CT and MRI Imaging Characteristics of Salivary Ductal Carcinoma

Objective:To investigate the CT and MRI features of salivary ductal carcinoma(SDC).Method:The imaging,clinical and pathological data of 32 patients with SDC confirmed by histomathology and operation were retrospectively analyzed.The location,size,shape,boundary,relationship with surrounding tissues,density,signal,enhancement mode,calcification,cystic degeneration and metastasis were observed.Result:Of the 32 patients with SDC,31 cases were isolated,17 were located in the parotid gland,8 in the submandibular gland,2 in the sinuses,2 in the orbit,1 in the part of the eye,and 1 in the sublingual gland.One case had multiple lesions located in the parotid gland.The maximum diameter of tumor was 1.5-7.2cm,and the median diameter was 3.0cm.The tumor showed diffuse growth in 11 cases and focal growth in 21 cases.The boundary was clear in 24 cases and unclear in 8 cases.The lesion may invade parapharyngeal space,soft palate,facial nerve,auditory nerve,skin,surrounding muscle and bone;There were 15 cases(47%)with lymph node metastasis and 1 case with lung metastasis.MRI showed that the solid part of the tumor was dominated by isointensity and low intensity on T1 WI,mixed high intensity on T2 WI,low intensity on T1 WI and high intensity on T2 WI.CT showed uneven tumor density,with equal or low density in 15 cases,high density in 4 cases,and calcification in 7 cases.Contrast-enhanced scan showed moderate to significant enhancement of the solid part.Conclusion:SDC is mostly single,prone to cystic necrosis and calcification,with strong aggressiveness and frequent lymph node metastasis.Understanding the imaging findings of SDC is helpful to improve the accuracy of preoperative diagnosis.

Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.

Clinical study on improving the diagnostic accuracy of adult elbow joint cartilage injury by multisequence magnetic resonance imaging

BACKGROUND Due to frequent and high-risk sports activities,the elbow joint is susceptible to injury,especially to cartilage tissue,which can cause pain,limited movement and even loss of joint function.AIM To evaluate magnetic resonance imaging(MRI)multisequence imaging for improving the diagnostic accuracy of adult elbow cartilage injury.METHODS A total of 60 patients diagnosed with elbow cartilage injury in our hospital from January 2020 to December 2021 were enrolled in this retrospective study.We analyzed the accuracy of conventional MRI sequences(T1-weighted imaging,T2-weighted imaging,proton density weighted imaging,and T2 star weighted image)and Three-Dimensional Coronary Imaging by Spiral Scanning(3D-CISS)in the diagnosis of elbow cartilage injury.Arthroscopy was used as the gold standard to evaluate the diagnostic effect of single and combination sequences in different injury degrees and the consistency with arthroscopy.RESULTS The diagnostic accuracy of 3D-CISS sequence was 89.34%±4.98%,the sensitivity was 90%,and the specificity was 88.33%,which showed the best performance among all sequences(P<0.05).The combined application of the whole sequence had the highest accuracy in all sequence combinations,the accuracy of mild injury was 91.30%,the accuracy of moderate injury was 96.15%,and the accuracy of severe injury was 93.33%(P<0.05).Compared with arthroscopy,the combination of all MRI sequences had the highest consistency of 91.67%,and the kappa value reached 0.890(P<0.001).CONCLUSION Combination of 3D-CISS and each sequence had significant advantages in improving MRI diagnostic accuracy of elbow cartilage injuries in adults.Multisequence MRI is recommended to ensure the best diagnosis and treatment.

Magnetic resonance imaging techniques for lithium-ion batteries:Principles and applications

Operando monitoring of internal and local electrochemical processes within lithium-ion batteries(LIBs)is crucial,necessitating a range of non-invasive,real-time imaging characterization techniques including nuclear magnetic resonance(NMR)techniques.This review provides a comprehensive overview of the recent applications and advancements of non-invasive magnetic resonance imaging(MRI)techniques in LIBs.It initially introduces the principles and hardware of MRI,followed by a detailed summary and comparison of MRI techniques used for characterizing liquid/solid electrolytes,electrodes and commercial batteries.This encompasses the determination of electrolytes'transport properties,acquisition of ion distribution profile,and diagnosis of battery defects.By focusing on experimental parameters and optimization strategies,our goal is to explore MRI methods suitable to a variety of research subjects,aiming to enhance imaging quality across diverse scenarios and offer critical physical/chemical insights into the ongoing operation processes of LIBs.

Application of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)combined with magnetic resonance spectroscopy(MRS)in prostate cancer diagnosis

Objective The aim of the study was to investigate the application of dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)combined with magnetic resonance spectroscopy(MRS)in prostate cancer diagnosis.Methods In the outpatient department of our hospital(Sichuan Cancer Hospital,Chengdu,China),60 patients diagnosed with prostate disease were selected randomly and included in a prostate cancer group,60 patients with benign prostatic hyperplasia were included in a proliferation group,and 60 healthy subjects were included in a control group,from January 2013 to January 2017.Using Siemens Avanto 1.5 T high-field superconducting MRI for DCE-MRI and MRS scans,after the MRS scan was completed,we used the workstation spectroscopy tab spectral analysis,and eventually obtained the crest lines of the prostate metabolites choline(Cho),creatine(Cr),citrate(Cit),and the values of Cho/Cit,and(Cho+Cr)/Cit.Results Participants who had undergone 21-s,1-min,and 2-min dynamic contrast-enhanced MR revealed significant variations among the three groups.The spectral analysis of the three groups revealed a significant variation as well.DCE-MRI and MRS combined had a sensitivity of 89.67%,specificity of 95.78%,and accuracy of 94.34%.Conclusion DCE-MRI combined with MRS is of great value in the diagnosis of prostate cancer.

Generalized Nonconvex Low-Rank Algorithm for Magnetic Resonance Imaging (MRI) Reconstruction

In recent years,utilizing the low-rank prior information to construct a signal from a small amount of measures has attracted much attention.In this paper,a generalized nonconvex low-rank(GNLR) algorithm for magnetic resonance imaging(MRI)reconstruction is proposed,which reconstructs the image from highly under-sampled k-space data.In the algorithm,the nonconvex surrogate function replacing the conventional nuclear norm is utilized to enhance the low-rank property inherent in the reconstructed image.An alternative direction multiplier method(ADMM) is applied to solving the resulting non-convex model.Extensive experimental results have demonstrated that the proposed method can consistently recover MRIs efficiently,and outperforms the current state-of-the-art approaches in terms of higher peak signal-to-noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.

The parallel-plate resonator:An RF probe for MR and MRI studies over a wide frequency range

We explore the use of the parallel-plate resonator for the study of thin cuboid samples over a wide range of magnetic resonance frequencies.The parallel-plate resonator functions at frequencies from tens to hundreds of MHz.Seven parallel-plate resonators are presented and discussed in a frequency range from 8 to 500 MHz.Magnetic resonance experiments were performed on both horizontal and vertical bore magnet systems with lithium and hydrogen nuclei.Parallel-plate radiofrequency(RF)probes are easy to build and easy to optimize.Experiments and simulations showed good sensitivity of the parallel-plate RF probe geometry with a small decrease in sensitivity at higher frequencies.

Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

The automatic localization of the left ventricle(LV)in short-axis magnetic resonance(MR)images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest(ROI).The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration.Nevertheless,this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices.Thus,this study proposed a region-based convolutional network(Faster R-CNN)for the LV localization from short-axis cardiac MRI images using a region proposal network(RPN)integrated with deep feature classification and regression.Themodel was trained using images with corresponding bounding boxes(labels)around the LV,and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters.The experimental findings showthat the proposed modelwas adequate,with accuracy,precision,recall,and F1 score values of 0.91,0.94,0.95,and 0.95,respectively.This model also allows the cropping of the detected area of LV,which is vital in reducing the computational cost and time during segmentation and classification procedures.Therefore,itwould be an ideal model and clinically applicable for diagnosing cardiac diseases.

Value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology

Objective To evaluate the value of MRI diffusion weighted imaging in localization of prostate cancer with whole-mount step section pathology. Methods We treated 36 patients using laparoscopic radical prostatectomy from Oct. 2009 to Jun. 2010. Patients who did not have an MRL /DWI examination or a surgical history of pros-

Reduced Imaging Time and Improved Image Quality of 3D Isotropic T2-Weighted Magnetic Resonance Imaging with Compressed Sensing for the Female Pelvis

This study is to compare three-dimensional(3D)isotropic T2-weighted magnetic resonance imaging(MRI)with compressed sensing-sampling perfection with application optimized contrast(CS-SPACE)and the conventional image(3D-SPACE)sequence in terms of image quality,estimated signal-to-noise ratio(SNR),relative contrast-to-noise ratio(CNR),and the lesions’conspicuous of the female pelvis.Thirty-six females(age:51,28-73)with cervical carcinoma(n=20),rectal carcinoma(n=7),or uterine fibroid(n=9)were included.Patients underwent magnetic resonance(MR)imaging at a 3T scanner with the sequences of 3D-SPACE,CS-SPACE,and twodimensional(2D)T2-weighted turbo-spin echo(TSE).Quantitative analyses of estimated SNR and relative CNR between tumors and other tissues,image quality,and tissue conspicuity were performed.Two radiologists assessed the difference in diagnostic findings for carcinoma.Quantitative values and qualitative scores were analyzed,respectively.The estimated SNR and the relative CNR of tumor-to-muscle obturator internus,tumor-to-myometrium,and myometrium-to-muscle obturator internus was comparable between 3D-SPACE and CS-SPACE.The overall image quality and the conspicuity of the lesion scores of the CS-SPACE were higher than that of the 3D-SPACE(P<0.01).The CS-SPACE sequence offers shorter scan time,fewer artifacts,and comparable SNR and CNR to conventional 3D-SPACE,and has the potential to improve the performance of T2-weighted images.

Hypersensitivity Reaction Caused by Intravenous Gadolinium-based MRI Contrast Agents

Objective To present a rare case of skin allergic reaction to gadobutrol,a magnetic resonance imaging(MRI)contrast agent,in a 37-year-old man.Methods The adverse reactions of gadobutrol were analyzed combined with the instructions and related literatures.Results and Conclusion The presence of this patient is consistent with the adverse reactions in the instructions of gadobutrol.The incidence of ADR in gadobutrol is considered to be low,although sometimes patients report a hypersensitivity reaction when undergoing MRI.There are only a few cases of immediate adverse reactions to gadobutrol.However,we should improve the ability of medical staff to use drugs safely and take preventive measures.

Intelligent Machine Learning Based Brain Tumor Segmentation through Multi-Layer Hybrid U-Net with CNN Feature Integration

Brain tumors are a pressing public health concern, characterized by their high mortality and morbidity rates.Nevertheless, the manual segmentation of brain tumors remains a laborious and error-prone task, necessitatingthe development of more precise and efficient methodologies. To address this formidable challenge, we proposean advanced approach for segmenting brain tumorMagnetic Resonance Imaging (MRI) images that harnesses theformidable capabilities of deep learning and convolutional neural networks (CNNs). While CNN-based methodshave displayed promise in the realm of brain tumor segmentation, the intricate nature of these tumors, markedby irregular shapes, varying sizes, uneven distribution, and limited available data, poses substantial obstacles toachieving accurate semantic segmentation. In our study, we introduce a pioneering Hybrid U-Net framework thatseamlessly integrates the U-Net and CNN architectures to surmount these challenges. Our proposed approachencompasses preprocessing steps that enhance image visualization, a customized layered U-Net model tailoredfor precise segmentation, and the inclusion of dropout layers to mitigate overfitting during the training process.Additionally, we leverage the CNN mechanism to exploit contextual information within brain tumorMRI images,resulting in a substantial enhancement in segmentation accuracy.Our experimental results attest to the exceptionalperformance of our framework, with accuracy rates surpassing 97% across diverse datasets, showcasing therobustness and effectiveness of our approach. Furthermore, we conduct a comprehensive assessment of ourmethod’s capabilities by evaluating various performance measures, including the sensitivity, Jaccard-index, andspecificity. Our proposed model achieved 99% accuracy. The implications of our findings are profound. Theproposed Hybrid U-Net model emerges as a highly promising diagnostic tool, poised to revolutionize brain tumorimage segmentation for radiologists and clinicians.

Targeted prostate biopsy： value of multiparametric magnetic resonance imaging in detection of localized cancer

Prostate cancer is the second most common cancer in men, with 1.1 million new cases worldwide reported by the World Health Organization in one recent year. Transrectal ultrasound （TRUS）-guided biopsy has been used for the diagnosis of prostate cancer for over 2 decades, but the technique is usually blind to cancer location. Moreover, the false negative rate of TRUS biopsy has been reported to be as high as 47%. Multiparametric magnetic resonance imaging （mp-MRI） includes T1- and T2-weighted imaging as well as dynamic contrast-enhanced （DCE） and diffusion-weighted imaging （DWI）. mp-MRI is a major advance in the imaging of prostate cancer, enabling targeted biopsy of suspicious lesions. Evolving targeted biopsy techniquesmincluding direct in-bore biopsy, cognitive fusion and software-based MRI-ultrasound （MRI-US） fusion--have led to a several-fold improvement in cancer detection compared to the earlier method. Importantly, the detection of clinically significant cancers has been greatly facilitated by targeting, compared to systematic biopsy alone. Targeted biopsy via MRI-US fusion may dramatically alter the way prostate cancer is diagnosed and managed.

Therapeutic imaging window of cerebral infarction revealed by multisequence magnetic resonance imaging An animal and clinical study

In this study, we established a Wistar rat model of right middle cerebral artery occlusion and observed pathological imaging changes （T2-weighted imaging [T2WI], T2FLAIR, and diffusion-weighted imaging [DWI]） following cerebral infarction. The pathological changes were divided into three phases： early cerebral infarction, middle cerebral infarction, and late cerebral infarction. In the early cerebral infarction phase （less than 2 hours post-infarction）, there was evidence of intracellular edema, which improved after reperfusion. This improvement was defined as the ischemic penumbra. In this phase, a high DWI signal and a low apparent diffusion coefficient were observed in the right basal ganglia region. By contrast, there were no abnormal T2WI and T2FLAIR signals. For the middle cerebral infarction phase （2-4 hours post-infarction）, a mixed edema was observed. After reperfusion, there was a mild improvement in cell edema, while the angioedema became more serious. A high DWI signal and a low apparent diffusion coefficient signal were observed, and some rats showed high T2WI and T2FLAIR signals. For the late cerebral infarction phase （4-6 hours post-infarction）, significant angioedema was visible in the infarction site. After reperfusion, there was a significant increase in angioedema, while there was evidence of hemorrhage and necrosis. A mixed signal was observed on DWI, while a high apparent diffusion coefficient signal, a high T2WI signal, and a high T2FLAIR signal were also observed. All 86 cerebral infarction patients were subjected to T2WI, T2FLAIR, and DWI. MRI results of clinic data similar to the early infarction phase of animal experiments were found in 51 patients, for which 10 patients （10/51） had an onset time greater than 6 hours. A total of 35 patients had MRI results similar to the middle and late infarction phase of animal experiments, of which eight patients （8/35） had an onset time less than 6 hours. These data suggest that defining the ＂therapeutic time window＂ as the time 6 hours after infarction may not be suitable for all patients. Integrated application of MRI sequences including T2WI, T2FLAIR, DW-MRI, and apparent diffusion coefficient mapping should be used to examine the ischemic penumbra, which may provide valuable information for identifying the ＂therapeutic time window＂.

A Machine Learning Approach for MRI Brain Tumor Classification

A new method for the denoising,extraction and tumor detection on MRI images is presented in this paper.MRI images help physicians study and diagnose diseases or tumors present in the brain.This work is focused towards helping the radiologist and physician to have a second opinion on the diagnosis.The ambiguity of Magnetic Resonance(MR)image features is solved in a simpler manner.The MRI image acquired from the machine is subjected to analysis in the work.The real-time data is used for the analysis.Basic preprocessing is performed using various filters for noise removal.The de-noised image is segmented,and the feature extractions are performed.Features are extracted using the wavelet transform.When compared to other methods,the wavelet transform is more suitable for MRI image feature extraction.The features are given to the classifier which uses binary tree support vectors for classification.The classification process is compared with conventional methods.

Magnetic resonance imaging for prostate cancer clinical application

As prostate cancer is a biologically heterogeneous disease for which a variety of treatment options are available, the major objective of prostate cancer imaging is to achieve more precise disease characterization. In clinical practice, magnetic resonance imaging （MRI） is one of the imaging tools for the evaluation of prostate cancer, the fusion of MRI or dynamic contrast-enhanced MRI （DCE-MRI） with magnetic resonance spectroscopic imaging （MRSI） is improving the evaluation of cancer locafon, size, and extent, while providing an indication of tumor aggressiveness. This review summarizes the role of MRI in the application of prostate cancer and describes molecular MRI techniques （including MRSI and DCE-MRI） for aiding prostate cancer management.

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

Value of ^(18)F-FDG PET/CT and MRI in diagnosing primary endometrial small cell carcinoma

Primary small cell carcinoma（SCC） is a group of aggressive neoplasms that mainly arise from the lung and digestive tract. Endometrial small cell carcinoma（ESCC） is extremely rare. To our knowledge, less than 90 cases have been reported, and most of these reports were dedicated to describing the clinicopathologic or immunochemical features of ESCC. Herein, we present a new case of ESCC involving a 51-year-old woman and mainly focus on the magnetic resonance imaging（MRI） and positron emission tomography/computed tomography（PET/CT） findings. MRI showed that the uterus was significantly enlarged（11.6 cm × 11.1 cm × 14.4 cm）, and a giant irregular mass（7.5 cm × 8.4 cm × 8.5 cm） was observed in the uterine cavity. The lesion demonstrated an extremely low apparent diffusion coefficient（ADC） value [（0.553±0.088）×10^–3 mm^2/s] and a high FDG uptake value（22.7）. Multiple metastatic lymph nodes（LNs） were identified at different positions, with diameters ranging from 0.3 to 2.8 cm and a maximum standardized uptake value（SUV max） ranging from 6.9 to 19.3.

Spinal cord injury in patients with systemic lupus Erythematosus Clinical manifestations, imaging characteristics and treatment

BACKGROUND： There are fewer reports on systemic lupus erythematosus （SLE） related myelitis, and definite and uniform therapeutic program is not available. OBJECTIVE： To observe the clinical manifestations, imaging characteristics, results of laboratory examination and treatment of SLE. DESIGN： A retrospective case analysis. SETTING： Department of Neurology, the Second Affiliated Hospital of Sun Yat-sen University. PARTICIPANTS： Totally 1 052 SLE inpatients were selected from the Second Affiliated Hospital of Sun Yat-sen University from January 1995 to May 2005, and they all accorded with the diagnostic standards for SLE set by American Rheumatism Association in 1982. 124 of them were diagnosed to have damage of central nervous system. Inclusive criteria： Patients with one of the focal physical signs, including mental and behavior disorders, headache, seizure and involvement of nervous system. Exclusive criteria： Patients with hypertensive encephalopathy, damage of nervous system due to uremia and infection of central nervous system. Spinal cord lesion occurred in 15 female cases of 23 - 51 years old. Informed consents were obtained from all the participants. METHODS： The physical signs, laboratory examinations, therapeutic program and prognosis were recorded in the 15 patients with symptoms of spinal cord lesions. All the patients underwent MRI scan of brain or lesioned segment of spinal cord, and 8 cases of them underwent lumbar puncture to determine intracranial pressure, routine and biochemical examinations were cerebrospinal fluid were performed. The disease activity of SLE in systems beyond central nervous system was evaluated with modified lupus activity criteria count （LACC）. MAIN OUTCOME MEASURES：① Incidence of SLE related myelitis, attack age distribution and its association with the activity of SLE; ② Comparisons of the clinical characteristics, cranial and spinal cord MRI manifestations, different therapeutic program and prognosis. RESULTS： All the 15 SLE patients were involved in the analysis of results. ① The incidence of SLE related myelitis was low （1%, 15/1 052）. ②SLE related myelitis occurred mostly when the SLE symptoms were active, and only a few occurred at the stable period. ③ Among the SLE patients, MRI displayed abnormal changes in 71% （10/14）, the typical changes appeared abnormal signals at corresponding spinal segments, manifested as prolonged T1 and T2 signals, thickened spinal segments. Lumbar segments were mostly involved. ④ Of the 9 patients treated with hormone impact, 7 cases （78%） had obvious improvements, and the effects were better in those treated with immunosuppressor combined with intravenous immunoglobulin of large dosage. CONCLUSION：① Myelitis is a rare complication of SLE.② MRI serves as a valuable supplementary approach in the diagnosis of SLE related myelitis without specificity. ③ Steroid pulse combined with immunosuppressor and intravenous immunoglobulin of large dosage is effective in the treatment.

3D printing of patient-specific implants for osteochondral defects: workflow for an MRI-guided zonal design

Magnetic resonance imaging(MRI)is a common clinical practice to visualize defects and to distinguish different tissue types and pathologies in the human body.So far,MRI data have not been used to model and generate a patient-specific design of multilayered tissue substitutes in the case of interfacial defects.For orthopedic cases that require highly individual surgical treatment,implant fabrication by additive manufacturing holds great potential.Extrusion-based techniques like 3D plot-ting allow the spatially defined application of several materials,as well as implementation of bioprinting strategies.With the example of a typical multi-zonal osteochondral defect in an osteochondritis dissecans(OCD)patient,this study aimed to close the technological gap between MRI analysis and the additive manufacturing process of an implant based on dif-ferent biomaterial inks.A workflow was developed which covers the processing steps of MRI-based defect identification,segmentation,modeling,implant design adjustment,and implant generation.A model implant was fabricated based on two biomaterial inks with clinically relevant properties that would allow for bioprinting,the direct embedding of a patient’s own cells in the printing process.As demonstrated by the geometric compatibility of the designed and fabricated model implant in a stereolithography(SLA)model of lesioned femoral condyles,a novel versatile CAD/CAM workflow was successfully established that opens up new perspectives for the treatment of multi-zonal(osteochondral)defects.