Nonlinear handheld detection of magnetic nanoparticles is used to assess the lymph node status of cancer patients. Joint sensitivity and resolving power of nonlinear handheld detection can be maximized by optimizing the frequency of the excitation field, which is strongly influenced by Brownian and Néel relaxation. The characteristic frequency of magnetic nanoparticles that defines sensitivity and resolving power is usually assessed by AC susceptometry. In this study, we used SPaQ data to predict handheld detection performance for magnetic nanoparticles with various particle sizes. SPaQ assesses dynamics by measuring the derivative of the magnetization originating from magnetic nanoparticles activated by an alternating excitation field. The ratio between the maximum signal difference and full-width-at-half-maximumis used to estimate the optimal excitation frequency. Thereupon, it was shown that a particle with a combination of Brownian and Néel relaxation is superior in nonlinear handheld detection compared to Brownian or Néel only particles. Moreover, the optimal excitation frequency is generally established at a slightly higher frequency compared to the characteristic frequency assessed by AC susceptometry. Consequently, this insight into the consequences of the dynamic behavior of magnetic nanoparticles under an alternating magnetic field enables the optimization of nonlinear handheld detection for specific clinical applications.
Distinguishing critical tissues can be challenging in (para-)thyroid surgery. As a first step towards intraoperative image contrast enhancement, we collected and analyzed spectral signatures (350-1830nm) of thyroid, parathyroid and recurrent laryngeal nerve in 10 patients.
Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is becoming an indispensable tool to non-invasively study tumor characteristics. However, many different DCE-analysis methods are currently being used. To compare and validate different methods, histology is the gold standard. For this purpose, exact co-localization between histology and MRI images is a prerequisite. In this study a methodology is developed to validate DCE-data with histology with an emphasis on correct registration of DCE-MRI and histological data. A pancreatic tumor was grown in a rat model. The tumor was dissected after MR imaging, embedded in paraffin, and cut into thin slices. These slices were stained with haematoxylin and eosin, digitized and stacked in a 3D volume. Next, the 3D histology was registered to ex-vivo SWI-weighted MR images, which in turn were registered to in-vivo SWI and DCE images to achieve correct co-localization. Semi-quantitative and quantitative parameters were calculated. Preliminary results suggest that both pharmacokinetic and heuristic DCE-parameters can discriminate between vital and non-vital tumor regions. The developed method offers the basis for an accurate spatial correlation between DCE-MRI derived parametric maps and histology, and facilitates the evaluation of different DCE-MRI analysis methods.
Abstract Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.
Neovascular glaucoma (NVG) is a severe type characterized by forming new blood vessels on the iris and the anterior chamber angle, often resulting from ischemic retinal diseases. Pars plana vitrectomy (PPV) is a standard surgical procedure for treating various retinal and vitreous conditions. Understanding the risk factors associated with NVG development following PPV is crucial for improving patient outcomes.
Adsorption followed by stepwise desorption to concentrate volatile fatty acids (VFAs) from very dilute aqueous streams is challenging and only a limited amount of VFAs can be collected at high concentration using N2-stripping. Here, we describe the preparation and use of superparamagnetic porous adsorbents to recover much larger fractions of VFAs in highly concentrated form from dilute aqueous streams than with the state-of-the-art N2-stripping technique. Our system is based on poly(divinylbenzene) (PDVB) impregnated with superparamagnetic magnetite nanoparticles (MNPs) synthesized by coprecipitation and functionalized with oleic acid (OA). The OA grafted MNPs (OA-MNPs) were embedded in the matrix of the polymer during suspension polymerization. The porous particles had an average size of 222 ± 40 µm with a surface area of 496 ± 10 m2/g and contained 11 ± 1 wt% MNPs with an average core size of 10 nm. VFAs adsorption from a dilute aqueous solution (containing 0.25 wt% of each acid) reached a saturation capacity of 43 g carboxylic acid per kg adsorbent,. The two-stage desorption was started with alternating magnetic field (AMF) heating at 25 mT and 52 kHz, followed by a hot N2 stripping stage removed 90 ± 9% of the water that had physically filled the pores during adsorption, and only 11 ± 2% of the loaded VFAs. Subsequently, 89 ± 3% of the VFAs were recovered almost water free using hot N2 stripping. Compared to sorbent regeneration and vapor fractionation by nitrogen stripping with a temperature gradient only, this new approach offers a much sharper acid fractionation from water, facilitating large energy savings in downstream operations.
Abstract Purpose In breast conserving surgery, accurate lesion localization is essential for obtaining adequate surgical margins. Preoperative wire localization (WL) and radioactive seed localization (RSL) are widely accepted methods to guide surgical excision of nonpalpable breast lesions but are limited by logistical challenges, migration issues, and legislative complexities. Radiofrequency identification (RFID) technology may offer a viable alternative. The purpose of this study was to evaluate the feasibility, clinical acceptability, and safety of RFID surgical guidance for localization of nonpalpable breast cancer. Methods In a prospective multicentre cohort study, the first 100 RFID localization procedures were included. The primary outcome was the percentage of clear resection margins and re-excision rate. Secondary outcomes included procedure details, user experience, learningcurve, and adverse events. Results Between April 2019 and May 2021, 100 women underwent RFID guided breast conserving surgery. Clear resection margins were obtained in 89 out of 96 included patients (92.7%), re-excision was indicated in three patients (3.1%). Radiologists reported difficulties with the placement of the RFID tag, partially related to the relatively large needle-applicator (12-gauge). This led to the premature termination of the study in the hospital using RSL as regular care. The radiologist experience was improved after a manufacturer modification of the needle-applicator. Surgical localization involved a low learning curve. Adverse events ( n = 33) included dislocation of the marker during insertion (8%) and hematomas (9%). The majority of adverse events (85%) occurred using the first-generation needle-applicator. Conclusion RFID technology is a potential alternative for non-radioactive and non-wire localization of nonpalpable breast lesions.
Background Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize cancerous tissue. However, registration is complicated by deformations during pathological processing, and differences in scale and information content. Methodology/Principal Findings This study proposes a methodology for establishing an accurate 3D relation between histological sections and high resolution in vivo MRI tumor data. The key features of the methodology are: 1) standardized acquisition and processing, 2) use of an intermediate ex vivo MRI, 3) use of a reference cutting plane, 4) dense histological sampling, 5) elastic registration, and 6) use of complete 3D data sets. Five rat pancreatic tumors imaged by T2*-w MRI were used to evaluate the proposed methodology. The registration accuracy was assessed by root mean squared (RMS) distances between manually annotated landmark points in both modalities. After elastic registration the average RMS distance decreased from 1.4 to 0.7 mm. The intermediate ex vivo MRI and the reference cutting plane shared by all three 3D images (in vivo MRI, ex vivo MRI, and 3D histology data) were found to be crucial for the accurate co-registration between the 3D histological data set and in vivo MRI. The MR intensity in necrotic regions, as manually annotated in 3D histology, was significantly different from other histologically confirmed regions (i.e., viable and hemorrhagic). However, the viable and the hemorrhagic regions showed a large overlap in T2*-w MRI signal intensity. Conclusions The established 3D correspondence between tumor histology and in vivo MRI enables extraction of MRI characteristics for histologically confirmed regions. The proposed methodology allows the creation of a tumor database of spatially registered multi-spectral MR images and multi-stained 3D histology.
To assess the feasibility and merits of a complete magnetic approach for a sentinel lymph node biopsy (SLNB) procedure in oral cancer patients. This study included ten oral cancer patients (stage cT1-T2N0M0) scheduled for elective neck dissection (END). Superparamagnetic iron oxide nanoparticles (SPIO) were administered peritumorally prior to surgery. A preoperative MRI was acquired to identify lymph nodes (LNs) with iron uptake. A magnetic detector was used to identify magnetic hotspots prior, during, and after the SLNB procedure. The resected sentinel LNs (SLNs) were evaluated using step-serial sectioning, and the neck dissection specimen was assessed by routine histopathological examination. A postoperative MRI was acquired to observe any residual iron. Of ten primary tumors, eight were located in the tongue, one floor-of-mouth (FOM), and one tongue-FOM transition. SPIO injections were experienced as painful by nine patients, two of whom developed a tongue swelling. In eight patients, magnetic SLNs were successfully detected and excised during the magnetic SLNB procedure. During the END procedure, additional magnetic SLNs were identified in three patients. Histopathology confirmed iron deposits in sinuses of excised SLNs. Three SLNs were harboring metastases, of which one was identified only during the END procedure. The END specimens revealed no further metastases. A complete magnetic SLNB procedure was successfully performed in eight of ten patients (80% success rate), therefore the procedure seems feasible. Recommendations for further investigation are made including: use of anesthetics, magnetic tracer volume, planning preoperative MRI, comparison to conventional technique and follow-up.
