“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Summary In the CNS, iron in several proteins is involved in many important processes such as oxygen transportation, oxidative phosphorylation, myelin production, and the synthesis and metabolism of neurotransmitters. Abnormal iron homoeostasis can induce cellular damage through hydroxyl radical production, which can cause the oxidation and modification of lipids, proteins, carbohydrates, and DNA. During ageing, different iron complexes accumulate in brain regions associated with motor and cognitive impairment. In various neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, changes in iron homoeostasis result in altered cellular iron distribution and accumulation. MRI can often identify these changes, thus providing a potential diagnostic biomarker of neurodegenerative diseases. An important avenue to reduce iron accumulation is the use of iron chelators that are able to cross the blood–brain barrier, penetrate cells, and reduce excessive iron accumulation, thereby affording neuroprotection.

/pdf/the-role-of-iron-in-brain-ageing-and-neurodegenerative-1atdly4m30.pdf

The role of iron in brain ageing and neurodegenerative disorders

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

SUMMARY: Susceptibility-weighted imaging (SWI) is a new neuroimaging technique, which uses tissue magnetic susceptibility differences to generate a unique contrast, different from that of spin density, T1, T2, and T2*. In this review (the first of 2 parts), we present the technical background for SWI. We discuss the concept of gradient-echo images and how we can measure local changes in susceptibility. Armed with this material, we introduce the steps required to transform the original magnitude and phase images into SWI data. The use of SWI filtered phase as a means to visualize and potentially quantify iron in the brain is presented. Advice for the correct interpretation of SWI data is discussed, and a set of recommended sequence parameters for different field strengths is given.

pdf/susceptibility-weighted-imaging-technical-aspects-and-46wb5gpr8n.pdf

Susceptibility-weighted imaging: technical aspects and clinical applications, part 1.

In patients presenting with a clinically isolated syndrome, MRI can support and substitute clinical information in the diagnosis of multiple sclerosis by showing disease dissemination in space and time and by helping to exclude disorders that can mimic multiple sclerosis. MRI criteria were first included in the diagnostic work-up for multiple sclerosis in 2001, and since then several modifications to the criteria have been proposed in an attempt to simplify lesion-count models for showing disease dissemination in space, change the timing of MRI scanning to show dissemination in time, and increase the value of spinal cord imaging. Since the last update of these criteria, new data on the use of MRI to establish dissemination in space and time have become available, and MRI technology has improved. State-of-the-art MRI findings in these patients were discussed in a MAGNIMS workshop, the goal of which was to provide an evidence-based and expert-opinion consensus on proposed modifications to MRI criteria for the diagnosis of multiple sclerosis.

/pdf/mri-criteria-for-the-diagnosis-of-multiple-sclerosis-magnims-49e6ewajdo.pdf

MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines.

/pdf/rthp-21-characterization-of-radiation-therapy-effects-on-4bf2bv63vi.pdf

Rthp-21. characterization of radiation therapy effects on cerebral vasculature in pediatric brain tumor survivors

Evaluation of major depressive disorder using 7 Tesla phase sensitive neuroimaging before and after mindfulness-based cognitive therapy.

Cranial irradiation is associated with long-term cognitive changes. Cerebral microbleeds (CMBs) have been identified on susceptibility-weighted MRI (SWI) in patients who have received prior cranial radiation, and serve as radiographic markers for microvascular injury thought to contribute to late cognitive decline. The relationship between CMB formation and radiation dose has not previously been quantified. SWI was performed on 13 patients with stable WHO grade III-IV gliomas between 2 and 4 years after chemoradiotherapy to 60 Gy. The median age at the time of treatment was 41 years (range 25 – 74 years). CMBs were identified as discrete foci of susceptibility on SWI that did not correspond to vessels. CMB density for low ( 45 Gy) dose regions was computed. Twelve of 13 patients exhibited CMBs. The number of CMBs was significantly higher for late (>3 years from treatment) compared to early (<3 years) timepoints (early median 6 CMBs; late median 27 CMBs; p = 0.001), and there were proportionally more CMBs at lower doses for late scans (p = 0.006). 88% of all CMBs were observed in regions receiving at least 30 Gy, but the CMB density within medium and high dose regions was not significantly different (p = 0.33 and p = 0.9, respectively, for early and late time points). CMBs predominantly form in regions receiving at least 30 Gy, but form in lower dose regions with longer follow-up. We do not observe a clear dose–response relationship at doses above 30 Gy. These findings provide important information to assess the risk of late microvascular sequelae from cranial irradiation.

Relationship Between Radiation Dose and Microbleed Formation in Patients With Malignant Glioma.

Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multi-contrast brain images. The deep neural network contained 3 residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on 3 healthy volunteers as well as animal (N=6) and prostate patient (N=1) experiments. Results: In animal validation experiment, the differences/errors (mean difference $\pm$ standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 $\pm$ 486 and 154 $\pm$ 512 ms for T1, and 5 $\pm$ 33 and 7 $\pm$ 41 ms for T2, respectively. In healthy volunteer experiments (N=3), whole brain segmentation and relaxometry were finished within ~5 seconds. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1 and T2 weighted images. Conclusion: The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1 and T2 weighted images, in conjunction with segmentation of gray matter, white matter and CSF.

Simultaneous Segmentation and Relaxometry for MRI through Multitask Learning.

Identifying Voxels at Risk for Progression in GBM Based on Dosimetry, Physiologic, and Metabolic MRI

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