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Background: Rectal cancer is the most prevalent gastrointestinal tumor. Early diagnosis, accurate staging as well as early treatment are the keys for improving the five-year survival rate. The objective of this research is to assess the effectiveness of diffusion-weighted MRI (DWI) in identifying lymph nodes and distinguishing between benign and metastatic nodes throughout the first stage of primary rectal cancer. Results: The study showed that mean ADC value was significantly higher in mucinous carcinoma (1.72 ± 0.36 × 10–3 mm2/sec) than that in non-mucinous carcinoma (0.981 ± 0.276 × 10–3 mm2/sec) with a cutoff value of (1.3 × 10–3) mm2/s which was the precise value to produce high sensitivity, specificity and accuracy of 93%, 94%, and 94%, respectively. ADC analysis showed either intermediate or low signal in 49 (70%) and high signal in 21 (30%) L.Ns. Mean ADC value showed a significant reduction in malignant L.Ns (1.01 ± 0.54 × 10–3 mm2/sec) compared to benign L.Ns (1.51 ± 0.51 × 10–3 mm2/sec), AUC of 0.674 (P = 0.008) and a cutoff value of 0.987 × 10–3 mm2/s with sensitivity, specificity and accuracy of 44.4%, 91.2% and 67.5%, respectively. The mean L.N /tumor ratio was 1.65 ± 0.73 in benign L.Ns and 1.06 ± 0.37 in malignant L.Ns. Conclusions: In rectal cancer, there was a significant difference between benign and malignant L.Ns regarding diffusion result, L.Ns size, shape, and margin. The study demonstrated the effectiveness of DWI in diagnosing lymph node metastasis in colorectal cancer; true diffusion restriction was significantly noted in malignant L.Ns compared to benign L.Ns. Mean ADC value showed a significant reduction in malignant L.Ns compared to benign L.Ns. L.N/tumor ratio showed a significant reduction in malignant L.Ns compared to benign L.Ns. [ABSTRACT FROM AUTHOR]

• Unexpected error is an important remaining issue in deep learning-based noise reduction. • The devised model has a restricted degree of freedom to reduce the chance of error. • Multiple pathways divided the function to balance the capability and the constraints. • The model can perform adequate denoising even under the restricted freedom. • The model utilized the characteristics of MRI to incorporate the unique loss function. In deep learning-based noise reduction, larger networks offer advanced and complex functionality by utilizing its greater degree of freedom, but come with increased unpredictability, raising the potential risk of unforeseen errors. Here, we introduce a novel denoising model for diffusion-weighted images that intentionally limits the network output freedom by incorporating multiple pathways with varying degrees of freedom, with the aim of minimizing the chance of unintended alterations to the input. The purpose of this pilot study is to assess the model's ability to perform effective denoising under the constraints. Images from 10 healthy volunteers were used. Key innovations in our model development include: (1) neural network architecture that separated the function for calculating the specific output values from the function for adjusting the calculation for each pixel and (2) training that optimised the network based on both image and secondary obtained diffusion tensor. The generated images were compared with the original ones by measuring the deviation from ground truth images (averaged across eight acquisitions). The generated images demonstrated closer alignment with the ground truth images, both visually and statistically (Q < 0.05), compared to the original images. Furthermore, the advantage of the generated images over the original images was also found in the secondary obtained quantitative parameter maps with significance (Q < 0.05). The usefulness of the proposed method was suggested because it was successful in improving both the quality of the generated images and accuracy of the major diffusion parameter maps under the given restrictions. [ABSTRACT FROM AUTHOR]

Background High-intensity focused ultrasound (HIFU) is an emerging technique for noninvasive ablative treatment. However, HIFU has rarely been performed for the treatment of soft-tissue tumors. Thus, we aimed to assess the feasibility and safety of performing extracorporeal HIFU for the treatment of soft-tissue tumor. The treatment response was assessed using functional magnetic resonance imaging (MRI) techniques. Materials and methods In the rabbit VX2 intramuscular tumor model, HIFU was performed using an extracorporeal HIFU device (YDME FEP-BY02) by varying the electric power from 50 to 400 W, with the other parameters being fixed. The HIFU beam was insonated to one layer of focal spots having a depth of 8 mm. The degree of ablation was evaluated by histological examination and functional MRI techniques including dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) map. The presence of skin burn was also evaluated. Results Applying HIFU with an electric power of 200 W discretely produced the ablation zone without skin burn as planned before treatment (maximal depth: 8-9 mm), which shows the suitability of using HIFU (with 200 W electric power) for the treatment of soft-tissue tumors. By contrast, HIFU with an electric power of 100 W produced an ill-marginated ablation zone with internal residual tumor foci, and HIFU with 300-400 W produced ablation zones with a maximum depth of 13-24 mm, which far exceeded the planned depth and caused skin burn. Perfusion maps of DCE-MRI demonstrated the devascularized ablation zone more conspicuously than conventional contrast-enhanced T1-weighted images, and ADC map demonstrated the surrounding edema or granulation tissue better than conventional T2-weighted images. Conclusion Extracorporeal HIFU treatment for soft-tissue tumor may be a feasible approach with adjustment of input energy level. For post-treatment assessment, functional MRI techniques including DCE-MRI and ADC map may be useful and complementary to conventional MRI. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To evaluate the diagnostic impact of echo planar DW imaging in distinguishing benign from malignant musculoskeletal soft-tissue masses using ADC mapping as a quantitative assessment tool. Patients and methods: We evaluated 73 tumors (21 bone tumors and 52 soft-tissue tumors). MR examinations were performed with a 1.5-T system. Diffusion-weighted single-shot EPI images were obtained in all patients. Apparent diffusion coefficients (ADCs) were calculated by using b factors of 0 and 1000s/mm2. ADC value measurements were compared with the histopathological findings. Results: The average ADC of benign tumors was 1.86±0.67×10−3 mm2/s, and that of malignant soft-tissue tumors was 0.97±0.35×10−3 mm2/s. ADC value of malignant tumors was significantly lower than that of the benign tumor group (p <0.0001). The highest ADC value was seen in the case of ganglion cyst (2.8±0.23×10−3 mm2/s) and cystic neurofibroma (2.5±0.04×10−3 mm2/s), and juxta cortical enchondroma (2.65±0.36×10−3 mm2/s) while the lowest one was seen in aggressive fibromatosis (0.37±0.05×10−3 mm2/s). For malignant soft-tissue masses, the highest ADC value was seen in mesenchymal chondrosarcoma (2.1±0.32) liposarcoma (intermediate grade) (1.4±0.21) while the lowest ADC value was seen in fibrosarcoma (high grade) (0.78±0.14). Conclusion: MR diffusion provides additional information to the routine MRI sequences rendering it an effective non-invasive tool in differentiating between benign and malignant soft-tissue tumors. [Copyright &y& Elsevier]

Abstract: We describe the clinical, EEG, and MRI features of children with reduced diffusion in the unilateral hemispheres. Seven patients with reduced diffusion in the subcortical white matter of the unilateral hemisphere alone were identified. Their median age was 32 months. Human herpesvirus 6 infection was virologically proven in 2 patients and human herpesvirus 7 in 1. The initial neurological symptom was a prolonged seizure in 4, a brief seizure in 2, and delirious behavior in 1. Three patients had biphasic clinical course. Laboratory tests were unremarkable in most patients. MRI showed no abnormal findings during the acute phase, whereas reduced diffusivity in the unilateral hemisphere was seen during the subacute phase. EEG during the acute phase demonstrated marked slowing in the affected hemisphere in 1 patient, mild slowing in 4, and relatively low voltage in 1. No patients died, but 6 patients had various degrees of neurological sequelae. Acute encephalopathy with reduced diffusion in the unilateral hemisphere may be one of the representative phenotypes of acute encephalopathy. [Copyright &y& Elsevier]

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