Enhanced preoperative planning in congenital polydactyly: superior assessment of MCP/MTP joint angular deformity with 3D-FS-FSPGR MRI compared to conventional radiography.

The objective of this research was to determine whether a fast 3-dimensional (3-D) gradient echo magnetic resonance imaging (MRI) sequence could be used to acquire images suitable for image guided surgery of the spine. The main difficulty with MRI is that inhomogeneities in the static magnetic field lead to geometric distortions in the images. We used a very fast 3-D MRI sequence with a wide bandwidth and short echo time (TE) to minimize these distortions. Fiducial markers that could be localized in MRI and computed tomography (CT) images and in physical space were attached to a phantom in order to assess the accuracy of a landmark based registration method. The effect of varying the MRI parameters on image contrast was also investigated. The results demonstrate that the registration can be undertaken with an accuracy of 0.4 mm using the 3-D MRI. This is comparable to the accuracy of 0.3 mm obtained with CT and is a significant improvement over the accuracy of the 2-D MRI techniques (> 1.0 mm). In vivo images demonstrating good contrast between the spine and surrounding soft tissues such as fat, intervertebral disks, and cerebrospinal fluid were obtained. The MRI acquired using the sequence described in this article shows promise for use in computer assisted surgery of the spine.

The objective of this research was to determine whether a fast 3-dimensional (3-D) gradient echo magnetic resonance imaging (MRI) sequence could be used to acquire images suitable for image guided surgery of the spine. The main difficulty with MRI is that inhomogeneities in the static magnetic field lead to geometric distortions in the images. We used a very fast 3-D MRI sequence with a wide bandwidth and short echo time (TE) to minimize these distortions. Fiducial markers that could be localized in MRI and computed tomography (CT) images and in physical space were attached to a phantom in order to assess the accuracy of a landmark based registration method. The effect of varying the MRI parameters on image contrast was also investigated. The results demonstrate that the registration can be undertaken with an accuracy of 0.4 mm using the 3-D MRI. This is comparable to the accuracy of 0.3 mm obtained with CT and is a significant improvement over the accuracy of the 2-D MRI techniques (> 1.0 mm). In vivo images demonstrating good contrast between the spine and surrounding soft tissues such as fat, intervertebral disks, and cerebrospinal fluid were obtained. The MRI acquired using the sequence described in this article shows promise for use in computer assisted surgery of the spine.

The complexity of Magnetic Resonance Imaging (MRI) sequences requires expert knowledge about the underlying contrast mechanisms to select from the wide range of available applications and protocols. Automation of this process using machine learning (ML) can support the radiologists and MR technicians by complementing their experience and finding the optimal MRI sequence and protocol for certain applications. We define domain-specific languages (DSL) both for describing MRI sequences and for formulating clinical demands for sequence optimization. By using various abstraction levels, we allow different key users exact definitions of MRI sequences and make them more accessible to ML. We use a vendor-independent MRI framework (gammaSTAR) to build sequences that are formulated by the DSL and export them using the generic file format introduced by the Pulseq framework, making it possible to simulate phantom data using the open-source MR simulation framework JEMRIS to build a training database that relates input MRI sequences to output sets of metrics. Utilizing ML techniques, we learn this correspondence to allow efficient optimization of MRI sequences meeting the clinical demands formulated as a starting point. ML methods are capable of capturing the relation of input and simulated output parameters. Evolutionary algorithms show promising results in finding optimal MRI sequences with regards to the training data. Simulated and acquired MRI data show high correspondence to the initial set of requirements. This work has the potential to offer optimal solutions for different clinical scenarios, potentially reducing exam times by preventing suboptimal MRI protocol settings. Future work needs to cover additional DSL layers of higher flexibility as well as an optimization of the underlying MRI simulation process together with an extension of the optimization method.

Tumor Size on Abdominal MRI Versus Pathologic Specimen in Resected Pancreatic Adenocarcinoma: Implications for Radiation Therapy Planning

Evaluation of technical factors affecting the quantification of trabecular bone structure using magnetic resonance imaging

Objectives:The decision to perform anterior cruciate ligament (ACL) reconstruction in skeletally immature patients carries a risk of growth disturbance due to iatrogenic physeal injury. Multiple physeal-sparing techniques have been described but none, to our knowledge combine the benefits of an anatomic reconstruction and socket fixation, without violation of either the femoral or tibial physis at any stage of the procedure. The purpose of this study was to compare the incidence and calculate the area of post-operative physeal disturbances, using a physeal-sensitive magnetic resonance imaging (MRI) sequence*, between all-epiphyseal (AE) and partial transphyseal (PTP) ACL reconstructions in a cohort of skeletally-immature patients.Methods:Twenty-one skeletally immature patients with a mean chronologic age of 12.7 years (range 10 to 16) undergoing all-inside ACL reconstruction were prospectively followed. Fourteen patients had an all-epiphyseal (AE) ACL reconstruction and 7 patients had a partial transphyseal (PTP) ACL reconstruction, which spared the femoral physis but crossed the tibial physis. Hamstring autograft was used in all cases. At a mean of 11.6 months follow-up, all patients were assessed for focal physeal disturbance and graft survival using a three-dimensional (3D) fat suppressed spoiled gradient-recalled echo (SPGR) MRI sequence. Angular deformity and leg length discrepancy were evaluated on full-length standing radiographs. The International Knee Documentation Committee (IKDC) subjective score and Lysholm knee score were also documented.Results:The tibial physis was violated in 13/14 patients in the AE group and all patients in the PTP group. The mean area of post-operative tibial physeal disturbance (± SD) was 42.4 ± 38.6 mm2 (mean 1.7% of total physeal area) in the AE group compared to 216.7 ± 129.1 mm2 (mean 7.3% of total physeal area) in the PTP group (p = 0.003). The femoral physis was violated in one case in both groups resulting in a mean physeal disturbance of 1.5% of the total distal femoral physeal area. No cases of fracture, articular surface violation or avascular necrosis were noted on MRI in either group. Short-term graft survival was 100% amongst the entire cohort. There were no cases of angular deformity in either group with a mean side-side difference in the lateral distal femoral angle of 1.11° ± 1.02° in the AE group and 0.72° ± 0.65° in the PTP group (p = 0.23). No significant leg-length discrepancies were measured in either group. The mean IKDC and Lysholm scores (± SD) were 93.3 ± 5.9 and 97.8 ± 3.8 respectively in the AE group and 87.7 ± 3.5 and 96 ± 5.2 respectively in the PTP group.Conclusion:All-epiphyseal ACL reconstruction caused significantly less focal physeal disturbance than PTP ACL reconstruction, as determined by a 3D physeal-sensitive MRI sequence. Neither technique however resulted in angular deformity or leg-length discrepancy at early follow-up. Both all-inside ACL reconstruction techniques used in this study are safe and effective at early follow-up in skeletally immature patients. Further longitudinal study of this cohort is required to determine any potential advantages of a purely physeal-sparing technique.

Regarding the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the isocitrate dehydrogenase (IDH) mutation status is one of the most important factors for CNS tumor classification. The aim of our study is to analyze which of the commonly used magnetic resonance imaging (MRI) sequences is best suited to obtain this information non-invasively using radiomics-based machine learning models. We developed machine learning models based on different MRI sequences and determined which of the MRI sequences analyzed yields the highest discriminatory power in predicting the IDH mutation status. In our retrospective IRB-approved study, we used the MRI images of 106 patients with histologically confirmed gliomas. The MRI images were acquired using the T1 sequence with and without administration of a contrast agent, the T2 sequence, and the Fluid-Attenuated Inversion Recovery (FLAIR) sequence. To objectively compare performance in predicting the IDH mutation status as a function of the MRI sequence used, we included only patients in our study cohort for whom MRI images of all four sequences were available. Seventy-one of the patients had an IDH mutation, and the remaining 35 patients did not have an IDH mutation (IDH wild-type). For each of the four MRI sequences used, 107 radiomic features were extracted from the corresponding MRI images by hand-delineated regions of interest. Data partitioning into training data and independent test data was repeated 100 times to avoid random effects associated with the data partitioning. Feature preselection and subsequent model development were performed using Random Forest, Lasso regression, LDA, and Naïve Bayes. The performance of all models was determined with independent test data. Among the different approaches we examined, the T1-weighted contrast-enhanced sequence was found to be the most suitable for predicting IDH mutations status using radiomics-based machine learning models. Using contrast-enhanced T1-weighted MRI images, our seven-feature model developed with Lasso regression achieved a mean area under the curve (AUC) of 0.846, a mean accuracy of 0.792, a mean sensitivity of 0.847, and a mean specificity of 0.681. The administration of contrast agents resulted in a significant increase in the achieved discriminatory power. Our analyses show that for the prediction of the IDH mutation status using radiomics-based machine learning models, among the MRI images acquired with the commonly used MRI sequences, the contrast-enhanced T1-weighted images are the most suitable.

Radiation-induced brain damage was evaluated using sequential magnetic resonance (MR) imaging in monkeys more than 1 year after either brachytherapy or combined brachytherapy and external beam radiotherapy (EBRT). MR images were obtained 1 week, 1 month, 3 months, and subsequently every 3 months after brachytherapy, and the volume of the lesions was measured. In all four monkeys receiving only brachytherapy and three of the four animals receiving combined brachytherapy and EBRT, MR imaging revealed only transient extensive edema 1 week after treatment and ring enhancement, which was maximal 3 months after treatment, surrounding the implantation site. In one of the four animals undergoing combined brachytherapy and EBRT, MR images obtained 9 months after treatment showed an irregularly extending enhanced lesion with edema. MR images obtained 15 months after brachytherapy in this monkey showed the lesion extended into the contralateral hemisphere through the corpus callosum. Necropsy revealed severe radiation necrosis. This animal developed a very similar MR imaging presentation to that often encountered in human brains after combined brachytherapy and EBRT.

This study proposes an accurate vessel segmentation method using an elliptic-model-guided dynamic programming algorithm to find the shortest path in a two-dimensional matrix. The elliptic model increases the algorithm’s resistance to noise around the vessel boundaries. To test the system reliability and accuracy, we use phantom images with added uniformly distributed noise to get different signal-to-noise ratios. In addition, the optimal parameter values are determined via a phantom study. We further apply this method to detect the boundaries of the common carotid artery (CCA) in real magnetic resonance imaging (MRI) sequences without contrast agent injection. Manual tracings of the CCA boundaries are performed by well-trained experts as the gold standard. Comparisons between the manual tracings and automated results are made on 8 MRI sequences (400 total images). The average unsigned error rate is 3.6 % (standard deviation = 2.4 %). The results demonstrate that the proposed method is qualitatively better than traditional dynamic programming for vessel boundary detection on MRI sequences without contrast agent injection.

Numerous types of Magnetic Resonance Imaging (MRI) sequence have been utilized for in vivo tibiofemoral contact area study has led inconsistency and disproportionate results. Thus, this study aim is to assess intensity differences of MRI images in three different MRI sequences. Ten healthy subjects with average age of 25.5 ± 4.76 consisted three males and seven females have no history of knee injuries participated in this study. Subjects were scanned through an Achieva 3.0T TX coupled with a SENSE spine coil 15. The selected MRI sequences were Turbo Spin Echo (TSE), Fast Field Echo (FFE) and Steady State Free Precision (SSFP). The results showed that the SSFP sequence consistently uncovered extra contact areas than the FFE and TSE sequences. Overall intensity value indicated that the TSE sequence intersected the highest intensity differences at important knee tissue components: trabecular-cortical and cartilage-synovial. The study demonstrated that the TSE sequence yields an accurate contact length detection which promoted a consistent tibiofemoral contact area for quantification. Also, this study suggested that the TSE sequence is a proper MRI sequence selection for in vivo tibofemoral contact area study.

Currently, minute structures, such as cervical nerve roots, can be viewed using magnetic resonance imaging (MRI) sequences; however, studies comparing multiple sequences in the same set of patients are rare. The aim of the study is to compare the diagnostic values of three 3.0-T MRI sequences used in the imaging of cervical nerve roots.This study included 2 phases. In the first phase (n = 45 patients), the most optimal MRI sequence was determined. In the second phase, this MRI sequence was compared with surgical results (n = 31 patients). The three-dimensional double-echo steady-state (3D-DESS), multi-echo data image combination (MEDIC), and 3D sampling perfection with application-optimized contrasts using different flip angle evolutions (3D-SPACE) sequences were performed to analyze the image quality. Furthermore, the most optimal MRI sequence was compared with surgical results to determine the agreement rate.The image quality scores of the 3 sequences were significantly different (P < .05). The score for 3D-DESS sequence was superior to that of MEDIC sequence, while the score for 3D-SPACE sequence was the worst. For visualization of compressed nerve roots, 3D-DESS sequence was superior to the other 2 sequences in terms of the total quality score and compressed nerve root score. Therefore, 3D-DESS sequence was used for MRI in 31 patients with cervical spondylosis in the second phase of this study. The diagnostic agreement rate was 93.5%.This study concluded that in patients with cervical radiculopathy, the 3D-DESS sequence is superior to the MEDIC and 3D-SPACE sequences and shows a high agreement rate with the surgical diagnosis.

To prospectively evaluate the correlation between a three-dimensional (3D) delayed enhancement magnetic resonance (MR) imaging sequence and a two-dimensional (2D) delayed enhancement MR imaging sequence for noninvasive assessment of myocardial viability in pigs and patients. The pig and patient studies were approved by the responsible authorities, and patients gave written informed consent. MR imaging was performed by using a rapid 3D inversion-recovery balanced steady-state free precession sequence and a 2D segmented inversion-recovery fast low-angle shot sequence as the reference standard. Fourteen pigs with reperfused (n=7) or nonreperfused (n=7) myocardial infarction and 17 patients (13 men, four women; mean age, 64.9 years+/-8.6 [standard deviation]) suspected of having myocardial infarction were included. Linear regression analysis and Bland-Altman analysis were used to compare the infarction volumes. In 10 of the 14 pigs the induction of myocardial infarction was successful. In these pigs, altogether 81 segments with myocardial infarction were demonstrated by both MR sequences, and agreement between the two sequences for classification of transmural extent of myocardial infarction was 99.7%. The infarction volume determined by using 3D MR imaging (4.64 cm3+/-2.48) in the pigs highly correlated with that of 2D MR imaging (4.65 cm3+/-2.39, r=0.989, P<.001) and that of staining by using triphenyltetrazolium chloride (4.67 cm3+/-2.44, r=0.996, P<.001). Thirteen of the 17 patients examined showed myocardial infarction in 34 myocardial segments with both sequences, and agreement between the two sequences for classification of transmural extent of myocardial infarction was 98.6%. In the patients, the infarction volume determined with both sequences highly correlated (9.71 cm3+/-7.47 for the 3D sequence vs 10.01 cm3+/-8.04 for the 2D sequence, r=0.982, P<.001). The breath-hold time necessary for the 3D MR imaging (21.0+/-2.3 seconds) was significantly shorter than that for 2D MR imaging (188.3+/-20.2 seconds, P<.001). Myocardial infarction volumes obtained with the 3D MR imaging sequence are highly correlated and in good agreement with volumes obtained with the 2D MR imaging standard approach and reduced the acquisition time by a factor of nine.

To evaluate the diagnostic accuracy of common magnetic resonance (MR) imaging sequences for detection of small pulmonary nodules by using a chest phantom and porcine lungs containing simulated lesions. Fourteen porcine lungs containing 366 porcine myocardial tissue implants were inflated inside a phantom. Two-dimensional (2D) and three-dimensional (3D) gradient-echo (GRE), T2-weighted turbo spin-echo (SE), and T2-weighted single-shot SE train MR sequences were performed. Spiral computed tomography (CT) was performed for comparison. Blinded observers read the images and recorded the sizes and locations of visible nodules by consensus. The sensitivity of each imaging method for depicting single nodules of given sizes was calculated. Specificities, positive predictive values (PPVs), and negative predictive values (NPVs) for detection of one or more nodules of various sizes were calculated. Sensitivities of 3D GRE, 2D GRE, T2-weighted turbo SE, and T2-weighted single-shot SE train MR imaging and of CT were 0.50, 0.40, 0.12, 0.00, and 0.55, respectively, for detection of 1.4-mm nodules and 0.88, 0.84, 0.69, 0.06, and 0.96, respectively, for detection of 4.2-mm nodules. The 95% CIs for CT and GRE MR imaging overlapped, but those for turbo SE and single-shot SE train MR imaging differed significantly (P <.05). For detection of nodules larger than 5 mm, all examinations except single-shot SE train MR imaging yielded a specificity, PPV, and NPV of 1.00 each. For detection of nodules smaller than 5 mm, diagnostic accuracy of 3D GRE MR imaging was high: Specificity, PPV, and NPV all were approximately 0.90. Two-dimensional GRE MR imaging results were influenced by false-positive findings: Specificity was 0.64; PPV, 0.74; and NPV, 1.00. Common MR imaging sequences such as 3D GRE have high diagnostic accuracy in depicting small pulmonary nodules when artifacts from cardiac and respiratory motion are absent.

The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95% in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2∗ mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2∗ mapping sequence showed the best diagnosis results for different degrees of KOA injury.

INTRODUCTION: The purpose of this study was to estimate the cost effectiveness of ultrasonography and magnetic resonance imaging (MRI) as diagnostic modalities for suspected appendicitis in pregnancy. We examined the cost of ultrasound as the first-line modality with sequential MRI in the setting of a non-visualized appendix versus MRI alone. METHODS: A cost-effectiveness model was created using TreeAge to compare the sequential ultrasound and MRI versus MRI alone in a theoretical cohort of 100,000 women. Outcomes examined included perforation of the appendix, cesarean delivery, maternal death, fetal death, cost, and quality-adjusted life years (QALY). Probabilities, utilities, and costs were derived from the literature, and a cost-effectiveness threshold was set at $100,000/QALY. One-way sensitivity analyses were used to evaluate the robustness of our results. RESULTS: When comparing sequential ultrasound and MRI versus MRI alone for diagnosis of appendicitis in pregnancy, there were 3.5 fewer fetal deaths with sequential screening. There was no difference in rate of maternal death. There were higher QALYs associated with sequential screening, but MRI alone was cost-effective. With sensitivity analysis, sequential screen became cost-effective below $200 (cost assumption in our model was $604). CONCLUSION: The sequential screening strategy led to fewer adverse neonatal outcomes. While this screening modality was not cost-effective, the higher QALYs make this screening algorithm worth considering in the clinical setting, especially due to improving clinician skills and the low cost of bedside ultrasound. Sequential screening is recommended as first-line imaging, and further investigation is warranted to better understand how these imaging modalities can improve clinical outcomes.