Pattern Recognition in White Matter Disorders

109.1 Introduction

MRI is highly sensitive in the detection of white mat- ter lesions. A close association has been demonstrat- ed between the occurrence of white matter abnormal- ities observed with MRI and those found at autopsy. It has been generally assumed that the specificity of MRI is much lower than its sensitivity. The specificity of MRI, however, depends not only on the potential and limitations of the method, but also on the capa- bilities of the person interpreting the MR images (Fig. 109.1). Hence, optimization of the diagnostic specificity of MRI in white matter disorders is achieved by optimizing the quality of both the imag- ing and its interpretation.

Aids to the perceptual and decision processes can be constructed to support the interpretative process. One such aid is systematic and detailed image analysis. A checklist or score list, which prompts the image reader to assess and record a scale value for each feature, is helpful in this respect. A sec- ond aid is a computer program that integrates these scale values into a pattern, compares the pattern ob- tained with the known patterns in a database, and then reaches a differential diagnosis. The reader of the images can use the computer estimates of the like- lihood of various disorders as a guide in the diagnos- tic process.

It is important is to realize that MRI pattern recog- nition has its limitations. In the first place, MRI pat- terns have characteristic features only during a cer- tain phase of progressive disorders. This is well illus-

trated in Fig. 109.2. In the early phase of the disease, the MRI pattern is diagnostic. In the end phase al- most all cerebral white matter is affected and distin- guishing characteristics have been lost. A second problem is illustrated in Fig. 109.3, where the pattern, for reasons unknown, is the inverse of the pattern commonly observed in this disease. The computer program should allow for these exceptions to the rule when they are known to occur. The third problem is illustrated in Fig. 109.4. Sometimes superficial read- ing of the images strongly suggests a certain diagno- sis, whereas on closer examination only some but not all of the main MRI features of a disease are present.

And sometimes the resemblance between patterns is so strong that it is difficult or impossible to discrimi- nate disorders on the basis of the MRI pattern recog- nition program alone.

The benefits of MRI pattern recognition are di- verse. First of all, it facilitates the diagnostic process and reduces the list of necessary laboratory tests.

Sometimes the activity of only one enzyme has to be assessed or only one gene has to be analyzed after reading the MRI. In this way, MRI pattern recognition reduces the burden for patients and families and is a money-saving strategy. Secondly, the recognition of patterns provides important scientific information.

Selective vulnerability of brain structures for differ- ent noxious influences underlies the development of different patterns of involvement of brain structures in different disorders. So far, our understanding of the reasons for the selective vulnerability has remained highly limited.

Pattern Recognition in White Matter Disorders

Chapter 109

Fig. 109.1. T1- and T2-weighted images of a 10-month-old male with glutaric aciduria type I. The images show diffuse bilateral subdural hygromas and fronto- temporal hypoplasia. Myelination is severely retarded. Note that the arach- noid and subdural spaces can be clearly distinguished. The presence of bilateral subdural hygromas could be wrongly interpreted as evidence of child batter- ing. Knowledge of the specific features of glutaric aciduria type I with presence of frontotemporal opercular hypoplasia precludes this mistake. Courtesy of Osaka et al. (1993), with permission

109.2 Noncomputerized Pattern Recognition

Pattern recognition in the daily practice of medical imaging involves three levels of action which are nec- essary to permit optimal interpretation of the image.

These levels can be described as image formation, im- age analysis, and image interpretation.

The first level is the technological level and has to do with the formation of the image. Although the

reader of the image can regard large parts of an MR system and the imaging process as a “black box,” he or she needs to have a more than general knowledge of the imaging process, the parameters involved (repeti- tion time, echo time, inversion time, number of exci- tations, slice thickness, gradient strength and perfor- mance, diffusion sensitivity), the influence of para- meter settings on the image, the possible artifacts, and the ways improving quality when special answers are required.

Fig. 109.2. The upper row of T1-weight- ed (IR) images in this 3-year-old boy show the pattern that is typical for the childhood cerebral form of X-linked adrenoleukodystrophy: peritrigonal and occipital leukoencephalopathy, sparing the U fibers, involving the geniculate bodies and the splenium of the corpus callosum, with typical involvement of corticospinal tracts in pons and mesencephalon. The lower row of one T1- and one T2-weighted image are of the same child, 3 years later. No pattern is recognizable as all white matter structures are involved and all characteristic features of the disease are lost

Fig. 109.3. T₂-weighted images in a 6-year-old boy show bilateral, sym- metrical involvement of the frontal white matter and the frontospinal tracts in the anterior limb of the inter- nal capsule, which can be followed in the brain stem. The spread of the disease is evidently in a ventrodorsal direction. The diagnosis is X-linked adrenoleukodystrophy with reversed pattern. Courtesy of Dr. P. Hoogland and Dr. W.F.M. Arts, Juliana Children’s Hospital, The Hague, The Netherlands

The second level is the level of image analysis, in which the structural elements of the image are ana- lyzed, weighed as to their normality or abnormality, and described as such. This is an important step in the process of teaching and learning, because the analysis of structural elements depends very much on experi- ence, knowledge of anatomy, knowledge of normal brain maturation, and knowledge of pathology.

The first and second levels lay the foundation for the third, the interpretation of the image. In this process the image as such is transcended. Interpret- ing the image requires a combination of a certain un- derstanding of image formation, analysis of the struc- tural elements, experience, and knowledge of disease entities, histopathology, pathophysiology, biochem- istry, and toxicology, and possibly knowledge from

still other sources, in an assessment that aims at an- swering the clinical question.

The first level, image formation, will not be dis- cussed in this chapter. Some details of special tech- niques are discussed in Chaps. 106, 107, and 108.

The second level is the analytical level, which in- cludes a systematic analysis and classification of structural elements of the image (see Tables 109.1–

109.3). Many separate gray and white matter struc- tures should be scored as normal or abnormal. Which structural elements of an image need to be evaluated has been determined by experience of their discrimi- nating value. For example, if one were not aware from previous experience and histopathological studies that the arcuate fibers are spared in many white mat- ter disorders, but that, for example,

-2-hydroxyglu-

109.2 Noncomputerized Pattern Recognition 883

Fig. 109.4. A 23-year-old man had experienced moderate psychomotor retardation from birth onwards and progressive disturbances of gait for the last 2 years.The T2-weighted sagit- tal and transverse images show bilateral peritrigonal and oc- cipital involvement of the white matter; the splenium of the corpus callosum and the posterior limb of the internal capsule are also affected. There is no enhancement after gadolinium injection, which argues against X-linked adrenoleukodystro-

phy.There was no laboratory evidence for a peroxisomal disor- der. Although the changes in the peritrigonal area and occipi- tal lobe could be the remnants of periventricular leukomala- cia, the involvement of the internal capsule and the involve- ment of the splenium of the corpus callosum make this diag- nosis highly improbable. Despite the highly characteristic image and perfect symmetry this remains an unsolved case, which needs to be followed up

taric aciduria starts in the arcuate fibers, it would be senseless to make involvement of the arcuate fibers a point of discrimination in the diagnostic process. The structural elements that need to be scored may also have to be adapted to the diseases under investiga- tion. Some diseases affect very special brain stem structures, as for example in leukoencephalopathy with brain stem and spinal cord abnormalities and el- evated lactate (LBSL). In this disease brain stem structures need to be scored in detail, which is not necessary for most other disorders. Then there are so- called “general characteristics” that need to be scored, including symmetry, confluent versus isolated/multi- focal involvement of the white matter, predominant localization of the abnormalities, white matter swelling, atrophy, rarefaction, cystic degeneration, and the evolution over time. Finally, analysis of “extra characteristics” is important, including contrast en- hancement, calcium deposits, hemorrhages, and the presence of localized cysts.

Structural elements that have been identified by us as having in general the highest discriminating value are: symmetry versus asymmetry, confluent involve- ment of the white matter versus multifocal isolated le- sions, the predominant localization of lesions in the brain, the additional involvement of gray matter structures, contrast enhancement, and the presence of calcium deposits.

In many cases, symmetry is a striking feature of inherited white matter disorders and toxic en- cephalopathies, although not without exceptions, whereas asymmetry is most often seen in acquired white matter disorders, particularly inflammatory disorders and infections. The appearance of the le- sions is important: isolated, or confluent, or both. In most inherited white matter disorders, in toxic en- cephalopathies, and in diffuse white matter injury af-

Table 109.1. List of structural elements to be analyzed for MRI pattern recognition Cerebral cortex Occipital/frontal/parietal/temporal Arcuate fibers Occipital/frontal/parietal/temporal Lobar (deep) white matter Occipital/frontal/parietal/temporal Periventricular white matter Occipital/frontal/parietal/temporal Internal capsule Anterior limb/posterior limb External capsule + extreme capsule

Caudate nucleus Putamen Globus pallidus Thalamus

Corpus callosum Rostrum/genu/corpus/splenium

Cerebellar cortex Cerebellar white matter Hilus of dentate nucleus Cerebellar pedunculi Dentate nucleus

Midbrain Central part/peripheral rim/tectum and tegmentum/specific tracts

Pons Central part/peripheral rim/tegmentum/specific tracts

Medulla Dorsal part/specific tracts

Scoring: no abnormality/slight to mild abnormality/severe abnormality

Table 109.2. List of general characteristics to be analyzed for MRI pattern recognition

Predominance Frontal/occipital/parietal/temporal Periventricular/lobar (deep)/

arcuate fibers

Supratentorial/posterior fossa Symmetry Perfectly symmetrical/slightly asymmetrical/asymmetrical Extension Small isolated lesions/large isolated

lesions/irregularly confluent lesions/highly confluent lesions/combination of these Appearance Swelling/atrophy/rarefaction/cystic

degeneration

Signal intensity Slightly to mildly abnormal/severely abnormal/mixed

Homogeneity Homogeneous/inhomogeneous/

two zones

Demarcation Sharp/vague/mixed

Table 109.3. List of extra characteristics to be analyzed for MRI pattern recognition

Calcium deposition Absent/present

Hemorrhage Absent/present

Contrast enhancement Absent/present Ventricular enlargement No/slight to mild/severe Enlargement of pericerebral No/slight to mild/severe subarachnoid spaces

Cerebellar atrophy No/slight to mild/severe

Myelination Normal/ delayed/no

or hardly any myelin

ter irradiation and chemotherapy, the lesions are con- fluent; as a rule no isolated lesions are seen. Multifo- cal and isolated lesions are more commonly seen in acquired conditions. In multiple sclerosis a mixture of isolated and confluent lesions is the rule. The same is true of Binswanger disease.A global survey of the dis- tribution of the lesion may give an indication of the type of disorder we are dealing with. Sparing of the U fibers is seen in many inherited white matter disor- ders, in vascular disorders, and in subacute HIV en- cephalitis. Several organic and amino acidopathies, contrariwise, preferentially involve the U fibers.

Extra characteristics such as calcifications occur in Cockayne syndrome, in some patients with X-linked adrenoleukodystrophy, in malignant phenylketon- uria, in Aicardi–Goutières syndrome, in some pa- tients with systemic lupus erythematosus, in children with AIDS encephalopathy, in congenital cytomegalo- virus infection, in some patients with a mitochondri- al leukoencephalopathy, and in some children after cranial irradiation and chemotherapy. In some white matter disorders enhancement with contrast is seen in part of the lesion, often in the active border. This observation can be very helpful in the diagnostic process. In X-linked adrenoleukodystrophy the en- hancing border separates the area of complete de- myelination from the area with ongoing demyelina- tion. Enhancement is also a prominent feature of mul- tiple sclerosis and Alexander disease.

Perhaps other criteria could also be used in the recognition of patterns of white matter disorders on the images, such as measurements of the absolute sig- nal intensities and measurements of T

and T

, ADC, fractional anisotropy, and MTR. For instance, in sub- acute HIV encephalitis, the signal intensity of the white matter lesion, at least in the beginning of the disease, is not as high on T

-weighted images as it is in most other white matter disorders. Neuropathologi- cal examinations confirm that demyelination in sub- acute HIV encephalitis is only partial and mild. On the MR images the involved white matter has a coarse, granular texture, which corresponds well with the histological finding of numerous small foci of more complete demyelination.

In our definition, specificity exists in degrees. The pattern emerging from the image can be expressed as being diagnostic (pathognomonic), highly sugges- tive, suggestive, possible, atypical, and impossible. It is only in the first category that the MRI pattern is pathognomonic for one specific disorder. In the other categories, clinical and laboratory evidence is neces- sary to complete the diagnosis. The lists given should be considered as examples; they are by no means complete (Table 109.4).

Quite a few white matter disorders can be listed under the categories “diagnostic” or “highly sugges- tive,” implying that MRI makes a firm contribution to

the diagnostic process in these disorders. Very often, by adding clinical information or laboratory data, a higher diagnostic category can be achieved.Addition- al facts that will help to reach a diagnosis are many and include facts in the clinical history and findings

109.2 Noncomputerized Pattern Recognition 885 Table 109.4. Specificity of MRI patterns

Diagnostic

Cerebral form of X-linked adrenoleukodystrophy Zellweger syndrome

Cerebrotendinous xanthomatosis (if fat deposits are present) Periventricular leukomalacia

Megalencephalic leukoencephalopathy with subcortical cysts

Canavan disease Maple syrup urine disease

L-2-Hydroxyglutaric aciduria Alexander disease

Some toxic encephalopathies Kearns–Sayre syndrome

Multiple sclerosis, when McDonald criteria are met Highly suggestive

Metachromatic leukodystrophy Globoid cell leukodystrophy Mucopolysaccharidoses Leigh syndrome MELAS

Cockayne syndrome Phenylketonuria Glutaric aciduria type I

Acute disseminated encephalomyelitis Wilson disease

Central pontine myelinolysis Suggestive

Lowe syndrome

Pelizaeus–Merzbacher disease Multiple sclerosis

Subacute HIV encephalitis Binswanger disease Wallerian degeneration Toxic encephalopathies Possible

Multiple sclerosis, less advanced cases Extrapontine myelinolysis

Atypical

Unusual appearance with established diagnosis, e.g., asymmetric cerebral involvement in X-linked adrenoleukodystrophy; Alexander disease starting in the cerebellum

Impossible

The pattern excludes the suspected diagnosis

at physical examination. The latter may include ab- normalities of eyes, face, hair, internal organs, skin, skeleton, and peripheral nerves. Examples are facial dysmorphia in Cockayne syndrome and mucopoly- saccharidoses, ectopic lenses in hyperhomocystein- uria, and multiple organ involvement in Zellweger syndrome and in mitochondrial disorders.

109.3 Computer-Assisted Pattern Recognition

With the increasing complexity of diagnostic pro- cesses and with the increase in number of recognized, rare disorders, the need to implement expert systems is felt. To develop these, it is essential to collect data systematically and quantitatively and to set up large data banks. We created a database that could be used to develop a computer-assisted pattern recognition program in white matter disorders, with the excep- tion of ischemic white matter disease related to arte- riosclerosis (van der Knaap et al. 1991). Over a certain period of time, the MR images of all patients under the age of 50 years or, when risk factors for vascular disease were present, under the age of 40 years, with lesions exclusively or predominantly involving the white matter, were scored with the help of a detailed scoring list (see Tables 109.1–109.3). The total study population numbered 1483. The diagnosis in the pa- tients was known. The patients were grouped into di- agnostic categories according to the classification we proposed, which stresses etiological and histological similarities between disorders of the same category.

The data obtained were analyzed. The frequency of occurrence of image abnormalities and features per disease category or subcategory were counted and the results presented as histograms (examples in Figs. 109.5–109.8). The outlines of the histograms – the “skylines” – can be considered to be characteristic of the disease or disease category.

In addition, a computer program was developed to estimate post-MRI probabilities of possible diag- noses in new patients with white matter abnormali- ties. The computer program was based on Bayes’ the- orem. Prevalences of the different disease categories and subcategories and frequencies of image abnor- malities per disease were estimated from the data of this study. When the imaging findings of a new pa- tient were entered, the computer program would pro- vide a differential diagnosis. For each possible diag- nosis the positive predictive value and a two-sided 95% confidence interval could be computed.

Of course, there are several limitations to the prac- tical use of a computer-assisted diagnostic system.

The large amounts of data involved make clustering necessary; for example, a two-point scale (yes/no) had to be used instead of a three-point scale (se-

vere/mild/not involved) indicating the severity of involvement of the structures. Also, disease groups had to be clustered to some extent. This would seem to be sensible, because it is practically impossible to differentiate all individual conditions on MR criteria alone. Another limitation of such a computer pro- gram is that its quality is highly dependent on the quantity and quality of the data it contains. To im- prove the quality of the program in this respect a mul- ticenter database with well-defined criteria for inclu- sion of cases would be extremely helpful in narrowing the confidence intervals in rare disorders.

Of course, such a computer system cannot compete with the flexibility and speed of the human brain in pattern recognition. Computer systems are, therefore, not to be regarded as competing, but as complemen- tary: a support to the experienced and a learning tool for the inexperienced.

109.4 Practical Application of Pattern Recognition

Usually MR images are interpreted without the help of a computer program. However, in daily practice, our approach should also be systematic and we should progress logically through the diagnostic process. We will analyze the logical steps involved in systematic reading of the MR image, well aware that several steps may occur synchronously and that the sequence is variable.

The first step identifies the nature of the cerebral abnormalities. In the global analysis of MR images of the brain, the first consideration relates to the struc- tures involved: gray matter, white matter, or both.

Next, the examiner tries to identify the nature of the disorder with which he or she is confronted. If gray matter is involved, are there signs of a congenital anomaly with ectopic gray matter? Is there gray mat- ter atrophy or are there parenchymatous lesions? If there is a white matter disorder, of what nature is it? Is it demyelination, delayed myelination, or hypomyeli- nation? Is there white matter swelling? Is there white matter rarefaction or cystic degeneration? Are there white matter cysts? Is there a loss of white matter vol- ume or gliotic retraction?

The second step considers the symmetry or asym-

metry of the abnormalities. Symmetrical white mat-

ter involvement occurs in most of the inherited white

matter disorders, toxic encephalopathies, and some

of the other acquired white matter disorders. Many

of the acquired disorders lead to asymmetrical le-

sions, with some exceptions. A tendency towards

symmetry is present in periventricular leukomalacia,

subcortical arteriosclerotic encephalopathy, subacute

HIV encephalitis, and congenital CMV encephalopa-

thy.

The third step defines the aspect of the lesions. Are they confluent, isolated, or both; diffuse or multiple?

In the inherited white matter disorders lesions are of- ten confluent and diffuse. Multiple sclerosis, of course, is the prototype of a disease with lesions that

are usually asymmetrical, partly confluent, but main- ly isolated, and located in rather specific areas of the brain.

The fourth step establishes the predominant loca- tion of the lesions. Confluent, symmetrical lesions in

109.4 Practical Application of Pattern Recognition 887

Fig. 109.5. Histogram of the frequency of involvement of the structural elements in the cerebral form of X-linked adreno- leukodystrophy. co, cortex; uf, arcuate fibers; lo, lobar (deep) white matter; pv, periventricular white matter; f, frontal; p, pari- etal; t, temporal; o, occipital; cc, corpus callosum; ci, internal capsule; ce, external and extreme capsules; bn, basal nuclei (globus pallidus, putamen, caudate nucleus); th, thalamus; bs, brain stem; cw, cerebellar white matter; cg, cerebellar cortical gray matter; cp, middle cerebellar pedunculi; nd, dentate nu- cleus; sy, symmetrical distribution; asy, asymmetrical distribu-

tion; s, small isolated lesions; l, large isolated lesions; i, irregular- ly confluent lesions; c, highly confluent lesions; sd, sharp de- marcation; md, mixed demarcation; vd, vague demarcation;

has, highly abnormal signal intensity; mas, mildly abnormal signal intensity; ms, mixed signal intensity; h, homogeneous signal intensity; ih, inhomogeneous signal intensity; 2z, two zones discernible in the lesion; ca, calcification; nmy, normal myelination; dmy, delayed myelination; nomy, no or hardly any myelin present; dv, deformation of the ventricular system

the periventricular area narrow the number of diag- nostic possibilities considerably. Sparing of the arcu- ate fibers is characteristic of the sphingolipidoses, whereas the reverse is true of some of the organic and amino acidopathies, which start in the U fibers. Le- sions confined to the cerebellar white matter are rare.

Cerebrotendinous xanthomatosis, adrenomyeloneu- ropathy, Refsum disease, and Langerhans cell histio- cytosis can be examples. Multiple sclerosis has a pref- erence for the upper edges of the lateral ventricles and the centrum semiovale. The cerebral form of X-linked adrenoleukodystrophy usually presents in the occipi- tal lobes. Herpes simplex virus infections favor the frontal and temporal lobes.

The fifth step defines the pattern of spread of the disease. Adding the pattern of spread to the analysis further helps to distinguish certain entities. The cere- bral form of X-linked adrenoleukodystrophy usually spreads in a dorsoventral direction; the direction of spread of Alexander disease is ventrodorsal, that of Canavan disease centripetal, and that of metachro- matic leukodystrophy centrifugal.

The sixth step weighs the contribution of gray matter involvement relative to white matter involve- ment. Is the gray matter involvement a major or a minor part of the disease? Are cortical or central gray matter structures involved? Deep gray matter is often involved in mitochondrial disorders, in Bin-

Fig. 109.6. Histogram of the frequency of involvement of the structural elements in Pelizaeus–Merzbacher disease. Abbrevia- tions, see Fig. 109.5

swanger disease, in vasculitides, in glutaric aciduria type I and many other organic acidurias, in toxic encephalopathies such as carbon monoxide intoxi- cation, cyanide intoxication, amphetamine intoxica- tion, and in post-hypoxic–ischemic conditions. In- volvement of the basal ganglia is rare in multiple sclerosis.

The seventh step evaluates whether the lesions en- hance after contrast injection. Sometimes enhance- ment of the lesions leads to a characteristic pattern, as in cerebral X-linked adrenoleukodystrophy and Alexander disease. Active lesions in multiple sclerosis enhance. In acute disseminated encephalomyelitis not all lesions enhance or no lesions enhance at all; in

progressive multifocal leukoencephalitis enhance- ment occurs occasionally.

109.5 Pattern Recognition in Unclassified Leukoencephalopathies

MRI pattern recognition has not changed the fact that in about 50% of the children with significant white matter abnormalities on MRI, no specific diagnosis can be established despite an extensive laboratory work-up; the disease remains unclassified. The per- centage is lower in adults. Pattern recognition works well in patients with a diagnosis that can be con-

109.5 Pattern Recognition in Unclassified Leukoencephalopathies 889

Fig. 109.7. Histogram of the frequency of involvement of the structural elements in multiple sclerosis. Abbreviations, see Fig. 109.5

firmed by laboratory tests because patients with the same or a similar disorder have been shown to share the MRI pattern. We decided to apply MRI pattern recognition to the large group of patients with an unclassified leukoencephalopathy, assuming that patients with the same disease entity would again share MRI characteristics. Application of pattern recognition to the MR images of large numbers of pa- tients with an unclassified leukoencephalopathy led to the identification of several distinct patterns seen in multiple patients. MRI pattern recognition has

contributed to the identification of several hitherto unidentified white matter disorders: megalencephal- ic leukoencephalopathy with subcortical cysts (MLC);

vanishing white matter disease (VWM); hypomyeli- nation with atrophy of the basal ganglia and cerebel- lum (HABC); and leukoencephalopathy with brain stem and spinal cord involvement and elevated white matter lactate (LBSL).

The general MRI-oriented approach to unclassi- fied leukoencephalopathies has been described else- where (van der Knaap et al. 1999). Seven major cate-

Fig. 109.8. Histogram of the frequency of involvement of the structural elements in central pontine myelinolysis. Abbreviations, see Fig. 109.5

gories can be identified using simple and robust MRI criteria, mainly based on the predominant location of the white matter abnormalities. Our aim was to divide the unclassified leukoencephalopathies into more workable groups. These categories are distinct, as has been validated statistically.

Group A is characterized by severe hypomyelina- tion. This is the largest single category among the un- classified leukoencephalopathies. In all patients cur- rently known disorders leading to hypomyelination were ruled out, including Pelizaeus–Merzbacher dis- ease, Salla disease, and DNA repair disorders.

Group B is characterized by global involvement of the cerebral white matter. In this group two separate disease entities were identified: B1, megalencephalic leukoencephalopathy with subcortical cysts, and B2, leukoencephalopathy with vanishing white matter.

These diseases, initially identified by their MRI pat- tern as separate entities, are now also confirmed to be genetic entities.

Group C is characterized by extensive, predomi- nantly frontal white matter abnormalities, relatively sparing of the occipital lobes, with basal ganglia ab- normalities and also in many cases brain stem abnor- malities. Most of the patients in this category had Alexander disease, a diagnosis that can now also be genetically confirmed.

Group D is characterized by predominantly peri- ventricular white matter abnormalities. This is a het- erogeneous group of disorders, often genetic, with a progressive clinical disease course.

Group E is characterized by predominant involve- ment of the deep (lobar) white matter. Deep or lobar white matter is located in between the periventricular white matter and the U fibers. In most patients the white matter abnormalities consisted of multifocal isolated lesions. In most patients the encephalopathy was static. A considerable proportion of the patients probably had congenital cytomegalovirus infection, a diagnosis that can now be confirmed using PCR for cytomegalovirus DNA on the filter paper containing neonatal blood spots (the Guthrie card).

Group F is characterized by predominant involve- ment of the U fibers.

Group G is characterized by abnormalities pre- dominantly in the white matter of the posterior fossa.

Subdividing patients with an unclassified leukoen- cephalopathy using these categories will facilitate fu- ture research on homogeneous subgroups of patients and allow pooling of data across multiple centers.

109.6 Typical and Atypical MRI Patterns

The steps leading to the identification of so far un- classified white matter disorders are based upon a strict adherence to MRI criteria. The resulting homo- geneous group allows further exploration of the bio- chemical or genetic background. Once the biochemi- cal or genetic background is established, less typical cases can be analyzed, so that information can be ob- tained about the phenotypic variations of the disease, including variations in MRI patterns. Important ex- amples can be found in the chapter on Alexander dis- ease (Chap. 57) and the chapter on vanishing white matter (Chap. 65).

109.7 Examples

The following examples serve as illustrations of the described approach.

109.7.1 Example I

A 4-year-old girl presented with bilaterally dimin- ished vision. The initial MRI showed optic neuritis, more severe on the right side. There was one lesion in the cerebral parenchyma on the right side around and under the anterior commissure. She responded favor- ably to a short course of methylprednisolone, but within a week after discontinuation she returned with severe symptoms of myelopathy in addition to de- creased vision. Figure 109.9 shows the MRI findings at that time. The lower row of FLAIR images shows involvement of the caudate nucleus and putamen on the right side and bilateral lesions in the pulvinar.

The lesion around the right anterior commissure is conspicuous. The signal intensity of the external/

extreme capsules on both sides is too high and there is a distinct lesion in the parietal operculum on the left. There is also involvement of the dorsal part of the pons. At upper left, a sagittal T

-weighted image of the cervicothoracic spine shows swelling of the cord with central high signal intensity (white arrows).

At upper right, a STIR image at the level of the chiasm demonstrates swelling of the right part of the optic chiasm with a central lesion (arrow). Although the combination of optic neuritis followed by myelopathy is reminiscent of Devic neuromyelitis optica, the extent of cerebral lesions in this case argues in favor of the diagnosis acute disseminated encephalomyelitis.

109.7 Examples 891

Fig. 109.9. Example I

109.7.2 Example II

The images shown in Fig. 109.10 reveal a diagnostic pattern. They relate to a 32-year-old man with pro- gressive neurological disability. The FLAIR images (first and second rows) show perfectly symmetrical, confluent white matter lesions, mainly involving the

posterior white matter, but also affecting the spleni- um of the corpus callosum, extending towards the basal ganglia, and including the corticospinal tracts.

After contrast (third row), enhancement of a rim within the lesion is seen.Although unusual at this age, the pattern is diagnostic of the cerebral form of X-linked adrenoleukodystrophy.

109.7 Examples 893

Fig. 109.10. Example II

Fig. 109.11. Example III

109.7.3 Example III

A 7-year-old boy presented with intractable seizures on the right. An MRI was obtained, which showed extensive white matter abnormalities for which no metabolic cause could be found (Fig. 109.11). The boy was sent for a second opinion. Clinical history and neurological examination revealed evidence of an old, mild, right-sided hemiparesis. Sequential MRIs taken during the following 2 years showed static white matter abnormalities. The T

-weighted images (first row) show white matter abnormalities in the up-

per part of the left hemisphere, involving both cen- trum semiovale and periventricular region. The cor- tex in contact with this lesion appears too thick.

Parasagittal T

-weighted images of the left hemi- sphere (second row) show the abnormalities in both the gray and white matter. The IR images (third row) also show the cortical and subcortical abnormalities.

The coronal FLAIR images (fourth row) show the tri- angular shape of the signal abnormalities, tapering as they approach the lateral ventricle. The pattern is that of a balloon-type focal cortical dysplasia (Taylor type), simulating a white matter disease.

109.7 Examples 895

Fig. 109.12. Example IV

109.7.4 Example IV

The images of this 1-year-old girl were sent for an opinion because of an unclassified white matter dis- ease (Fig. 109.12). The upper two rows of T

-weighted images show subcortical white matter lesions with broadening of the overlying cortex. The sagittal T

- weighted images (third row) show small indentations in the ventricular lining (arrows), representing subependymal nodules. These nodules are dark on the T

-weighted images, while they are white on T

- weighted images. The pattern of cortico-subcortical tubers and subependymal nodules is indicative of tuberous sclerosis.

109.7.5 Example V

The T

-weighted parasagittal images (second row of Fig. 109.13) show abnormalities in the temporal lobe of three different patients: cysts in the temporal pole in all three patients and widened temporal horns in the first two. The diagnosis in each case can be made in conjunction with the axial T

-weighted images (first row). The images on the left showing multifocal lesions with the largest lesions in the deep parietal white matter, together with the temporal abnormali- ties, in a microcephalic child are highly suggestive of congenital cytomegalovirus infection. The images in the middle showing diffuse cerebral white matter ab- normalities, diffuse cortical dysplasia, and pontine hypoplasia (small sagittal image) demonstrate the

Fig. 109.13. Example V

pattern of Walker–Warburg congenital muscular dys- trophy. The images on the right showing, in addition to the cysts in the anterior temporal lobe, diffuse cere- bral white matter abnormalities and frontoparietal subcortical cysts (small image) in a megalencephalic child are diagnostic of megalencephalic leukoen- cephalopathy with subcortical cysts.

In the next series of examples (VI–X) unusual MR findings are presented: black and white dots and stripes.

109.7.6 Example VI

In this patient with hypomelanosis of Ito (Fig.

109.14), the FLAIR images (first row) show confluent white matter abnormalities in the centrum semiovale and periventricular region. In addition, multiple black dots are seen, some arranged in rows. The sagit- tal T

-weighted images (second row) show that the dots are related to radially arranged widened perivas- cular spaces.

109.7 Examples 897

Fig. 109.14. Example VI

109.7.7 Example VII

In this case of Lowe syndrome (Fig. 109.15), the first row of T

-weighted images show symmetrical white matter abnormalities in the deep and periventricular white matter. Horizontal stripes in the corpus callo- sum indicate widened perivascular spaces. The sec-

ond row of FLAIR images shows a multitude of black dots but no stripes. They represent focal cysts, not clearly following the perivascular spaces. The third row of T

-weighted sagittal images confirms that the black holes do not or do not only represent widened perivascular spaces.

Fig. 109.15. Example VII

109.7.8 Example VIII

In some subtypes of mucopolysaccharidosis, widened perivascular spaces are the hallmark of the MR pat- tern. This is demonstrated in the T

- and T

-weighted transverse images and T

-weighted sagittal images (Fig. 109.16) of a patient with Hurler syndrome. Often the corpus callosum is involved.

109.7 Examples 899

Fig. 109.16. Example VIII

109.7.9 Example IX

Figure 109.17 contains the T

-weighted transverse, sagittal, and coronal images and one T

-weighted im- age of a 4-year-old child with a mild variant of maple syrup urine disease. In addition to the abnormalities in the globus pallidus, thalamus, midbrain, pons, and

dentate nucleus, there is a striking widening of the perivascular spaces, especially well depicted in the T

- weighted image (first row, right) and the sagittal and coronal T

-weighted images (third row). Myelin depo- sition also seems to have happened mainly in perivas- cular areas, leading to dark stripes on T

-weighted images.

Fig. 109.17. Example IX

109.7.10 Example X

A 10-year-old boy presented with multiple transient episodes of neurological dysfunction. In Fig. 109.18, the T

-weighted images on the left show a multitude of tiny lesions in the white matter and basal ganglia on a background of more confluent cerebral white matter lesions. The T

-weighted images in the middle show a multitude of dark stripes, which were con- firmed to be abnormal vessels on the MRA (source image shown upper right), whereas the conventional angiography shows the pattern of moyamoya syn- drome.

The next three examples show leukoencephalo- pathies linked to chromosomal abnormalities.

109.7 Examples 901

Fig. 109.18. Example X

109.7.11 Example XI

A 2-year-old boy presented with borderline macro- cephaly, retarded development, and dysmorphic fea- tures. Chromosomal analysis revealed an inversion- duplication of chromosome 8p. The T

-weighted MR images (Fig. 109.19) show dilated CSF spaces and multiple white matter abnormalities. They were found to be stable on follow-up.

Fig. 109.19. Example XI

109.7.12 Example XII

A 5-year-old girl was known to have psychomotor re- tardation, dysmorphic features, eye abnormalities, and hearing difficulties. Chromosomal analysis re- vealed an unbalanced translocation between chromo- some 6p25 and chromosome 20q13. The T

-weighted images (Fig. 109.20) show spotty white matter abnor- malities in the deep and periventricular white matter and several dot-like lesions in the basal ganglia. The coronal FLAIR image (lower right) confirms that some of the abnormalities are related to enlarged perivascular spaces.

109.7 Examples 903

Fig. 109.20. Example XII

109.7.13 Example XIII

A 2-year-old girl had a similar clinical picture and MRI findings (Fig. 109.21; compare with Fig. 109.20).

Chromosomal analysis was normal. Because of the striking clinical and MRI similarities, a submicro- scopic deletion of chromosome 6p25 was suspected.

FISH analysis confirmed the chromosome 6p25 dele- tion, illustrating the power of pattern recognition in the reading of MR images.

Fig. 109.21. Example XIII