Tatoo Ink, Magnetism and Sensation of Burn during Magnetic Resonance Imaging, and Introduction of Hand-Held Magnet Testing of Commercial Tattoo Ink Stock Products prior to Use.

BackgroundPersons with cosmetic tattoos occasionally experience severe pain and burning sensation on magnetic resonance imaging (MRI).ObjectiveTo explore the culprit magnetic substances in commonly used permanent makeup inks.Material and methods20 inks used for cosmetic tattooing of eyebrows, eyeliners, and lips were selected. Ink bottles were tested for magnetic behavior with a neodymium magnet. Eight iron oxide inks qualified for the final study. Metals were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP‐MS). The magnetic fraction of inks was isolated and analyzed by X‐ray fluorescence (XRF). Magnetic iron compounds were characterized by Mössbauer spectroscopy and powder X‐ray diffraction (XRD).ResultsICP‐MS showed iron in all magnetic samples, and some nickel and chromium. Mössbauer spectroscopy and XRD detected ferromagnetic minerals, particularly magnetite, followed by goethite and hematite.ConclusionThis original study of cosmetic ink stock products made with iron oxide pigments reports magnetic impurities in inks for cosmetic tattooing, e.g., magnetite, goethite, and hematite. These may be the main cause of MRI burn sensation in cosmetic tattoos. The mechanism behind sensations is hypothesized to be induction of electrical stimuli of axons from periaxonal pigment/impurity activated by magnetic force. Magnetite is considered the lead culprit.

Tattooed persons examined with magnetic resonance imaging (MRI) can develop burning sensation suggested in the literature to be thermal burn from the procedure. MRI-induced thermal effect and magnetic behavior of known tattoo pigments were examined ex vivo. Magnetic resonance imaging effects on 3 commonly used commercial ink stock products marketed for cosmetic tattooing was studied. A main study tested 22 formulations based on 11 pigment raw materials, for example, one line of 11 called pastes and another called dispersions. Samples were spread in petri dishes and tested with a 0.97 T neodymium solid magnet to observe visual magnetic behavior. Before MRI, the surface temperature of the ink was measured using an infrared probe. Samples were placed in a clinical 3T scanner. Two scans were performed, that is, one in the isocenter and one 30cm away from the center. After scanning, the surface temperature was measured again. Chemical analysis of samples was performed by mass spectroscopy. Mean temperature increase measured in the isocenter ranged between 0.14 and 0.26°C (P<.01) and in the off-center position from -0.16 to 0.21°C (P<.01). Such low increase of temperature is clinically irrelevant. Chemical analysis showed high concentrations of iron, but also nickel and chrome were found as contaminants. High concentration of iron was not associated with any increase of temperature or any physical draw or move of ink. The study could not confirm any clinically relevant temperature increase of tattoo pigments after MRI.

Vessel segmentation is important for many clinical applications, such as the diagnosis of vascular diseases, the planning of surgery, or the monitoring of the progress of disease. Although various approaches have been proposed to segment vessel structures from 3-dimensional medical images, to the best of our knowledge, there has been no known technique that uses magnetic resonance imaging (MRI) as prior information within the vessel segmentation of magnetic resonance angiography (MRA) or magnetic resonance venography (MRV) images. In this study, we propose a novel method that uses MRI images as an atlas, assuming that the patient has an MRI image in addition to MRA/MRV images. The proposed approach intends to increase vessel segmentation accuracy by using the available MRI image as prior information. We use a rigid mutual information registration of the MRA/MRV to the MRI, which provides subvoxel accurate multimodal image registration. On the other hand, vessel segmentation methods tend to mostly suer from imaging artifacts, such as Rician noise, radio frequency (RF) inhomogeneity, or partial volume eects that are generated by imaging devices. Therefore, this proposed method aims to extract all of the vascular structures from MRA/MRI or MRV/MRI pairs at the same time, while minimizing the combined eects of noise and RF inhomogeneity. Our method is validated both quantitatively and visually using BrainWeb phantom images and clinical MRI, MRA, and MRV images. Comparison and observer studies are also realized using the BrainWeb database and clinical images. The computation time is markedly reduced by developing a parallel implementation using the Nvidia compute unied device architecture and OpenMP frameworks in order to allow the use of the method in clinical settings.

Objective To investigate the way to accurately delineate gross tumor volume (GTV) of high grade gliomas(HGG) for intensity modulated radiation therapy (IMRT) by using computed tomography (CT) and magnetic resonance imaging (MRI) image fusion technique. Methods CT and MRI images were fused from 19 patients. The GTV of each patient were independently delineated by one chief doctor and one resident doctor on CT and MRI image. The GTV contoured on CT (GTVCT), MRI (GTVMRI) were measured, and composite volumes (GTVCT+MRI) were the sum of CT-defined GTV and MRI-defined GTV. The differences of these volumes were compared. Results Whether chief or resident doctors delineated, all were GTVMRI ＞GTVCT(P ＜0.050). The percentages of GTVMRI on GTVCT+MRI were (98.57±7.00)% by chief doctors, and (97.84±10.00)% by resident doctors. Compared the difference between GTVCT and GTVMRI in postoperative patients and preoperative patients, P =0.046, and the difference between chief doctors and resident doctors was statistically significant for GTV defined by CT (P =0.020), but not by MRI and composite image (P ＞0.050).Conclusion The GTV of HGG patients must be delineated on both CT image and MRI image, including using CT and MRI image fusion. But the composite volumes(GTVCT+MRI) should be the sum of CT-defined GTV and MRI-defined GTV. Especially for the postoperative patients,delineating GTV should be taken more attention. And the GTV should be delineated by doctors with full experiences. Key words: Glioma; Tomography, X-ray computed; Magnetic resonance imaging; Radiotherapy,intensity-modulated; Gross tumor volume

A critical step in predicting and avoiding radiation injury of organs at risk in radiation therapy of nasopharyngeal carcinoma is to carry out an accurate dose evaluation in planning design. In the present study, we investigated the dose evaluation feature of organs at risk on magnetic resonance imaging (MRI) images in intensity‐modulated radiation therapy of nasopharyngeal carcinoma compared with computed tomography (CT) images. A total of 35 nasopharyngeal carcinoma patients were selected for this trial. CT simulation with non‐contrast and contrast‐enhanced scan, and MRI simulation with non‐contrast and contrast‐enhanced T1, T2, and diffusion weighted imaging were obtained sequentially. The organs at risk were contoured on the CT and MRI images after rigid registration, respectively. Nine‐beam intensity‐modulated radiation therapy plans with equal division angles were designed for every patient, and the prescription dose for the tumor target was set as 72 Gy (2.4Gy/fraction). The boundary display, volume, and dosimetric indices of each organ were compared between MRI and CT images. We found that MRI showed clearer boundary of the brainstem, spinal cord, deep lobe of the parotid gland, and the optical nerve in the canal compared with CT. MRI images increased the volume of the lens and optic nerve, while slightly reducing the volume of eye; the maximum dose of the lens, and the mean dose of the eyes and optic nerve increased to different extents, though no statistical differences were found. The left and right parotid gland volume on MRI increased by 7.07% and 8.13%, and the mean dose increased by 14.95% (4.01 Gy) and 18.76% (4.95 Gy), with a statistically significant difference (P &lt; 0.05). The brainstem volume reduced by 9.33% (P &lt; 0.05), and the dose of 0.1 cm3 volume reduced by a mean 8.46% (4.32 Gy), whereas the dose of 0.1 cm3 of the spinal cord increased by 1.5 Gy on MRI. The maximum dose region of the spinal cord was very close on CT and MRI images, and was similar to the brainstem. In conclusion, it is credible to evaluate the radiation dose of the lens, eye, brainstem, and the spinal cord by applying simulation CT; whereas MRI images are sometimes necessary to evaluate the dose of the parotid glands and the optical nerve.

Objectives: Anterior cruciate ligament (ACL) tears are among the most frequent ligament injuries in the United States. Most of these injuries are treated surgically, depending on patient-specific factors. Management of these injuries includes nonoperative management with stepwise rehabilitation or surgical treatment with ACL repair or reconstruction. Patient goals, a clinical evaluation, and preoperative MRI imaging are generally assessed to inform the choice of treatment. Classification of ACL tear-type based on stump morphology is critical to preoperative planning, as it can determine a patient’s eligibility for ACL preservation techniques. We aim to determine the efficacy of preoperative magnetic resonance imaging (MRI) in predicting ruptured ACL stump morphology and thus assist in preoperative planning and decision-making. Methods: This retrospective observational study utilized chart review from the case log of a single surgeon at an urban tertiary institution. The review identified patients who underwent the Bridge Enhanced ACL Repair (BEAR) procedure between January 1, 2022 and August 31, 2023. The inclusion criteria included patients who underwent a primary ACL repair with the BEAR technique and had adequate intraoperative and MRI images available for review. Patients excluded were those with inadequate intraoperative imaging or MRI imaging that was unavailable for review. For each patient included, the two senior authors assessed the morphology of the ACL stump on MRI (sagittal T2, coronal T2) and intraoperative arthroscopy images in a blinded manner and assigned a classification grade to each. The MRI-based morphology and the intraoperative image morphology of the tears were classified based on a system validated by DeFelice et al, which includes types 1 to 5 based on the location of the tear from most proximal to most distal. The correlation between the preoperative MRI assessment and the intraoperative arthroscopic assessment was interrogated using simple linear regression quantified with the Pearson correlation coefficient. Results: A total of 35 patients that underwent the BEAR procedure were identified. One patient was excluded due to inadequate intraoperative images. Of the remaining patients, 13 (38.2%) were male and 21 (61.8%) were female. The average patient age was 31, with a median age of 28. Most patients were injured while skiing (32.4%), while the second and third most prevalent mechanisms of injury were nonsports-related (23.4%) and soccer injuries (17.6%), respectively. Of the MRI tears that were analyzed, 9 (26.5%) were classified as type 1, 19 (55.9%) were type 2, and 5 (14.7%) were type 3. There were no tears identified as types 4 or 5 tears. Of the arthroscopic images that were analyzed, 21 (61.8%) were classified as type 1, 10 (29.4%) were type 2, 3 (8.8%) were type 3. Similarly, there were no tears identified as types 4 or 5. MRI accurately predicted the ACL tear type seen arthroscopically 55.9% (19/34) of the time. Of the (15) cases in which MRI did not correlate with the arthroscopic tear type, the MRI classification grade was within 1 grade of the arthroscopic grade 93.3% of the time (14/15). In the cases in which MRI was not correct in its grading, 93.3% of them (14/15) were classified as too high of a grade (MRI perceiving the tear to be more distal than it was in actuality). The Pearson correlation coefficient assessing the correlation between MRI identified tear type and arthroscopy identified tear type was 0.56. Conclusions: MRI accurately predicted the ACL tear type seen arthroscopically in only 55.9% of patients. In &gt;90% of incorrect predictions, arthroscopy determined the ACL tear to be one classification more proximal than predicted by MRI. Our results demonstrate that while preoperative MRI can be useful in identifying approximate ACL tear location, it is essential to examine the ACL at time of arthroscopy for final determination of ACL stump size and viability for ACL restoration procedures. Moreover, a greater percentage of patients may be eligible for an ACL restoration procedure than predicted by preoperative MRI, highlighting the importance of discussing both ACL repair and ACL reconstruction with patients preoperatively. [Figure: see text]

An automated computerised tomography (CT) and magnetic resonance imaging (MRI) brain images are used to perform an efficient classification. The proposed technique consists of three stages, namely, pre-processing, feature extraction and classification. Initially, pre-processing is performed to remove the noise from the medical MRI images. Then, in the feature extraction stage, the features that are related with MRI and CT images are extracted and these extracted features which are given to the Feed Forward Back-propagation Neural Network (FFBNN) is exploited in order to classify the brain MRI and CT images into two types: normal and abnormal. The FFBNN is well trained by the extracted features and uses the unknown medical brain MRI images for classification in order to achieve better classification performance. The proposed method is validated by various MRI and CT scan images. A classification with an accomplishment of 96% and 70% has been obtained by the proposed FFBNN classifier. This achievement shows the effectiveness of the proposed brain image classification technique when compared with other recent research works.

BackgroundLinear measurement analysis using computed tomography (CT) scans to quantify abdominal muscle mass has been validated as a clinically practical approach for screening individuals with low muscle mass. However, there is still a need to validate such analysis using magnetic resonance imaging (MRI) imaging. The aim of this study is to assess the reproducibility and concordance of CT and MRI imaging for linear measurement analyses of skeletal muscle at mid‐L3.MethodsWe retrospectively analysed 66 patients with available CT and MRI images within 30 days of one other to evaluate linear measurement CT and MRI concordance. Linear measurement analysis for abdominal/pelvic CT and MRI scans for eight patients was conducted independently three times by the same person separated by at least 1 week to assess intra‐rater variability. The intra‐observer variability for both CT and MRI was assessed using the intraclass correlation coefficient (ICC). The concordance and correlation of CT and MRI mid‐L3 for linear measurements were assessed using Pearson correlation coefficients and Bland–Altman plots.ResultsThe intra‐rater reliability of linear measurements for both CT and MRI was high, as measured by the ICC (CT range: 0.788–0.992; MRI range: 0.766–0.984). CT and MRI linear measurements were found to be significantly positively correlated for all psoas (total psoas r = 0.98; P &lt; 0.0001) and paraspinal muscle measurements (total paraspinal r = 0.99; P &lt; 0.0001). Bland–Altman analysis revealed a mean bias of 0.83 (range: 0.03–5.56) for MRI over CT linear measurements.ConclusionsCT and MRI images were shown to be concordant for linear measurement analysis of abdominal muscle mass. T2‐weighted MRI sequences can be used interchangeably with CT in the assessment of sarcopenia using linear measurement analysis.

The last quarter century has witnessed major advancements that have brought biomedical imaging to a paramount status in life sciences. Generally speaking, the scope of biomedical imaging covers data acquisition, image reconstruction, and image analysis, involving theories, methods, systems, and applications. While many kinds of imaging modalities, such as X-ray computed tomography (CT) and magnetic resonance imaging (MRI), become increasingly sophisticated, the mathematical methods involved in these modalities play more and more critical roles in further improving their performance in anatomical, functional, cellular, and molecular applications. The overall goal of this issue is to promote research and development of biomedical imaging by publishing high-quality research articles in this rapidly growing interdisciplinary field. Due to the time limit, this special issue mainly focused on 4 kinds of biomedical imaging modalities: CT, MRI, ultrasound, and fluorescence imaging; several biomedical image processing methods were also involved. Each paper published in this special issue was reviewed by at least two reviewers and revised according to reviewer's comments. For CT imaging, A. Cai et al. developed an efficient iterative image reconstruction (IIR) algorithm, using cone beam CT reconstruction that is based on total-variation (TV) minimization to overcome the computational complexity of IIR scheme in cone beam CT reconstruction; L.-z. Deng et al. proposed a hybrid reconstruction method combining TV and nonaliasing contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. This algorithm utilized the geometrical information of CT image and got a sparser representation compared with wavelet and gradient operator. For MRI imaging, in order to reduce time consuming in MRI image reconstruction, Q. Li et al. proposed a parallel computing method which was based on a novel patch-based nonlocal operator (PANO). Simulation results demonstrated that this method can accelerate PANO-based MRI reconstruction several times compared with original one. W. He et al. introduced a direct nonconvex Lp norm algorithm for MRI phase unwrapping which leaded to faithful phase correction. Also analytical high order tensor decomposition was introduced into crossing fibers detection in diffusion MRI by T. Megherbi et al., which provided a better angular resolution and accuracy than the classical maxima localization method. For biomedical image processing, R. Jaramillo et al. used a wavelet domain filter to improve the performance of the Prony method. In this work, MRI images were considered to be affected only by Rician noise, and a new wavelet domain bilateral filtering process was implemented to reduce the noise in the images. X. Wang et al. proposed a model that allows robustness to noise as well for handling the intensity inhomogeneity and weak boundary problems in medical image segmentation using region-scalable discriminant and fitting energy for image segmentation. Also, a region-based active contour model which introduced a total energy as a penalty function in medical image segmentation is proposed by T. Liu et al. Another active contours based segmentation method was proposed by F. Akram et al. for intensity inhomogeneous MRI images to enable boundaries detection of the homogenous regions. Not only CT and MRI but also other medical imaging modalities, such as ultrasound and fluorescence imaging, are included in this special issue. S.-K. Ueng et al. designed a special filter aiming at suppressing speckles and enhancing features in the ultrasound images. In this method, diffusion tensor of intensity at each pixel was represented in the form of a Hessian matrix which was used to compute eigen values at each pixel. The eigen values were used in detection and classifying the underlying structure and a refinement strategy was followed to improve the classification. Then, based on the computed structure types, feasible filters were selected from a filter pool to suppress speckles and enhance features. X. Lin et al. used three-dimensional fluorescent spectra imaging to investigate whether and how Tubeimoside 1 (TBMS 1) can affect HepG2 cells, which indicated that fluorescent spectra method is a promising substitute for flow cytometry in cancer research. A computational model to estimate fluence rate for a biological medium with inclusion was developed by M. Gantri. In this work, the entire setting of the medium was treated to have spatially and stochastically varying refractive index to match practical applications and Legendre integral transform technique is incorporated to solve the radiative transfer equation. These papers represent an insightful observation into the state of the art, as well as future topics in this biomedical imaging field. We hope that this special issue would attract a wide attention of the peers and provide a chance to share the latest research work. Peng Feng Kumar Durai Fenglin Liu Xiaobo Qu

In order to explore the effect of convolutional neural network (CNN) algorithm based on deep learning on magnetic resonance imaging (MRI) images of brain tumor patients and evaluate the practical value of MRI image features based on deep learning algorithm in the clinical diagnosis and nursing of malignant tumors, in this study, a brain tumor MRI image model based on the CNN algorithm was constructed, and 80 patients with brain tumors were selected as the research objects. They were divided into an experimental group (CNN algorithm) and a control group (traditional algorithm). The patients were nursed in the whole process. The macroscopic characteristics and imaging index of the MRI image and anxiety of patients in two groups were compared and analyzed. In addition, the image quality after nursing was checked. The results of the study revealed that the MRI characteristics of brain tumors based on CNN algorithm were clearer and more accurate in the fluid-attenuated inversion recovery (FLAIR), MRI T1, T1c, and T2; in terms of accuracy, sensitivity, and specificity, the mean value was 0.83, 0.84, and 0.83, which had obvious advantages compared with the traditional algorithm (P < 0.05). The patients in the nursing group showed lower depression scores and better MRI images in contrast to the control group (P < 0.05). Therefore, the deep learning algorithm can further accurately analyze the MRI image characteristics of brain tumor patients on the basis of conventional algorithms, showing high sensitivity and specificity, which improved the application value of MRI image characteristics in the diagnosis of malignant tumors. In addition, effective nursing for patients undergoing analysis and diagnosis on brain tumor MRI image characteristics can alleviate the patient's anxiety and ensure that high-quality MRI images were obtained after the examination.

Cosmetic tattoos such as eyeliner, brow liner, and lip liner have become increasingly popular in the United States and throughout the world. For a variety of reasons, patients frequently regret their tattoos and request their removal; however, removal is often complicated by the aesthetically sensitive location of these specialized tattoos and the fact that they often contain white metallic compounds that darken on pigment-specific laser irradiation. To review the clinical use, effectiveness, and safety of an ablative laser technique for cosmetic tattoos. A thorough literature review pertaining to laser treatment of cosmetic tattoos and a discussion of illustrative patient cases showcasing the successful use of ablative carbon dioxide (CO2) laser to treat cosmetic tattoos is presented. Cosmetic eyeliner and lip liner tattoos were significantly improved after CO2 laser vaporization. Side effects were limited to erythema, edema, and serosanguinous drainage. No infection, scarring, nor tattoo ink darkening was observed. Because ablative lasers do not target specific tattoo inks, they do not pose a risk of paradoxical tattoo ink darkening and, thus, can be applied successfully in the treatment of iron oxide- or titanium dioxide-containing cosmetic tattoos.

Surgical pearl: Removal of cosmetic lip-liner tattoo with the pulsed carbon dioxide laser

Brain tumor classification using Magnetic Resonance Imaging (MRI) images is an important and emerging field of medical imaging and artificial intelligence in the current world. With advancements in technology, particularly in deep learning and machine learning, researchers and clinicians are leveraging these tools to create complex models that, using MRI data, can reliably detect and classify tumors in the brain. However, it has a number of drawbacks, including the intricacy of tumor types and grades, intensity variations in MRI data and tumors varying in severity. This paper proposes a Multi-Grade Hierarchical Classification Network Model (MGHCN) for the hierarchical classification of tumor grades in MRI images. The model's distinctive feature lies in its ability to categorize tumors into multiple grades, thereby capturing the hierarchical nature of tumor severity. To address variations in intensity levels across different MRI samples, an Improved Adaptive Intensity Normalization (IAIN) pre-processing step is employed. This step standardizes intensity values, effectively mitigating the impact of intensity variations and ensuring more consistent analyses. The model renders utilization of the Dual Tree Complex Wavelet Transform with Enhanced Trigonometric Features (DTCWT-ETF) for efficient feature extraction. DTCWT-ETF captures both spatial and frequency characteristics, allowing the model to distinguish between different tumor types more effectively. In the classification stage, the framework introduces the Adaptive Hierarchical Optimized Horse Herd BiLSTM Fusion Network (AHOHH-BiLSTM). This multi-grade classification model is designed with a comprehensive architecture, including distinct layers that enhance the learning process and adaptively refine parameters. The purpose of this study is to improve the precision of distinguishing different grades of tumors in MRI images. To evaluate the proposed MGHCN framework, a set of evaluation metrics is incorporated which includes precision, recall, and the F1-score. The structure employs BraTS Challenge 2021, Br35H, and BraTS Challenge 2023 datasets, a significant combination that ensures comprehensive training and evaluation. The MGHCN framework aims to enhance brain tumor classification in MRI images by utilizing these datasets along with a comprehensive set of evaluation metrics, providing a more thorough and sophisticated understanding of its capabilities and performance.

Comparison of contrast-enhanced ultrasonography and MRI results obtained by expert and novice radiologists indicating short-term response after transarterial chemoembolisation for hepatocellular carcinoma

Objective: To investigate the differences in the clinicopathological and magnetic resonance imaging (MRI) imaging features between ductal carcinoma in situ (DCIS) and ductal carcinoma in situ with microinfiltration (DCIS-MI) of the breast, and to clarify the risk factors for the development of DCIS-MI. Methods: Forty-four patients diagnosed with DCIS and 21 patients diagnosed with DCIS-MI by postoperative pathology at Guangdong Maternal and Child Health Hospital from November 2017 to November 2022 were included, and the clinicopathological and preoperative breast MRI data of these patients were retrospectively collected. The patients' MRI images were categorized and diagnosed with reference to the Breast Imaging Reporting and Data System (BI-RADS) criteria. The χ² test or Fisher exact probability method was used to compare the differences in the clinicopathological and MRI imaging characteristics between the two groups of patients, and generalized linear model analysis was used to clarify the influencing factors of DCIS-MI. Results: The differences in the histologic grading, estrogen receptor (ER) expression, progesterone receptor (PR) expression, human epidermal growth factor receptor 2 (HER-2) expression, Ki-67, and molecular typing between patients in the DCIS and DCIS-MI groups were statistically significant (all P＜0.05). The results of generalized linear model analysis showed that Ki-67 expression and specific molecular typing (Luminal B and triple-negative types) were significantly associated with the risk of developing DCIS-MI (P＜0.05). Breast fibroglandular tissue density, lesion type, background parenchymal enhancement, type of time-intensity curves (TICs), distribution of non-mass enhancement, non-mass enhancement internal enhancement characteristics, mass morphology, mass boundary, mass enhancement mode, and other MRI imaging features were not statistically significant (all P＞0.05).The MRI diagnostic accuracy of the DCIS group and the DCIS-MI group was 77.3% (34/44) and 95.2% (20/21), respectively, and the difference in the MRI BI-RADS classification of the patients in the two groups was not statistically significant (P=0.227). Conclusions: There was no significant difference in the breast MRI imaging characteristics between patients in the DCIS and DCIS-MI groups. Patients in the DCIS-MI group were more likely to present with high histologic grades, negative ER, negative PR, positive HER-2, high Ki-67 expression, HER-2 overexpression, and triple-negative phenotypes. The association between Ki-67 expression and specific molecular typing (Luminal B and triple-negative phenotypes) and the risk of developing DCIS-MI risk were correlated.