Slice Thickness

Slice thickness determines whether a dataset is isotropic or anisotropic.

• Isotropic: Voxels are perfect cubes (e.g., 0.5mm x 0.5mm x 0.5mm). This allows for high-fidelity Multi-Planar Reconstruction (MPR) without stair-step artifacts.
• Anisotropic: Slice thickness is larger than the in-plane pixel dimensions, creating rectangular voxels. This is common in routine clinical scans to cover anatomy faster but degrades reformatted image quality.

Thicker slices increase the partial volume effect, a critical artifact where a single voxel contains a mixture of tissue types.

• The resulting signal intensity is a weighted average, causing boundaries to blur and small lesions to become invisible.
• For example, a 5mm slice may average bone and soft tissue, while a 0.625mm slice resolves them distinctly. Reducing slice thickness is the primary mitigation strategy.

Slice thickness has a linear relationship with SNR.

• Doubling the slice thickness doubles the number of protons or photons contributing to the signal, increasing SNR by a factor of 2.
• Conversely, halving the slice thickness for higher resolution reduces SNR by 50%. Deep Learning Reconstruction (DLR) is increasingly used to break this trade-off, allowing thin slices with clinically acceptable noise levels.

Thinner slices require more individual acquisitions to cover the same anatomical volume, directly increasing scan time.

• In CT, this demands a higher pitch or longer breath-hold.
• In MRI, it increases phase-encoding steps, prolonging the sequence. This creates a clinical workflow tension between the diagnostic need for high-resolution isotropic data and the practical constraints of patient motion and throughput.

Modern spiral CT and 3D MRI acquisitions allow for retrospective reconstruction of overlapping slices.

• Reconstructing slices with 50% overlap (e.g., 1mm slices every 0.5mm) improves the effective resolution in the z-axis without increasing patient dose.
• This technique reduces partial volume effects and creates smoother MPR and Volume Rendering outputs, effectively decoupling acquisition thickness from display thickness.

A volumetric pixel representing a value on a regular grid in three-dimensional space. It is the fundamental unit for CT and MRI image reconstruction. The dimensions of a voxel are defined by the pixel spacing in the x and y axes and the slice thickness in the z-axis. Anisotropic voxels, where slice thickness is significantly larger than in-plane resolution, are a direct consequence of thick slice acquisition and can degrade the accuracy of multi-planar reconstructions.

An imaging artifact where a single voxel contains a mixture of multiple tissue types, resulting in a blurred, averaged signal intensity. This effect is directly proportional to slice thickness; thicker slices encompass a larger physical volume, increasing the probability of tissue averaging. This degrades boundary definition and can obscure small lesions, making it a primary trade-off when increasing slice thickness to reduce scan time or noise.

A technique for generating coronal, sagittal, or oblique 2D image slices from a volumetric 3D dataset without rescanning the patient. The quality of MPR images is highly dependent on the acquisition slice thickness. Near-isotropic imaging, where slice thickness approximates in-plane pixel dimensions, produces MPRs with minimal stair-step artifacts. Thick, anisotropic slices result in degraded reformats with poor z-axis resolution.

The mathematical process of estimating unknown voxel intensity values at intermediate spatial positions during image resampling or registration. When slice thickness is large, interpolation algorithms must estimate missing data across greater distances, introducing uncertainty. Common methods include:

• Nearest neighbor: Simple but produces blocky artifacts
• Trilinear: Computes a weighted average of the 8 nearest voxels
• Cubic convolution: Uses a higher-order kernel for smoother results, critical for thick-slice datasets

A class of CT and MRI reconstruction algorithms that use deep neural networks to suppress noise and resolve fine structures. DLR fundamentally alters the traditional trade-off between slice thickness and image noise. By training on paired low-quality (thin, noisy) and high-quality (thin, clean) data, DLR models can reconstruct thin slices with significantly reduced noise, enabling high-resolution volumetric imaging without the dose or time penalties that historically necessitated thicker slices.

A sequence of DICOM images acquired in a single scan that share the same modality, orientation, and temporal relationship. The DICOM header tag (0018,0050) Slice Thickness explicitly records the nominal reconstructed slice depth. This value is critical for any downstream volumetric analysis, as it defines the z-axis sampling interval. Inconsistent or missing slice thickness metadata can cause misregistration and dimensional errors in surgical planning and 3D printing workflows.