Comparative analysis of double inversion recovery (DIR) and diffusion tensor imaging (DTI) sequences for the detection of brain white matter diseases

INTRODUCTION

Double inversion recovery (DTI) and diffusion tensor imaging (DIR) sequences are contrasted, as both have very different roles in the assessment of brain pathologies. DTI will help in assessing microstructural changes in white matter tracts and hence give information about neural connectivity and integrity. On the other hand, DIR sequences have been proven to be highly sensitive in lesion and abnormality detection in the brain, particularly in pathologies such as MS and epilepsy. They have a higher rate of lesion detection compared to conventional sequences like fluid-attenuated inversion recovery (FLAIR) and T2. Moreover, synthetic DIR images have been proven to exhibit higher contrast to synthetic T2W-FLAIR and conventional DIR images, thus proving the advantage of DIR sequences in clinical neuroimaging. While DTI takes a look at microstructural changes, DIR sequences are much better at detecting and characterizing lesions; thus, the two techniques complement each other as tools for comprehensive assessment of brain imaging. DIR and DTI sequences are just two of the sequences used in detecting brain white matter diseases [Figure 1]. DIR is a pulse sequence for increasing the visibility of gray matter lesions, especially cortical lesions, in patients with MS.¹ It has been shown to be superior to FLAIR in detecting lesions at different locations such as the cortical, periventricular, and infratentorial regions.² DTI is a technique that measures water molecule diffusion in the brain tissue, giving information about white matter tract integrity. It has been used frequently to assess diseases of white matter, like MS.³

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Figure 1:

Shows the Transverse Section of brain. (a-c) axial section of the supratentorial brain in the diffusion weighted imaging (DIR) Sequences with background suppression appears with the characteristics feature. The inflammatory lesion show sharper demarcation, thus better visibility. Although the contrast between lesion and gray matter seems very subtle, the DIR sequences image tends to be very sensitive in detecting juxtacortical and mixed white-gray matter lesions clearly shown by arrows and arrow heads. Besides, it is easily distinguished between juxtacortical and lesion with the help of the DIR sequences.

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While DIR and DTI show some encouraging results in the detection of brain white matter disease, more studies are required with respect to the diagnostic accuracy of both techniques and their potential role in clinical practice.⁴

The white matter is composed of myelinated axons, oligodendrocytes, and glial cells. The oligodendrocytes produce myelin sheaths which envelop the axons and give the white matter its color [Figure 2]. These myelin sheaths ensure efficient conduction of electrical signals between different areas of the brain [Figure 3].⁵

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Figure 2:

Demonstration the Diffusion Tensor Imaging of white matter tract. (a and b) 3D Sagittal plane of brain nerve fibers shows the detailed anatomy of white matter tract. (c) T2W Axial and (d) Coronal respectively of brain white matter tract shows the connectivity structure of the brain. Bright colors indicate the directionally and integrity of the fiber bundles, indicating the neural communication and connectivity patterns.

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Figure 3:

(a) Coronal view and (b) Transverse view of the brain. It consists of myelinated axons, oligodendrocytes, and glial cells; the myelin sheath, produced by the oligodendrocytes, wraps the axons, giving the white matter its color. Myelin sheaths increase velocity and efficiency in the transmission of electrical impulses between different regions of the brain.

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The white matter acts as a network of pathways that connect different regions of the central nervous system (CNS).^5,6 It allows for the transmission of signals from one gray matter area to another, ensuring the timely delivery of information.⁵ The myelinated axons in the white matter are well-designed to conserve energy while maintaining this high-fidelity signal delivery.⁷ The white matter also contributes to the coordination of signal arrivals to target neurons and their summation with other synaptic inputs. The tightness of internal structural connectivity within white matter may impact the structure-function relationship as shown in Figure 4.

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Figure 4:

Microstructural view of brain tissue revealing nerve fibers intricately woven amidst supportive astrocytes. The oligodendrocytes form myelin sheaths that envelop the nerve fibers, thus enabling fast transmission. The given tight view shows just how complicated the interplay is between neurons and glial cells, which is fundamental for brain function and communication.

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Full-Text Review and Final Selection

Eligibility assessment: Each of the 401 articles were assessed based on the following eligibility criteria:

• Relevance to focus areas: DIR, DTI, white matter disease, neuroimaging, comparative analysis

• Availability of sufficient data to conduct comparative analysis

• Robustness of methodology and quality of the study

Exclusion of full-text articles: 344 full texts were excluded in the review process review because they did not extend satisfactory coverage to focus areas. They had to be discarded to maintain the relevance and quality of the review.

Final inclusion: The final set of articles included in the review consisted of: 44 original articles, 5 book chapters, 3 conference articles, 1 thesis: 1, 1 case report, 3 review and meta-analysis articles: Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that can detect neuropathology in neural tissue as shown in Figure 5. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enhance the microstructural data obtained via DW-MRI, making it more specific to microstructural properties. Machine learning methods are useful for analyzing multidimensional data sets. In one study, b-tensor encoded DW-MRI data analyzed with machine learning showed potential for diagnosing histopathology and neurodegeneration.^8,9 Another study proposed a disentangled conditional diffusion model for multi-contrast brain MRI super-resolution, which improved the quality of low-resolution contrasts by leveraging complementary information from multi-contrast MRI.¹⁰ Structural MRI image registration onto diffusion-weighted imaging variants was also investigated for tractographic samplings of the visual system, showing that structurally-placed ROIs with registration onto readout-segmented volumes were most suitable for white matter template generation.¹¹ Lastly, SI characteristics derived from DWI_b1000 and T2-weighted-based MRI demonstrated excellent performance in differentiating white matter lesions caused by MS and cerebral small vessel disease.¹²

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Figure 5:

Preferred reporting items for systematic reviews and meta-analyses flow chart for selection of the studies.

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DIR and DTI are valuable techniques for detecting brain white matter disease. DIR, is effective in identifying cortical lesions in multiple sclerosis patients.¹³ On the other hand, DTI, provides insights into white matter integrity and microstructural changes associated with aging, Alzheimer’s disease, and cerebrovascular disease.^14,15 DTI excels in characterizing tissue microstructure and detecting alterations related to brain vascular anomalies.¹⁶ On the other hand, DIR is particularly effective in pinpointing cortical lesions, especially in MS patients.¹⁴ DTI measures parameters such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) to provide detailed information on microstructural changes.^14,17 Neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP) MRI offer alternative models for better characterization of microstructural changes related to aging and Alzheimer’s disease.¹⁸ Integrating DTI and DIR can offer a comprehensive assessment of brain white matter diseases, leveraging the strengths of each modality for a more thorough evaluation of white matter integrity and cortical lesion detection.¹⁹ DTI captures water diffusion in tissues, which is vital for studying brain connectivity. It utilizes Echo planar imaging (EPI), typically with short echo times (TE) and long repetition times (TR), and requires multiple diffusion directions and b-values. Motion correction and tensor calculations ensure accurate results. DIR suppresses specific tissue contrasts to enhance pathological visibility. Implemented with gradient echo (GRE) or fast spin echo (FSE), it utilizes long TR with two inversion times (TI) to nullify selected tissues. Optional fat saturation and motion correction techniques improve image quality. Both protocols [Table 1] offer insights into brain structure and pathology in clinical and research settings.

Table 1: This tabulated form provides a clear comparison between the DTI and DIR sequences, including their purposes, sequence types, parameters, and special considerations.

Sequence	DTI	DIR
Purpose	Study diffusion of water molecules in biological tissues, assess microstructural organization of white matter tracts	Suppress specific tissue contrasts, enhance visibility of certain pathological features or tissue types
Sequence type	Echo Planar Imaging (EPI)	Gradient Echo (GRE) or Fast Spin Echo (FSE)
FOV	220-240 mm	220-240 mm
Slice thickness	2-3 mm	3-5 mm
Matrix size	128 × 128 or higher	256 × 256 or higher
NEX	1	1
TE	Short, 60-100 ms	Short for GRE: 20-30 ms, Long for FSE: 80-100 ms
TR	Long, 3000-10000 ms	Long, 3000-6000 ms
Diffusion directions	Minimum 6 non-collinear directions, up to 64 for higher angular resolution	N/A
B-values	b = 0 and b = 1000 s/mm2	N/A
Slice orientation	Axial, coronal, or sagittal	Axial, coronal, or sagittal
Optional parameters	Gradient Strength, Partial Fourier Factor, Parallel Imaging	Fat Saturation, Parallel Imaging, Motion Correction
Special considerations	Motion Correction, Eddy Current Correction, Tensor Calculation	TI Adjustment, Image Fusion

DTI: Diffusion tensor imaging, DIR: Double inversion recovery, FOV: Field of view, NEX: Number of excitation, TE: Echo time, TR: Repetition time, N/A: Not available, TI: Time of inversion

COMPARATIVE ANALYSIS

The comparison between DTI and DIR, as depicted in Table 1, points out the differences in their purpose, sequence parameters, and clinical applications. DTI is mainly designed to study the diffusion of water molecules within biological tissues and to assess the microstructural organization of white matter tracts.^20-22 In contrast, DIR focuses on suppressing specific tissue contrasts, such as cerebrospinal fluid (CSF) or white matter, to enhance the visibility of certain pathological features, such as lesions in neurological conditions.

DTI uses EPI sequences, while DIR employs GRE or FSE sequences. Both techniques have a field of view (FOV) in the range of 220–240 mm. Their slice thickness differs, with DTI offering thinner slices of 2–3 mm for improved spatial resolution and DIR using thicker slices of 3–5 mm for better signal-to-noise (S/N) ratio. Similarly, the resolution matrix size of DTI is usually 128 × 128 or higher, whereas DIR is 256 × 256 or higher. As for acquisition parameters, DTI uses a longer TR of 3000–10,000 ms and a short TE of 60–100 ms to optimize diffusion sensitivity. DIR, depending on the subtype, uses a TR of 3000–6000 ms, with GRE sequences requiring shorter TE (20–30 ms) and FSE sequences using longer TE (80–100 ms). Unlike DIR, DTI incorporates diffusion-specific parameters such as b-values (commonly b = 0 and b = 1000 s/mm²) and diffusion directions (minimum of six non-collinear directions, up to 64 for higher angular resolution), which are crucial for tensor calculations and tractography.

Both DTI and DIR permit axial, coronal, or sagittal slice orientation but differ in optional parameters and special considerations. DTI employs advanced gradient strength, partial Fourier factor, and parallel imaging to reduce artifacts, along with motion correction and eddy current correction for accurate tensor analysis. DIR focuses on fat saturation, motion correction, and the adjustment of TI to suppress specific tissue signals, with image fusion often used for enhanced diagnostic capabilities. It excels in detecting subtle microstructural changes, aiding in the study of neurological conditions such as MS, stroke, and traumatic brain injury.²³ Additionally, DTI provides quantitative measures such as FA and MD, enabling the quantification of tissue integrity and pathology.²⁴ Its applications extend to research, where it is widely utilized in neuroscience studies focusing on brain development, aging, and various neurological disorders. DTI’s strengths lie in its ability to offer comprehensive insights into white matter integrity and pathology, making it a valuable tool in both clinical and research settings. We analyzed data from reliable databases like Scopus, PubMed, SCI and Web of Science to draw conclusions in Table 2.

DTI exhibits limitations such as limited spatial resolution, especially when compared to structural MRI.²⁵ Moreover, DTI is highly sensitive to motion artifacts, which is particularly problematic in pediatric and patient populations, potentially compromising image quality and analysis accuracy.²⁴ Another weakness lies in DTI’s struggle in accurately resolving crossing fibers, especially in areas where white matter fibers intersect, potentially leading to inaccuracies in tractography results.²⁶ DTI’s assumption of a single fiber orientation within each voxel poses challenges in capturing complex fiber architectures, like regions with crossing, kissing, or fanning fibers, limiting its ability to represent intricate brain structures.²⁷

Diffusion-weighted imaging with resting-state fMRI (DIR) is a powerful technique that merges structural insights from DWI with functional connectivity data from resting-state fMRI, offering a holistic view of brain structure-function relationships.^28,29 By combining structural connectivity mapping with functional correlation analysis, DIR enables the exploration of how brain regions interact both structurally and functionally during rest.³⁰ Moreover, DIR can pinpoint functional networks and investigate how they are modulated by underlying structural connections, facilitating the examination of brain networks involved in diverse cognitive processes.³¹ With promising clinical applications, DIR holds the potential to unravel changes in structural and functional connectivity patterns associated with neurological and psychiatric disorders, showcasing its significance in advancing the understanding of brain disorders.³²

Complex data integration is a significant weakness when combining DIR data, requiring intricate processing techniques.³³ In addition, sensitivity to motion artifacts, common in DTI and DIR, necessitates meticulous correction strategies during data processing.³⁴ The interpretation of DIR data poses challenges, demanding expertise in both diffusion imaging and functional MRI, which can be a barrier for researchers and clinicians unfamiliar with both modalities.³⁵ Moreover, DIR faces limitations in spatial and temporal resolution inherited from diffusion-weighted imaging and resting-state fMRI, potentially hindering the resolution of fine anatomical details and rapid changes in brain connectivity.³⁶

DISCUSSION

The utilization of MRI sequences such as DIR and DTI is pivotal in diagnosing brain white matter diseases. DIR sequences excel in enhancing the visibility of cortical lesions in conditions such as MS, outperforming traditional sequences like FLAIR. DTI provides valuable insights into white matter integrity by measuring water molecule diffusion, aiding in the assessment of diseases such as MS. These techniques offer complementary benefits, with DIR pinpointing cortical lesions effectively while DTI unveils microstructural changes in white matter tracts. The importance of white matter integrity lies in its role as a network connecting various regions of the brain. Myelinated axons, oligodendrocytes, and glial cells compose white matter, which is crucial for efficient signal transmission. The myelin sheaths enveloping axons play a pivotal role in signal conduction and neural communication. Understanding these structural components and their interactions is vital for comprehending brain function and pathology. The review paper discusses the limitations of DTI in diagnosing conditions like cerebello-pontine angle (CPA) tumors.⁵⁴ It emphasizes the importance of preserving the facial nerve (FN) during CPA tumor surgery and highlights the suboptimal results of FN tracking with DTI. On the other hand, clinical decision support systems (CDSS) play a crucial role in impacting clinical decision-making.⁵⁵ They utilize computational models such as support vector machine (SVM) and Decision Tree to aid in diagnosing heart and stroke diseases. In addition, the review on multimorbidity emphasizes the need for more evidence on how different conditions interact to improve patient safety.⁵⁶ Integrating the strengths of DTI for precise imaging and CDSS for decision support can enhance patient care by providing accurate diagnoses and treatment planning in complex scenarios like multimorbidity or conditions requiring detailed neural imaging.

Methodologically, both DIR and DTI demonstrate strength in diagnosing white matter diseases. DIR has shown efficacy in identifying cortical lesions, especially in MS patients, while DTI provides detailed information on microstructural changes associated with various conditions, including aging, Alzheimer’s disease, and cerebrovascular disease. Integration of DTI and DIR methodologies can offer a comprehensive assessment of brain white matter diseases, leveraging their strengths for a more thorough evaluation. Recent advancements in neuroimaging, including machine learning techniques, have further enhanced the diagnostic capabilities of DIR and DTI. Artificial intelligence (AI)-generated images have proven reliable in lesion detection in MS patients, offering the potential for diagnostic and disease monitoring purposes. In addition, DTI studies have utilized machine learning algorithms to identify white matter deficits in psychiatric disorders and neurodegenerative diseases.

CONCLUSION

DIR and DTI are invaluable tools in the diagnosis and evaluation of brain white matter diseases. Their complementary benefits, when integrated with advanced imaging techniques and machine learning algorithms, offer a comprehensive approach to understanding and managing these conditions. The integration of DWI with DTI sequences in medical imaging holds significant importance in enhancing diagnostic capabilities. These sequences provide valuable insights into tissue microstructure and connectivity, aiding in the detection and characterization of various pathologies. By combining these imaging modalities with advanced techniques and machine learning algorithms, such as deep learning, the potential benefits are substantial. Advanced algorithms can leverage the rich information provided by DWI and DTI to improve accuracy in disease diagnosis, treatment planning, and monitoring. The synergy between these imaging modalities and machine learning approaches offers a promising avenue for more precise and personalized healthcare interventions, ultimately leading to enhanced patient care and outcomes. Future research directions in imaging for white matter disease detection involve the integration of advanced techniques like b-tensor encoding schemes in diffusion-weighted MRI with machine learning. In addition, the development of automated segmentation methods utilizing intensity standardization, texture feature extraction, and machine learning algorithms can enhance the detection of subtle white matter lesions. Exploring the role of white matter inflammation, microglial activation, and astrocytosis in neurodegenerative conditions such as Alzheimer’s disease can provide insights into early biomarkers for cognitive decline. Future studies could focus on refining imaging modalities to detect microstructural changes indicative of disease progression, leveraging machine learning for accurate segmentation, and investigating the relationship between white matter abnormalities and cognitive outcomes for improved diagnostic and therapeutic strategies.