Imaging Modalities

The most common imaging modalities for musculoskeletal imaging and modeling are X-rays imagery, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound (US).

We report in this Section sthe primary characteristics, as well as strengths and limitations of each modality. It is especially important to understand the capabilities and constraints of these modalities with respect to our goal of modeling the musculoskeletal structures.

3.3.1 X-rays, Fluoroscopy and Computed Tomography

X-rays imagery quantifies the absorption of X-rays from the various tissues under X-rays expo-sure. It is thus an invasive modality as ionizing radiation is observed and it is known to yield to cancers in case of over- or repetitive exposures. Still, it remains a widely utilized modality [BMVS02,DGZ07,ZSG10] for its ability to be used intra-operatively and its relative simplicity and low-cost. Fluoroscopy is a dynamic modality based on radiography which produces X-rays movies. Both X-X-rays and fluoroscopy images suffer from their inherent 2D projections which seriously limits the spatial interpretation of structures as well as the accurate tracking of motion in dynamic images. Furthermore, these modalities are not always the most appropriate when it comes to gauging small bone changes due to musculoskeletal disorders. In these cases, MRI (Sec.3.3.2) or ultrasound imagery (Sec. 3.3.3)[BKS⁺99] are better alternatives.

The 3D static counterpart of X-rays images is Computed Tomography (CT), which is often pre-ferred for the reconstruction of skeletal structures [KEK03,KK08], depending on the structures to be imaged (e.g., X-rays remains the common choice for trauma imaging of the hand and wrist [Wil05]). CT can deliver images with high resolution (below mm) and offers imaging in the transverse plane and multiplanar reconstructions. Moreover, in CT, cortical bone can be clearly differentiated from trabecular tissue which yields a better detection and characterization of complex fractures [Fel00]. CT intensity of different tissues can be predicted with a reasonable

accuracy by referring to their typical values in the Hounsfield scale. The common Hounsfield Units (HU) of different substances and tissues are reported in Table3.1.

Substance/Tissue HU

Air ≈1000

Fat ≈120

Water 0

Muscle 40

Bone >400

Table 3.1: Typical Hounsfield units of various substances and tissues (source:http://www.wikipedia.org)

Bone HU are typically very high compared to those of other structures. This characteristic eases the design of bone segmentation approaches based on techniques such as thresholding, region growing and deformable models (See Sec. 3.4.2). Figure3.4illustrates how a simple threshold-ing gives a good delineation of the bone contours. Finally, different research directions jointly combine some X-ray variants to derive new acquisition techniques, like the fusion of CT with fluoroscopy to assess in vivo motion [YWT⁺07].

(a) (b) (c) (d)

Figure 3.4: Computed Tomography (CT).(a)transversal CT slice of the hip articulations, which is easily thresholded as shown in(b).(c)-(d)same but in the coronal plane.

3.3.2 Magnetic Resonance Imaging

In MRI, hydrogen atoms, which are essentially found in water molecules within the tissues, are aligned under the influence of a powerful external magnetic field. Radio frequency (RF) fields are then used to perturb the alignment of the atoms which emit a resonance signal detected by the scanner. The collection and processing of these signals yield the creation of volumetric images.

Such magnetic-based imaging does not create any harmful ionizing radiation [SSG⁺03,SC04], which makes MRI adapted to conduct studies on healthy volunteers.

The diversity of factors accounted by the MRI signal (e.g., proton density, T1 and T2 relaxation times, fluid flow, etc.) grants MRI with unique versatility. For instance, a fluid flow/ tissue relationship is exploited in diffusion tensor MRI (dtMRI) to extract muscle fiber direction, pro-viding an essential biomechanical parameter [HSV⁺05]. Similarly, dtMRI can be used to image

bone marrow disorders [Stä05]. Unlike CT and X-rays imagery, MRI is furthermore able to si-multaneously capture and depict bony and soft structures (e.g., cartilages and ligaments). This characteristic of MRI has played an important role in the study of numerous human joints, such as the hip [KEK03,ZSN⁺04,PKB06,GMT10], knee [KBG⁺98,KHC05,LFG⁺98,KEK03], shoulder [BR05,NLBA07], elbow [CS05] and wrist [SVG02,STCK03]. Figure3.5illustrates how different high resolution images of the articulations can be obtained by varying the magnetic field strength (e.g., 1.5 T or 3 T) or the MRI acquisition sequences.

An increase of the magnetic field strength significantly improves the signal-to-noise ratio and hence the image quality. In fact, the increase in the signal-to-noise ratio allows to image smaller field of views with equivalent quality in the same acquisition time [Sch05]. As a consequence, MRI musculoskeletal imaging of joints clearly benefits from stronger magnetic fields, as depicted in Figs. 3.5(b) and3.5(c). These figures also show that by varying the acquisition protocol, the imaged intensities can drastically change between acquisitions as e.g. the bone tissue appears darker than the muscle tissue with the MERGE protocol 3.5(b) whereas the opposite happens with the CUBE sequence 3.5(b). This great flexibility of MRI compared to CT is very useful for clinical diagnosis but seriously complicates the task of image segmentation for structure delineation. Indeed, some kind of prior knowledge on the appearance is necessary to be able to delineate structures with such inhomogeneous intensities (See Fig. 1.1in Chap. 1). Further, the presence of ubiquitous imagery artifacts (e.g. those resulting from the chemical shift effect as shown in Fig.3.6(a)) demand fine tuning of acquisition sequences and the design of more robust modeling approaches.

(a) (b) (c)

Figure 3.5: MRI acquisitions of human articulation. (a) slice of an MRI volume (slice resolution: 0.39× 0.39mm) of the ankle acquired with a 1.5T High resolution FLAIR sagittal sequence. Knee acquired at high resolution on a 3T scanner with(b)MERGE (multiple echo recombined gradient echo) and(c)FSE-Cube sequences. These different sequences acquired with a more powerful magnet allow better imaging of the thin and soft structures, such as knee fibrocartilaginous menisci and ligaments. Images courtesy of Bailiang Chen and Prof. Andrew Todd-Pokropek from UCL, and Prof. Wady Gedroyc, Mr. Warren Casperz and Ms.

Lauren Sundblom from St. Mary’s Hospital.

(a) (b)

Figure 3.6: Example of MRI acquisition artifacts. In(a)artifacts due to the chemical shift effect are visible:

bone (white arrow) does not present uniform cortical and trabecular bone layers, some kind of “directional”

artifacts are indeed produced. As depicted in (b), this problem is clearly resolved with a more adapted acquisition protocols. Images courtesy of Bailiang Chen and Prof. Andrew Todd-Pokropek from UCL, and Prof. Wady Gedroyc, Mr. Warren Casperz and Ms. Lauren Sundblom from St. Mary’s Hospital.

3.3.3 Ultrasound

Ultrasound (US) emits sound waves into the medium and computes the amplitude and the time required for the waves to bounce back. Based on an estimation of the sound speed into the corresponding medium, distance from the emitter can be estimated. By casting multiple beams, a 2D image of the internal organs can be reconstructed. The acquisition process is extremely fast, thus real-time and dynamic images of the human body can be produced. Similarly, by rotating the emitter in 3D space or by using arrays of transducers, (dynamic) 3D US is nowadays possible and commonly used in fetal echography. US can also profit from a Doppler mode to track flow of fluids in time by using a color scale which indicates the fluid direction and speed.

US is low-cost, non-invasive and portable which makes it very attractive as an imaging device.

Unfortunately, US suffers from low image quality due to speckle noise (scattering elements with dimension below the wave length), geometric distortion due to incorrect assumptions on wave velocity and most importantly shadowing effects due to the incapacity of sound waves to traverse some media. This is particularly the case of bone tissue whose interior cannot be imaged with US and which occludes surrounding structures. US is also handicapped by the quick decay of the emitted waves which prevents the clear imaging of deep structures and results in small image field of view. Still, US is more and more used in diagnostic of musculoskeletal soft tissue to detect e.g. muscle, tendon and nerves disorders [BM05]. Figure 3.7shows an example of a 3D US of the leg. Image quality is undoubtedly poor compared to MRI or CT, and only a trained eye can detect the interfaces of some structures such as the tibia or the calf muscle.

3.3.4 Image resolution and field of view

In the clinical environment, acquisition time is often a critical factor and many acquisition pro-tocols strive to reduce it, often at the cost of (i) reduced field of view (FOV) [MI05] or (ii) low image resolution. The FOV is typically kept small in arthrography [EW04] in order to obtain the best resolution and signal-to-noise ratio around the joint (e.g., Fig 3.5). A low image resolution is often necessary to cover large FOV in order to fully image the bones of the joints, as reported

Figure 3.7: 3D ultrasound imaging of the leg. This picture shows different planes of the 3D US volume.

The tibia and calf muscle (composed of the gastrocnemius and soleus muscles) can only be distinguished by a trained operator due to the poor image quality and limited structures visibility. Image courtesy of Dr.

Jing Deng from UCL.

in some biomechanical studies [MKP⁺05]. As a consequence, a large slice thickness is produced, which yields ubiquitous partial volume effect (PVE).

A solution to simultaneously acquire a large FOV, while having fine details for small structures such as the cartilages is to combine together different acquisitions with different FOVs and image resolutions. This allows an overall reduction of the acquisition time but also introduces other issues related to the registration of the different acquisitions. Modern scanners can record the table localization information which can be used for the registration. However, other problems remain such as the movement of the patient between acquisitions and the many imagery artifacts which hinder the registration.

While most acquisition protocols are designed for best clinical practice, they are not necessarily well suited for purposes of segmentation. Even with the ability to register multiple images, segmentation is a complex task. In fact, we will later on demonstrate that image resolution and FOV have effects in all the stages of knowledge-based deformable models: creation of priors, initialization and evolution of deformable models. This will demand the design of new solutions to address these difficulties.