary brain tumors is a common occurrence. However, prospective, large population studies are required to definitively clarify this issue, and how infiltration relates to histologic tumor type, tumor size, and location.

Three validation metrics for automated probabilistic image segmentation of brain tumours.

The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts' manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered.

Magnetic resonance imaging based colonography for diagnosis and assessment of diverticulosis and diverticulitis.

BACKGROUND AND AIMS: MRI-based colonography is a new minimally invasive imaging modality to assess the colon and abdomen. This new method which is applied mainly for polyp screening could be an integrative approach for colonic diverticulitis assessment. This study evaluated the feasibility of MRI-based colonography to assess diverticulosis or diverticulitis. PATIENTS AND METHODS: Fourteen consecutive patients with clinically suspected diverticulitis were examined by MRI colonography on a 1.5-T scanner. All patients underwent abdominal CT as gold standard. N-Butyl-scopalamin was given intravenously to reduce bowel peristalsis. After rectal administration of a T1-positive enema T1- and T2-weighted acquisitions with additional intravenous contrast were obtained. A 3D FLASH sequence was acquired for virtual colonography. The results were compared with CT and biological parameters such as white blood cell count and C-reactive protein. RESULTS: Of 56 bowel segments (sigmoid colon, descending colon, transverse colon, ascending colon) in all 14 patients 54 were assessed to have good to fair image quality. Having CT as standard of reference, all sigmoid diverticula were diagnosed based on MRI. Inflammation as judged by CT was identically assessed on MRI. 3D models of the colon revealed further diverticula in the remaining colon; additionally, the 3D models gave a comprehensive image for surgical planning. CONCLUSION: In our preliminary study MRI colonography revealed the same diagnosis as CT in all patients without ionizing radiation. Additionally, 3D-rendered models and virtual colonoscopy can be performed. This comprehensive 3D models could replace presurgical planning barium enema with concurrent assessment of the residual colon.

Quantitative MR imaging assessment of prostate gland deformation before and during MR imaging-guided brachytherapy.

RATIONALE AND OBJECTIVES: The authors performed this study to document the deformations that occur between pretreatment magnetic resonance (MR) imaging and intraoperative MR imaging during brachytherapy. MATERIALS AND METHODS: MR images obtained at 1.5 and 0.5 T in 10 patients with prostate cancer were analyzed for changes in the shape and substructure of the prostate. Three-dimensional models of the prostate were obtained. The authors measured anteroposterior dimension; total gland, peripheral zone, and central gland volumes; transverse dimension; and superoinferior height. RESULTS: Gland deformations were seen at visual inspection of the three-dimensional models. The anteroposterior dimension of the total gland, central gland, and peripheral zone increased from 1.5- to 0.5-T imaging (median dimension, 4.9, 1.5, and 1.8 mm, respectively), and the increase was greatest in the peripheral zone (P < .05, all comparisons). There was a decrease in the transverse dimension from 1.5- to 0.5-T imaging (median, 4.5 mm; P < .005). The total gland volume and the superoinferior height did not show a statistically significant change. CONCLUSION: There were significant deformations in the shape of the prostate, especially in the peripheral zone, between the two imaging studies. The likely causes of the shape change are differences in rectal filling (endorectal coil used in 1.5-T studies vs obturator in 0.5-T studies) and/or changes in patient position (supine vs lithotomy). These findings suggest that pretreatment images alone may not be reliable for accurate therapy planning. It may be useful to integrate pre-and intraoperative data.