UNASSIGNED: Myelin oligodendrocyte glycoprotein antibody disease (MOGAD) is a relatively new entity of demyelinating diseases, clinically presenting with optic neuritis, transverse myelitis, or encephalic symptoms. Typical radiological features include demyelinating cerebral and spinal lesions, cortical involvement, leptomeningeal enhancement, or tumefactive lesions. Here we present a rare case of a young patient with extensive brain stem lesion on the MRI while exhibiting nystagmus, singultus and somnolence.
UNASSIGNED: A 30-year-old male patient presented initially with fever and impaired consciousness, but furthermore developed nystagmus, singultus and tetraparesis during the following week. Repeated MRI examinations revealed extensive brain stem edema with notable bilateral affection of the cerebellar peduncles and the pons. Antiviral and antibiotic treatment was changed to intravenous corticosteroids and immunoglobulins as soon as the diagnosis of MOGAD was established by testing serum and cerebrospinal fluid positive for MOG specific antibodies. MRI alterations vanished completely over time with a delayed, nearly complete clinical recovery of our patient.
UNASSIGNED: Brain stem affection in MOGAD is rare. However, in patients presenting with an unclear brain stem encephalitis the possibility of MOGAD should be considered and tested using MOG antibodies. In case of a positive testing treatment with steroids and immunoglobulins seems recommendable.

UNASSIGNED: Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency. This study aimed to investigate the influence of including and combining functional with anatomical magnetic resonance imaging (MRI) sequences on the quality of automatic segmentations.
UNASSIGNED: T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear and quadratic discriminant analysis and support vector machines, were trained for automatic segmentation of tumor and normal tissue using different combinations of the MR images as input, followed by semi-automatic morphological post-processing. Manual delineations from two experts served as ground truth. The Sørensen-Dice similarity coefficient (DICE) and mean symmetric surface distance (MSD) were used as performance metric in leave-one-out cross validation.
UNASSIGNED: Using T2w images alone, ADA outperformed the other algorithms, yielding a median per patient DICE of 0.67 and MSD of 3.6 mm. The performance improved when functional images were added and was highest for models based on either T2w and DME images (DICE: 0.72, MSD: 2.7 mm) or all four MRI sequences (DICE: 0.72, MSD: 2.5 mm).
UNASSIGNED: Machine learning models using functional MRI, in particular DME, have the potential to improve automatic segmentation of rectal cancer relative to models using T2w MRI alone.

Diagnosis of amyotrophic lateral sclerosis (ALS) depends on clinical evidence of combined upper motor neuron (UMN) and lower motor neuron (LMN) degeneration, although ALS patients can present with features predominantly of one or the other. Some UMN-predominant patients show hyperintense signal along the intracranial corticospinal tract (CST) on T2- and proton density (PD)-weighted images (ALS-CST +), and appear to have faster disease progression when compared to those without CST hyperintensity (ALS-CST -). The reason for this is unknown. We hypothesized that diffusion tensor tractography (DTT) would reveal differences in DTI abnormalities along the intracranial CST between these two patient subgroups. Clinical DTI scans were obtained at 1.5T in 14 neurologic controls and 45 ALS patients categorized into two UMN phenotypes based on clinical measures and MRI. DTT was used to quantitatively assess the CST in control and ALS groups. DTT revealed subcortical loss (\'truncation\') of virtual motor CST fibers (presumably) projecting from the precentral gyrus (PrG) in ALS patients but not in controls; in contrast, virtual fibers (presumably) projecting to the adjacent postcentral gyrus (PoG) were spared. No significant differences in virtual CST fiber length were observed between controls and ALS patients. However, the frequency of CST truncation was significantly higher in the ALS-CST + subgroup (9 of 21) than in the ALS-CST - subgroup (4 of 24; p = 0.049), suggesting this finding could differentiate these ALS subgroups. Also, because virtual CST truncation occurred only in the ALS patient group and not in the control group (p = 0.018), this DTT finding could prove to be a diagnostic biomarker of ALS. Significantly shorter disease duration and faster disease progression rate were observed in ALS patients with CST fiber truncation than in those without (p < 0.05). DTI metrics of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were also determined in four regions of interest (ROIs) along the CST, namely: cerebral peduncle (CP), posterior limb of internal capsule (PLIC), centrum semiovale at top of lateral ventricle (CSoLV) and subcortical to primary motor cortex (subPMC). Of note, FA values along the left hemisphere virtual CST tract were significantly different between controls and ALS-CST + patients (p < 0.05) only at the PLIC level, but not at the CSoLV or subPMC level. Also, no significant differences in FA values were observed between ALS subgroups or between control and ALS-CST - groups (p > 0.05) in any of the ROIs. In addition, comparing FA values between ALS patients with CST truncation and those without in the aforementioned four ROIs, revealed no significant differences in either hemisphere. However, visual evaluation of DTT was able to identify UMN degeneration in patients with ALS, particularly in those with a more aggressive clinical disease course and possibly different pathologic processes.

Cerebral small vessel disease (cSVD) is associated with microvascular and parenchymal alterations. Intravoxel incoherent motion (IVIM) MRI has been proposed to simultaneously measure both the microvascular perfusion and parenchymal diffusivity. This study aimed to evaluate the application of IVIM in cSVD to assess the microvasculature and parenchymal microstructure.
Seventy-three patients with cSVD (age 70 ± 11 y) and thirty-nine controls (age 69 ± 12 y) underwent IVIM imaging (3T). Group differences of the perfusion volume fraction f and the parenchymal diffusivity D were investigated using multivariable linear regression accounted for age, sex and cardiovascular factors. To examine the relation between the IVIM measures and the disease severity on structural MRI, white matter hyperintensity (WMH) load served as surrogate measure of the disease severity.
Patients had a larger f (p < 0.024) in the normal appearing white matter (NAWM) than controls. Higher D (p < 0.031) was also observed for patients compared with controls in the NAWM and grey matter. Both f (p < 0.024) and D (p < 0.001) in the NAWM and grey matter increased with WMH load.
The increased diffusivity reflects the predicted microstructural tissue impairment in cSVD. Unexpectedly, an increased perfusion volume fraction was observed in patients. Future studies are needed to reveal the precise nature of the increased perfusion volume fraction. IVIM imaging showed that the increases of f and D in cSVD were both related to disease severity, which suggests the potential of IVIM imaging to provide a surrogate marker for the progression of cSVD.